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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-08 12:29:11
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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dwililiya/emotion_recognition
|
dwililiya
| 2024-09-05T09:16:53Z | 244 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-09-05T08:58:50Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: emotion_recognition
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_recognition
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5235
- Accuracy: 0.4562
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3549 | 2.5 | 50 | 1.5704 | 0.4437 |
| 0.9647 | 5.0 | 100 | 1.5235 | 0.4562 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
pyh95510/Llama3_ft
|
pyh95510
| 2024-09-05T09:14:48Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-09-05T07:40:10Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
chaleris/biostd_hub
|
chaleris
| 2024-09-05T09:08:05Z | 0 | 0 | null |
[
"zh",
"region:us"
] | null | 2022-11-03T06:16:55Z |
---
language: zh
---
## BioStd Model Hubs
### [模型仓库](https://huggingface.co/chaleris/biostd_hub/tree/main/models)
| name | s_dim | c_dim | p_dim | e_dim | total_dim | 兼容版本 | 备注 |
| ----------------------------------- | ----- | ----- | ----- | ----- | --------- | -------- | -------------------------- |
| `v1.3.0/v1.3.0-sc` | 312 | 100 | NA | NA | 412 | v1.3.2 | |
| `v1.3.0/v1.3.0-scp` | 312 | 128 | 32 | NA | 472 | v1.3.2 | |
| `v1.3.0/v1.3.0-scpe` | 312 | 128 | 8 | 4 | 452 | v1.3.2 | |
| `v1.3.0/v1.3.0-sc-data-v3` | 312 | 100 | NA | NA | 412 | v1.3.2 | |
| `v1.3.0/v1.3.0-scp-data-v3` | 312 | 128 | 64 | NA | 504 | v1.3.2 | |
| `v1.3.0/v1.3.0-scpe-data-v3` | 312 | 128 | 8 | 8 | 448 | v1.3.2 | |
| `v1.3.0/v1.3.0-scp-data-v1-local` | 312 | 100 | 100 | NA | 512 | v1.3.2 | |
| `v1.3.3/v1.3.3-scp-data-v3-lc-1031` | 312 | 100 | 50 | NA | 462 | v1.3.3 | |
| `v1.5.0/v1_5_0_scp_data_v5` | 312 | 100 | 50 | NA | 462 | v1.3.8 | 同v1.3.3，只是训练数据不同 |
>s_dim：语义特征维度；c_dim：字符特征维度；p_dim：拼音特征维度；e_dim：主成分特征维度
>
>data-v3：使用v3版采样数据进行训练
>
>Data-v1：使用v1版采样数据进行训练（最开始版本）
>
>local：指的是用0523版临床版标准表训练
>
>lc-1031：指的是用1031版临床版标准表训练
>
>兼容版本：jarvis-standard-ranker可以正常运行的版本
|
dvyio/flux-lora-thermal-image
|
dvyio
| 2024-09-05T09:03:51Z | 211 | 6 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-09-05T08:54:30Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: close-up of a man's face, thermal image in the style of THRML
output:
url: images/WROSaNNU4-Gw0r5QoBRjf_f164ffa4f0804e68bad1d06d30deecfa.jpg
- text: a horse, thermal image in the style of THRML
output:
url: images/-brvSCXYZuNEHZ4df4YbL_3af6c1ef67044fe28ab97aa672412cca.jpg
- text: a woman using a VR headset, thermal image in the style of THRML
output:
url: images/2-qtpIYVaN9B8yVbaQJI4_a0394de5dda54e4b907f6a3bd085320d.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: thermal image in the style of THRML
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE
---
# Thermal Image
<Gallery />
## Trigger words
You should use `thermal image in the style of THRML` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/dvyio/flux-lora-thermal-image/tree/main) them in the Files & versions tab.
|
neopolita/yi-coder-1.5b-gguf
|
neopolita
| 2024-09-05T08:57:21Z | 30 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-09-05T08:48:17Z |
---
{}
---
# GGUF quants for [**01-ai/Yi-Coder-1.5B**](https://huggingface.co/01-ai/Yi-Coder-1.5B) using [llama.cpp](https://github.com/ggerganov/llama.cpp)
**Terms of Use**: Please check the [**original model**](https://huggingface.co/01-ai/Yi-Coder-1.5B)
<picture>
<img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png">
</picture>
## Quants
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_s`: Uses Q3_K for all tensors
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_s`: Uses Q4_K for all tensors
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_s`: Uses Q5_K for all tensors
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q6_k`: Uses Q8_K for all tensors
* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
|
alperenkoluk/gemma-Code-Instruct-Finetune-test
|
alperenkoluk
| 2024-09-05T08:49:21Z | 114 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-05T08:43:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
alegchenko/command-r-08-2024-awq-ru-calib
|
alegchenko
| 2024-09-05T08:48:49Z | 5 | 1 | null |
[
"safetensors",
"cohere",
"text-generation",
"conversational",
"ru",
"en",
"dataset:IlyaGusev/saiga_scored",
"dataset:Open-Orca/OpenOrca",
"base_model:CohereForAI/c4ai-command-r-08-2024",
"base_model:quantized:CohereForAI/c4ai-command-r-08-2024",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2024-09-05T08:11:44Z |
---
datasets:
- IlyaGusev/saiga_scored
- Open-Orca/OpenOrca
language:
- ru
- en
base_model: CohereForAI/c4ai-command-r-08-2024
pipeline_tag: text-generation
---
AWQ квантизация модели https://huggingface.co/CohereForAI/c4ai-command-r-08-2024
полученная с помощью https://github.com/casper-hansen/AutoAWQ
Для калибровки использовались ограничения на 256 пакетов длиной до 256 токенов,
собранные из решений различных задач на русском и английском языке с помощью GPT4 / GPT4o из датасетов:
https://huggingface.co/datasets/IlyaGusev/saiga_scored
https://huggingface.co/datasets/Open-Orca/OpenOrca
Валидация модели производилась на обучающей части бенчмарка MERA https://mera.a-ai.ru/ru/leaderboard,
так для задачи PARus модель набирает 0.92 что эквивалетно например 4bit квантизациям Qwen2-72B и Llama3-70B
|
neopolita/yi-coder-1.5b-chat-gguf
|
neopolita
| 2024-09-05T08:46:33Z | 46 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-05T08:37:28Z |
---
{}
---
# GGUF quants for [**01-ai/Yi-Coder-1.5B-Chat**](https://huggingface.co/01-ai/Yi-Coder-1.5B-Chat) using [llama.cpp](https://github.com/ggerganov/llama.cpp)
**Terms of Use**: Please check the [**original model**](https://huggingface.co/01-ai/Yi-Coder-1.5B-Chat)
<picture>
<img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png">
</picture>
## Quants
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_s`: Uses Q3_K for all tensors
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_s`: Uses Q4_K for all tensors
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_s`: Uses Q5_K for all tensors
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q6_k`: Uses Q8_K for all tensors
* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
|
dvyio/flux-lora-victorian-photograph
|
dvyio
| 2024-09-05T08:40:22Z | 46 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-09-05T08:40:11Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
a woman wearing a virtual reality headset, photograph in the style of VCTRN,
vintage Victorian era photograph
output:
url: images/vgGJTjf9QQsRi-E733SIj_2ca97f03c2e742fdaaf9f9125209a99a.jpg
- text: >-
a rocket launch, photograph in the style of VCTRN, vintage Victorian era
photograph
output:
url: images/KZ57SH3dByTPjg_exa6GA_06fffe0613704ecaabe811dab033d478.jpg
- text: >-
a man using a mobile telephone, photograph in the style of VCTRN, vintage
Victorian era photograph
output:
url: images/Be9cyaDRmgWJUFzoebLY5_88c1fbf39b2d431995d782265470e87e.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: photograph in the style of VCTRN
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE
---
# Victorian Photograph
<Gallery />
## Trigger words
You should use `photograph in the style of VCTRN` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/dvyio/flux-lora-victorian-photograph/tree/main) them in the Files & versions tab.
|
Autsadin/gemma_2_test1
|
Autsadin
| 2024-09-05T08:39:25Z | 88 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gemma2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-05T08:27:42Z |
---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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|
dvyio/flux-lora-simple-illustration
|
dvyio
| 2024-09-05T08:34:28Z | 1,189 | 30 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-09-05T08:34:17Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
a woman, illustration in the style of SMPL, thick black lines on a white
background
output:
url: images/HXUchW9Fp2hp6jDnDIeIT_1f56ae684f7445c8bc75910ec48bfdab.jpg
- text: >-
a bicycle, illustration in the style of SMPL, thick black lines on a white
background
output:
url: images/YJTMyMgUmuC2TWshUDl9K_58376274e6f04bac8743bca05297b708.jpg
- text: >-
the London skyline, illustration in the style of SMPL, thick black lines on
a white background
output:
url: images/aPFE0cefB8S0Ylhf_XV5a_3fcaf8c6c44147949c3e56ce49887be7.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: illustration in the style of SMPL
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE
---
# Simple Illustration
<Gallery />
## Trigger words
You should use `illustration in the style of SMPL` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/dvyio/flux-lora-simple-illustration/tree/main) them in the Files & versions tab.
|
edwsiew/phantom-dispatch-01
|
edwsiew
| 2024-09-05T08:32:32Z | 5 | 0 |
setfit
|
[
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2",
"model-index",
"region:us"
] |
text-classification
| 2024-09-05T08:29:55Z |
---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: troubleshooting n a test results n a trouble description generator failed
to start during blackout test transfer switch died before generator could start
transfer switch need repair asap back power need to be wired to transfer switch
history of trouble n a vendor acas problem description generator failed to start
during blackout test transfer switch died before generator could start transfer
switch need repair asap back power need to be wired to transfer switch special
access n a
- text: 1 gen with oil pressure shutdown alarm 2 genfail alarm is not showing up in
site boss requestor banaag rommel requestor email rommel banaag verizonwireless
com requestor phone 951 8342458
- text: troubleshooting triage category gen fail site id cvl02692 alarms cvl02692
rbs generator fail fieldreplaceableunit=sau 1 alarmport=12 2024 08 06 06 23 36
med generator verification yes history n a knowledge judgement sending to vendor
to check generator dispatch strategy vendor test results triage category gen fail
site id cvl02692 alarms cvl02692 rbs generator fail fieldreplaceableunit=sau 1
alarmport=12 2024 08 06 06 23 36 med generator verification yes history n a knowledge
judgement sending to vendor to check generator dispatch strategy vendor trouble
description rbs generator fail history of trouble triage category gen fail site
id cvl02692 alarms cvl02692 rbs generator fail fieldreplaceableunit=sau 1 alarmport=12
2024 08 06 06 23 36 med generator verification yes history n a knowledge judgement
sending to vendor to check generator dispatch strategy vendor vendor acas problem
description rbs generator fail special access n a
- text: troubleshooting triage category rbs generator fuel leak alarm cvl08526 cvl08526
rbs generator fuel leak fieldreplaceableunit=sau 1 alarmport=23 2024 07 10 13
07 38 cvl08526 cvl08526 rbs rbs generator fuel leak fieldreplaceableunit=sau 1
alarmport=20 2024 07 10 13 05 04 mdat oremis verification generator generac baldor
magnum sd30 manufacturer generac baldor magnum model sd30 status in use serial
3008406953 kw 30 prime power source no still on site yes engine perkins engine
co ltd 404d 22ta manufacturer perkins engine co ltd model 404d 22ta serial gr84695u9967000g
max engine kw 36 manufacturered date 2021 02 01 engine type diesel max brake hp
49 in service date 2022 07 13 fuel type ultra low sulfur diesel ulsd owner cell
no repeats open related tckt active eim intrusion knowledge judgement sending
to vendor to investigate and resolve gen rbs generator fuel leak condition dispatch
strategy vendor test results triage category generator rbs generator fuel leak
alarm cvl08526 cvl08526 rbs generator fuel leak fieldreplaceableunit=sau 1 alarmport=23
2024 07 10 13 07 38 cvl08526 cvl08526 rbs generator rbs generator fuel leak fieldreplaceableunit=sau
1 alarmport=20 2024 07 10 13 05 04 mdat oremis verification generator generac
baldor magnum sd30 manufacturer generac baldor magnum model sd30 status in use
serial 3008406953 kw 30 prime power source no still on site yes engine perkins
engine co ltd 404d 22ta manufacturer perkins engine co ltd model 404d 22ta serial
gr84695u9967000g max engine kw 36 manufacturered date 2021 02 01 engine type diesel
max brake hp 49 in service date 2022 07 13 fuel type ultra low sulfur diesel ulsd
owner cell no repeats open related tckt active eim intrusion knowledge judgement
sending to vendor to investigate and resolve gen rbs generator fuel leak condition
dispatch strategy vendor trouble description smart rbs generator fuel leak history
of trouble na vendor acas problem description smart rbs generator fuel leak special
access na
- text: troubleshooting triage category gen fail oss netcool alarms ccl05638 rbs generator
fail fieldreplaceableunit=sau 1 alarmport=10 rbs generator fail ca daly city cell
site guadalupe canyon parkway 2024 07 29 23 37 56 smart alarm y mdat verification
active generac sd030 2022 d 3012298793 fixed in compound history no repeats tab
no open related tickets in aots knowledge judgement sending to vendor to check
gen fail dispatch strategy vendor test results triage category gen fail oss netcool
alarms ccl05638 rbs generator fail fieldreplaceableunit=sau 1 alarmport=10 rbs
generator fail ca daly city cell site guadalupe canyon parkway 2024 07 29 23 37
56 smart alarm y mdat verification active generac sd030 2022 d 3012298793 fixed
in compound history no repeats tab no open related tickets in aots knowledge judgement
sending to vendor to check gen fail dispatch strategy vendor trouble description
triage category gen fail oss netcool alarms ccl05638 rbs generator fail fieldreplaceableunit=sau
1 alarmport=10 rbs generator fail ca daly city cell site guadalupe canyon parkway
2024 07 29 23 37 56 smart alarm y mdat verification active generac sd030 2022
d 3012298793 fixed in compound history no repeats tab no open related tickets
in aots knowledge judgement sending to vendor to check gen fail dispatch strategy
vendor history of trouble n a vendor acas problem description triage category
gen fail oss netcool alarms ccl05638 rbs generator fail fieldreplaceableunit=sau
1 alarmport=10 rbs generator fail ca daly city cell site guadalupe canyon parkway
2024 07 29 23 37 56 smart alarm y mdat verification active generac sd030 2022
d 3012298793 fixed in compound history no repeats tab no open related tickets
in aots knowledge judgement sending to vendor to check gen fail dispatch strategy
vendor special access n a
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.6666666666666666
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | <ul><li>'please dispatch to troubleshoot generator failure the generator failed during exercise requestor cadenhead jason requestor email jason cadenhead verizonwireless com requestor phone 903 399 6027'</li><li>'troubleshooting triage category generator shut down oss netcool alarms ccl04246 rbs generator shut down fieldreplaceableunit=sau alarmport=5 rbs generator shut down 2024 07 04 16 31 52 smart alarm ccl04246 rbs generator shut down fieldreplaceableunit=sau alarmport=5 2024 07 04 16 31 36 mdat oremis verification generac baldor magnum repeats open related tckt active eim intrusionknowledge judgement sending to vendor to investigate and resolve gen shut down condition dispatch strategy vendor test results triage category generator shut down oss netcool alarms ccl04246 rbs generator shut down fieldreplaceableunit=sau alarmport=5 rbs generator shut down 2024 07 04 16 31 52 smart alarm ccl04246 rbs generator shut down fieldreplaceableunit=sau alarmport=5 2024 07 04 16 31 36 mdat oremis verification generac baldor magnum repeats open related tckt active eim intrusionknowledge judgement sending to vendor to investigate and resolve gen shut down condition dispatch strategy vendor trouble description triage category generator shut down oss netcool alarms ccl04246 rbs generator shut down fieldreplaceableunit=sau alarmport=5 rbs generator shut down 2024 07 04 16 31 52 smart alarm ccl04246 rbs generator shut down fieldreplaceableunit=sau alarmport=5 2024 07 04 16 31 36 mdat oremis verification generac baldor magnum repeats open related tckt active eim intrusionknowledge judgement sending to vendor to investigate and resolve gen shut down condition dispatch strategy vendor history of trouble na vendor acas problem description triage category generator shut down oss netcool alarms ccl04246 rbs generator shut down fieldreplaceableunit=sau alarmport=5 rbs generator shut down 2024 07 04 16 31 52 smart alarm ccl04246 rbs generator shut down fieldreplaceableunit=sau alarmport=5 2024 07 04 16 31 36 mdat oremis verification generac baldor magnum repeats open related tckt active eim intrusionknowledge judgement sending to vendor to investigate and resolve gen shut down condition dispatch strategy vendor special access na'</li><li>'troubleshooting triage category gen fail oss netcool alarms dxl02173 rbs generator fail fieldreplaceableunit=sau alarmport=13 rbs generator fail tx buffalo cell site dt buffalo 2024 08 11 04 59 30 2024 08 11 04 59 30 07 20 36 7200 56110 1091 1 30508 dxl02173 southwest central texas 0 external alarm ericsson_oss_rc dxl02173 ericsson_oss external alarm rbs generator fail d_357_prx1 mobility pl1_259ag 61089964 0 0 2024 08 11 2 3 critical 5 dxl02173 rbs generator fail fieldreplaceableunit=sau alarmport=13 rbs generator fail tx buffalo cell site dt buffalo 2024 08 11 05 01 11 2024 08 11 05 01 11 07 18 55 7200 tt000080568668 56110 1091 1 30508 dxl02173 southwest central texas 1723385658 correlated external alarm correlated ericsson_oss_rc dxl02173 ericsson_oss external alarm rbs generator fail smart alarm dxl02173 dxl02173 rbs generator fail fieldreplaceableunit=sau alarmport=13 2024 08 11 06 59 23 mdat verification generac baldor magnum history no repeats tab no open related tickets in aots knowledge judgement sending to vendor to check gen fail weekly scheduled exercise tu 1pm for 30 mins active rbs generator fail alarm pls send vendor to check the generator dispatch strategy vendor test results triage category gen fail oss netcool alarms dxl02173 rbs generator fail fieldreplaceableunit=sau alarmport=13 rbs generator fail tx buffalo cell site dt buffalo 2024 08 11 04 59 30 2024 08 11 04 59 30 07 20 36 7200 56110 1091 1 30508 dxl02173 southwest central texas 0 external alarm ericsson_oss_rc dxl02173 ericsson_oss external alarm rbs generator fail d_357_prx1 mobility pl1_259ag 61089964 0 0 2024 08 11 2 3 critical 5 dxl02173 rbs generator fail fieldreplaceableunit=sau alarmport=13 rbs generator fail tx buffalo cell site dt buffalo 2024 08 11 05 01 11 2024 08 11 05 01 11 07 18 55 7200 tt000080568668 56110 1091 1 30508 dxl02173 southwest central texas 1723385658 correlated external alarm correlated ericsson_oss_rc dxl02173 ericsson_oss external alarm rbs generator fail smart alarm dxl02173 dxl02173 rbs generator fail fieldreplaceableunit=sau alarmport=13 2024 08 11 06 59 23 mdat verification generac baldor magnum history no repeats tab no open related tickets in aots knowledge judgement sending to vendor to check gen fail weekly scheduled exercise tu 1pm for 30 mins active rbs generator fail alarm pls send vendor to check the generator dispatch strategy vendor trouble description triage category gen fail oss netcool alarms dxl02173 rbs generator fail fieldreplaceableunit=sau alarmport=13 rbs generator fail tx buffalo cell site dt buffalo 2024 08 11 04 59 30 2024 08 11 04 59 30 07 20 36 7200 56110 1091 1 30508 dxl02173 southwest central texas 0 external alarm ericsson_oss_rc dxl02173 ericsson_oss external alarm rbs generator fail d_357_prx1 mobility pl1_259ag 61089964 0 0 2024 08 11 2 3 critical 5 dxl02173 rbs generator fail fieldreplaceableunit=sau alarmport=13 rbs generator fail tx buffalo cell site dt buffalo 2024 08 11 05 01 11 2024 08 11 05 01 11 07 18 55 7200 tt000080568668 56110 1091 1 30508 dxl02173 southwest central texas 1723385658 correlated external alarm correlated ericsson_oss_rc dxl02173 ericsson_oss external alarm rbs generator fail smart alarm dxl02173 dxl02173 rbs generator fail fieldreplaceableunit=sau alarmport=13 2024 08 11 06 59 23 mdat verification generac baldor magnum history no repeats tab no open related tickets in aots knowledge judgement sending to vendor to check gen fail weekly scheduled exercise tu 1pm for 30 mins active rbs generator fail alarm pls send vendor to check the generator dispatch strategy vendor history of trouble na vendor acas problem description triage category gen fail oss netcool alarms dxl02173 rbs generator fail fieldreplaceableunit=sau alarmport=13 rbs generator fail tx buffalo cell site dt buffalo 2024 08 11 04 59 30 2024 08 11 04 59 30 07 20 36 7200 56110 1091 1 30508 dxl02173 southwest central texas 0 external alarm ericsson_oss_rc dxl02173 ericsson_oss external alarm rbs generator fail d_357_prx1 mobility pl1_259ag 61089964 0 0 2024 08 11 2 3 critical 5 dxl02173 rbs generator fail fieldreplaceableunit=sau alarmport special access 24x7 access indoor site fixed generator on site portable generator can be deployed to site single phase cam loc on site will need standard length power cords crown castle 800 788 7011 txu oncor 888 313 4747 meter# 113 091 407 acct# 114949 compound combo 7011 locks 3588 lockbox 0696 lockbox is inside generator left hand side'</li></ul> |
| 0 | <ul><li>'troubleshooting triage category gen fail cli alarms rbs generator fail fieldreplaceableunit=sau alarmport=24 2024 08 06 06 34 13 mdat verification y generator generac baldor magnum sg35 3002404264 fixed history knowledge judgement sending to vendor to check generator dispatch strategy vendor test results triage category gen fail cli alarms rbs generator fail fieldreplaceableunit=sau alarmport=24 2024 08 06 06 34 13 mdat verification y generator generac baldor magnum sg35 3002404264 fixed history knowledge judgement sending to vendor to check generator dispatch strategy vendor trouble description triage category gen fail cli alarms rbs generator fail fieldreplaceableunit=sau alarmport=24 2024 08 06 06 34 13 mdat verification y generator generac baldor magnum sg35 3002404264 fixed history knowledge judgement sending to vendor to check generator dispatch strategy vendor history of trouble n a vendor acas problem description triage category gen fail cli alarms rbs generator fail fieldreplaceableunit=sau alarmport=24 2024 08 06 06 34 13 mdat verification y generator generac baldor magnum sg35 3002404264 fixed history knowledge judgement sending to vendor to check generator dispatch strategy vendor special access n a'</li><li>'troubleshooting triage category gen mj oss netcool alarms cvl06141 rbs generator mj fieldreplaceableunit=sau 1 alarmport=11 rbs generator mj ca kingsburg cell site south traver rev 2024 08 19 11 39 15 2024 08 19 11 39 15 01 39 50 3600 tt000080604725 56110 1091 1 9585 cvl06141 west sacramento 0 external alarm ericsson_oss_rc cvl06141 ericsson_oss external alarm rbs generator mj smart alarm cvl06141 cvl06141 rbs generator mj fieldreplaceableunit=sau 1 alarmport=11 2024 08 19 11 39 07 mdat verification generac baldor magnum history no repeats tab no open related tickets in aots knowledge judgement sending to vendor to check gen mj weekly scheduled exercise tu 06 30 active rbs generator mj alarm pls send vendor to check the generator dispatch strategy vendor test results triage category gen mj oss netcool alarms cvl06141 rbs generator mj fieldreplaceableunit=sau 1 alarmport=11 rbs generator mj ca kingsburg cell site south traver rev 2024 08 19 11 39 15 2024 08 19 11 39 15 01 39 50 3600 tt000080604725 56110 1091 1 9585 cvl06141 west sacramento 0 external alarm ericsson_oss_rc cvl06141 ericsson_oss external alarm rbs generator mj smart alarm cvl06141 cvl06141 rbs generator mj fieldreplaceableunit=sau 1 alarmport=11 2024 08 19 11 39 07 mdat verification generac baldor magnum history no repeats tab no open related tickets in aots knowledge judgement sending to vendor to check gen mj weekly scheduled exercise tu 06 30 active rbs generator mj alarm pls send vendor to check the generator dispatch strategy vendor trouble description triage category gen mj oss netcool alarms cvl06141 rbs generator mj fieldreplaceableunit=sau 1 alarmport=11 rbs generator mj ca kingsburg cell site south traver rev 2024 08 19 11 39 15 2024 08 19 11 39 15 01 39 50 3600 tt000080604725 56110 1091 1 9585 cvl06141 west sacramento 0 external alarm ericsson_oss_rc cvl06141 ericsson_oss external alarm rbs generator mj smart alarm cvl06141 cvl06141 rbs generator mj fieldreplaceableunit=sau 1 alarmport=11 2024 08 19 11 39 07 mdat verification generac baldor magnum history no repeats tab no open related tickets in aots knowledge judgement sending to vendor to check gen mj weekly scheduled exercise tu 06 30 active rbs generator mj alarm pls send vendor to check the generator dispatch strategy vendor history of trouble na vendor acas problem description triage category gen mj oss netcool alarms cvl06141 rbs generator mj fieldreplaceableunit=sau 1 alarmport=11 rbs generator mj ca kingsburg cell site south traver rev 2024 08 19 11 39 15 2024 08 19 11 39 15 01 39 50 3600 tt000080604725 56110 1091 1 9585 cvl06141 west sacramento 0 external alarm ericsson_oss_rc cvl06141 ericsson_oss external alarm rbs generator mj smart alarm cvl06141 cvl06141 rbs generator mj fieldreplaceableunit=sau 1 alarmport=11 2024 08 19 11 39 07 mdat verificatio special access fixed gen on site portable can be deployed 24 7 access no notice required crown managed bu 815954 gate combo 7011 door arrow key generator 7011'</li><li>'troubleshooting while on site for generator controller survey we found 2 active alarms alarmport=28 rbs generator mj and alarmport=26 rbs generator fuel leak test results active alarms will be active until vendor changes the 2 alarms from open to close trouble description while on site for generator controller survey we found 2 active alarms alarmport=28 rbs generator mj and alarmport=26 rbs generator fuel leak we need vendor to redefine the alarms on the generator from normally open to normally closed history of trouble n a vendor acas problem description while on site for generator controller survey we found 2 active alarms alarmport=28 rbs generator mj and alarmport=26 rbs generator fuel leak special access n a'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.6667 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("edwsiew/phantom-dispatch-01")
# Run inference
preds = model("1 gen with oil pressure shutdown alarm 2 genfail alarm is not showing up in site boss requestor banaag rommel requestor email rommel banaag verizonwireless com requestor phone 951 8342458")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:----|
| Word count | 3 | 182.3273 | 915 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 14 |
| 1 | 41 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 3
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0238 | 1 | 0.2379 | - |
### Framework Versions
- Python: 3.12.0
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
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-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
daviddrzik/SK_Morph_BLM-sentiment-multidomain
|
daviddrzik
| 2024-09-05T08:15:45Z | 186 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"sentiment",
"sk",
"base_model:daviddrzik/SK_Morph_BLM",
"base_model:finetune:daviddrzik/SK_Morph_BLM",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-09-04T08:42:07Z |
---
license: mit
language:
- sk
pipeline_tag: text-classification
library_name: transformers
metrics:
- f1
base_model: daviddrzik/SK_Morph_BLM
tags:
- sentiment
---
# Fine-Tuned Sentiment Classification Model - SK_Morph_BLM (Universal multi-domain sentiment classification)
## Model Overview
This model is a fine-tuned version of the [SK_Morph_BLM model](https://huggingface.co/daviddrzik/SK_Morph_BLM) for the task of sentiment classification. It has been trained on datasets from multiple domains, including banking, social media, movie reviews, politics, and product reviews. Some of these datasets were originally in Czech and were machine-translated into Slovak using Google Cloud Translation.
## Sentiment Labels
Each row in the dataset is labeled with one of the following sentiments:
- **Negative (0)**
- **Neutral (1)**
- **Positive (2)**
## Dataset Details
The dataset used for fine-tuning comprises text records from various domains. Below are the details for each domain:
### Banking Domain
- **Source**: [Banking Dataset](https://doi.org/10.1016/j.procs.2023.10.346)
- **Description**: Sentences from the annual reports of a commercial bank in Slovakia.
- **Records per Class**: 923
- **Unique Words**: 11,469
- **Average Words per Record**: 20.93
- **Average Characters per Word**: 142.41
### Social Media Domain
- **Source**: [Social Media Dataset](http://hdl.handle.net/11858/00-097C-0000-0022-FE82-7)
- **Description**: Data from posts on the Facebook social network.
- **Records per Class**: 1,991
- **Unique Words**: 114,549
- **Average Words per Record**: 9.24
- **Average Characters per Word**: 57.11
### Movies Domain
- **Source**: [Movies Dataset](https://doi.org/10.1016/j.ipm.2014.05.001)
- **Description**: Short movie reviews from ČSFD.
- **Records per Class**: 3,000
- **Unique Words**: 72,166
- **Average Words per Record**: 52.12
- **Average Characters per Word**: 330.92
### Politics Domain
- **Source**: [Politics Dataset](https://doi.org/10.48550/arXiv.2309.09783)
- **Description**: Sentences from Slovak parliamentary proceedings.
- **Records per Class**: 452
- **Unique Words**: 6,697
- **Average Words per Record**: 12.31
- **Average Characters per Word**: 85.22
### Reviews Domain
- **Source**: [Reviews Dataset](https://aclanthology.org/W13-1609)
- **Description**: Product reviews from Mall.cz.
- **Records per Class**: 3,000
- **Unique Words**: 35,941
- **Average Words per Record**: 21.05
- **Average Characters per Word**: 137.33
## Fine-Tuning Hyperparameters
The following hyperparameters were used during the fine-tuning process:
- **Learning Rate:** 1e-05
- **Training Batch Size:** 64
- **Evaluation Batch Size:** 64
- **Seed:** 42
- **Optimizer:** Adam (default)
- **Number of Epochs:** 15 (with early stopping)
## Model Performance
The model was trained on data from all domains simultaneously and evaluated using stratified 10-fold cross-validation on each individual domain. The weighted F1-score, including the mean, minimum, maximum, and quartile values, is presented below for each domain:
| Domain | Mean | Min | 25% | 50% | 75% | Max |
|--------------|------|------|------|------|------|------|
| Banking | 0.672| 0.640| 0.655| 0.660| 0.690| 0.721|
| Social media | 0.586| 0.567| 0.584| 0.587| 0.593| 0.603|
| Movies | 0.577| 0.556| 0.574| 0.579| 0.580| 0.604|
| Politics | 0.629| 0.566| 0.620| 0.634| 0.644| 0.673|
| Reviews | 0.580| 0.558| 0.578| 0.580| 0.588| 0.597|
## Model Usage
This model is suitable for sentiment classification within the specific domains it was trained on, such as banking, social media, movies, politics, and product reviews. While it may not achieve high F1-scores across all text types, it is well-suited for a wide range of text within these trained domains. However, it may not generalize effectively to entirely different types of text outside these domains.
### Example Usage
Below is an example of how to use the fine-tuned `SK_Morph_BLM-sentiment-multidomain` model in a Python script:
```python
import torch
from transformers import RobertaForSequenceClassification, RobertaTokenizerFast
from huggingface_hub import snapshot_download
class SentimentClassifier:
def __init__(self, tokenizer, model):
self.model = RobertaForSequenceClassification.from_pretrained(model, num_labels=3)
repo_path = snapshot_download(repo_id = tokenizer)
sys.path.append(repo_path)
# Import the custom tokenizer from the downloaded repository
from SKMT_lib_v2.SKMT_BPE import SKMorfoTokenizer
self.tokenizer = SKMorfoTokenizer()
def tokenize_text(self, text):
encoded_text = self.tokenizer.tokenize(text.lower(), max_length=256, return_tensors='pt', return_subword=False)
return encoded_text
def classify_text(self, encoded_text):
with torch.no_grad():
output = self.model(**encoded_text)
logits = output.logits
predicted_class = torch.argmax(logits, dim=1).item()
probabilities = torch.softmax(logits, dim=1)
class_probabilities = probabilities[0].tolist()
predicted_class_text = self.model.config.id2label[predicted_class]
return predicted_class, predicted_class_text, class_probabilities
# Instantiate the sentiment classifier with the specified tokenizer and model
classifier = SentimentClassifier(tokenizer="daviddrzik/SK_Morph_BLM", model="daviddrzik/SK_Morph_BLM-sentiment-multidomain")
# Example text to classify sentiment
text_to_classify = "Napriek zlepšeniu očakávaní je výhľad stále krehký."
print("Text to classify: " + text_to_classify + "\n")
# Tokenize the input text
encoded_text = classifier.tokenize_text(text_to_classify)
# Classify the sentiment of the tokenized text
predicted_class, predicted_class_text, logits = classifier.classify_text(encoded_text)
# Print the predicted class label and index
print(f"Predicted class: {predicted_class_text} ({predicted_class})")
# Print the probabilities for each class
print(f"Class probabilities: {logits}")
```
Here is the output when running the above example:
```yaml
Text to classify: Napriek zlepšeniu očakávaní je výhľad stále krehký.
Predicted class: Positive (2)
Class probabilities: [0.04016311839222908, 0.4200247824192047, 0.5398120284080505]
```
|
daviddrzik/SK_BPE_BLM-sentiment-multidomain
|
daviddrzik
| 2024-09-05T08:11:10Z | 163 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"sentiment",
"sk",
"base_model:daviddrzik/SK_BPE_BLM",
"base_model:finetune:daviddrzik/SK_BPE_BLM",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-09-04T07:42:20Z |
---
license: mit
language:
- sk
pipeline_tag: text-classification
library_name: transformers
metrics:
- f1
base_model: daviddrzik/SK_BPE_BLM
tags:
- sentiment
---
# Fine-Tuned Sentiment Classification Model - SK_BPE_BLM (Universal multi-domain sentiment classification)
## Model Overview
This model is a fine-tuned version of the [SK_BPE_BLM model](https://huggingface.co/daviddrzik/SK_BPE_BLM) for the task of sentiment classification. It has been trained on datasets from multiple domains, including banking, social media, movie reviews, politics, and product reviews. Some of these datasets were originally in Czech and were machine-translated into Slovak using Google Cloud Translation.
## Sentiment Labels
Each row in the dataset is labeled with one of the following sentiments:
- **Negative (0)**
- **Neutral (1)**
- **Positive (2)**
## Dataset Details
The dataset used for fine-tuning comprises text records from various domains. Below are the details for each domain:
### Banking Domain
- **Source**: [Banking Dataset](https://doi.org/10.1016/j.procs.2023.10.346)
- **Description**: Sentences from the annual reports of a commercial bank in Slovakia.
- **Records per Class**: 923
- **Unique Words**: 11,469
- **Average Words per Record**: 20.93
- **Average Characters per Word**: 142.41
### Social Media Domain
- **Source**: [Social Media Dataset](http://hdl.handle.net/11858/00-097C-0000-0022-FE82-7)
- **Description**: Data from posts on the Facebook social network.
- **Records per Class**: 1,991
- **Unique Words**: 114,549
- **Average Words per Record**: 9.24
- **Average Characters per Word**: 57.11
### Movies Domain
- **Source**: [Movies Dataset](https://doi.org/10.1016/j.ipm.2014.05.001)
- **Description**: Short movie reviews from ČSFD.
- **Records per Class**: 3,000
- **Unique Words**: 72,166
- **Average Words per Record**: 52.12
- **Average Characters per Word**: 330.92
### Politics Domain
- **Source**: [Politics Dataset](https://doi.org/10.48550/arXiv.2309.09783)
- **Description**: Sentences from Slovak parliamentary proceedings.
- **Records per Class**: 452
- **Unique Words**: 6,697
- **Average Words per Record**: 12.31
- **Average Characters per Word**: 85.22
### Reviews Domain
- **Source**: [Reviews Dataset](https://aclanthology.org/W13-1609)
- **Description**: Product reviews from Mall.cz.
- **Records per Class**: 3,000
- **Unique Words**: 35,941
- **Average Words per Record**: 21.05
- **Average Characters per Word**: 137.33
## Fine-Tuning Hyperparameters
The following hyperparameters were used during the fine-tuning process:
- **Learning Rate:** 1e-05
- **Training Batch Size:** 64
- **Evaluation Batch Size:** 64
- **Seed:** 42
- **Optimizer:** Adam (default)
- **Number of Epochs:** 15 (with early stopping)
## Model Performance
The model was trained on data from all domains simultaneously and evaluated using stratified 10-fold cross-validation on each individual domain. The weighted F1-score, including the mean, minimum, maximum, and quartile values, is presented below for each domain:
| Domain | Mean | Min | 25% | 50% | 75% | Max |
|--------------|------|------|------|------|------|------|
| Banking | 0.658| 0.608| 0.645| 0.658| 0.674| 0.697|
| Social media | 0.575| 0.552| 0.570| 0.577| 0.581| 0.599|
| Movies | 0.572| 0.518| 0.555| 0.580| 0.590| 0.615|
| Politics | 0.613| 0.576| 0.602| 0.614| 0.623| 0.644|
| Reviews | 0.576| 0.555| 0.562| 0.575| 0.583| 0.608|
## Model Usage
This model is suitable for sentiment classification within the specific domains it was trained on, such as banking, social media, movies, politics, and product reviews. While it may not achieve high F1-scores across all text types, it is well-suited for a wide range of text within these trained domains. However, it may not generalize effectively to entirely different types of text outside these domains.
### Example Usage
Below is an example of how to use the fine-tuned `SK_Morph_BLM-sentiment-multidomain` model in a Python script:
```python
import torch
from transformers import RobertaForSequenceClassification, RobertaTokenizerFast
class SentimentClassifier:
def __init__(self, tokenizer, model):
self.model = RobertaForSequenceClassification.from_pretrained(model, num_labels=3)
self.tokenizer = RobertaTokenizerFast.from_pretrained(tokenizer, max_length=256)
def tokenize_text(self, text):
encoded_text = self.tokenizer.encode_plus(
text.lower(),
max_length=256,
padding='max_length',
truncation=True,
return_tensors='pt'
)
return encoded_text
def classify_text(self, encoded_text):
with torch.no_grad():
output = self.model(**encoded_text)
logits = output.logits
predicted_class = torch.argmax(logits, dim=1).item()
probabilities = torch.softmax(logits, dim=1)
class_probabilities = probabilities[0].tolist()
predicted_class_text = self.model.config.id2label[predicted_class]
return predicted_class, predicted_class_text, class_probabilities
# Instantiate the sentiment classifier with the specified tokenizer and model
classifier = SentimentClassifier(tokenizer="daviddrzik/SK_BPE_BLM", model="daviddrzik/SK_BPE_BLM-sentiment-multidomain")
# Example text to classify sentiment
text_to_classify = "Napriek zlepšeniu očakávaní je výhľad stále krehký."
print("Text to classify: " + text_to_classify + "\n")
# Tokenize the input text
encoded_text = classifier.tokenize_text(text_to_classify)
# Classify the sentiment of the tokenized text
predicted_class, predicted_class_text, logits = classifier.classify_text(encoded_text)
# Print the predicted class label and index
print(f"Predicted class: {predicted_class_text} ({predicted_class})")
# Print the probabilities for each class
print(f"Class probabilities: {logits}")
```
Here is the output when running the above example:
```yaml
Text to classify: Napriek zlepšeniu očakávaní je výhľad stále krehký.
Predicted class: Positive (2)
Class probabilities: [0.003513934789225459, 0.3542619049549103, 0.642224133014679]
```
|
JiaweiGuo123/microsoft-phi-2-fine-tune-alpaca-chinese-merged-model
|
JiaweiGuo123
| 2024-09-05T08:08:27Z | 114 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-05T08:05:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
anindyas/dogbooth
|
anindyas
| 2024-09-05T08:07:35Z | 28 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-09-05T07:33:34Z |
---
base_model: stabilityai/stable-diffusion-2-1
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of [v]dog
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - anindyas/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
Gunulhona/Llama-Ko-Merge
|
Gunulhona
| 2024-09-05T07:59:55Z | 29 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:NousResearch/Hermes-3-Llama-3.1-8B",
"base_model:merge:NousResearch/Hermes-3-Llama-3.1-8B",
"base_model:Saxo/Linkbricks-Horizon-AI-Korean-llama-3.1-sft-dpo-8B",
"base_model:merge:Saxo/Linkbricks-Horizon-AI-Korean-llama-3.1-sft-dpo-8B",
"base_model:Saxo/Linkbricks-Horizon-AI-Korean-llama3-sft-dpo-8b-base",
"base_model:merge:Saxo/Linkbricks-Horizon-AI-Korean-llama3-sft-dpo-8b-base",
"base_model:maum-ai/Llama-3-MAAL-8B-Instruct-v0.1",
"base_model:merge:maum-ai/Llama-3-MAAL-8B-Instruct-v0.1",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:merge:meta-llama/Llama-3.1-8B",
"base_model:tesser-ai/Tesser-Llama-3-Ko-8B",
"base_model:merge:tesser-ai/Tesser-Llama-3-Ko-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-05T07:55:08Z |
---
base_model:
- Saxo/Linkbricks-Horizon-AI-Korean-llama3-sft-dpo-8b-base
- tesser-ai/Tesser-Llama-3-Ko-8B
- NousResearch/Hermes-3-Llama-3.1-8B
- meta-llama/Meta-Llama-3.1-8B
- maum-ai/Llama-3-MAAL-8B-Instruct-v0.1
- Saxo/Linkbricks-Horizon-AI-Korean-llama-3.1-sft-dpo-8B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Saxo/Linkbricks-Horizon-AI-Korean-llama-3.1-sft-dpo-8B](https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-llama-3.1-sft-dpo-8B) as a base.
### Models Merged
The following models were included in the merge:
* [Saxo/Linkbricks-Horizon-AI-Korean-llama3-sft-dpo-8b-base](https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-llama3-sft-dpo-8b-base)
* [tesser-ai/Tesser-Llama-3-Ko-8B](https://huggingface.co/tesser-ai/Tesser-Llama-3-Ko-8B)
* [NousResearch/Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B)
* [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B)
* [maum-ai/Llama-3-MAAL-8B-Instruct-v0.1](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: tesser-ai/Tesser-Llama-3-Ko-8B
layer_range: [0, 32]
parameters:
density: 0.5
weight: 0.45
- model: maum-ai/Llama-3-MAAL-8B-Instruct-v0.1
layer_range: [0, 32]
parameters:
density: 0.5
weight: 0.45
- model: meta-llama/Meta-Llama-3.1-8B
layer_range: [0, 32]
parameters:
density: 0.5
weight: 0.45
- model: NousResearch/Hermes-3-Llama-3.1-8B
layer_range: [0, 32]
parameters:
density: 0.5
weight: 0.45
- model: Saxo/Linkbricks-Horizon-AI-Korean-llama3-sft-dpo-8b-base
layer_range: [0, 32]
parameters:
density: 0.5
weight: 0.45
- model: Saxo/Linkbricks-Horizon-AI-Korean-llama-3.1-sft-dpo-8B
layer_range: [0, 32]
parameters:
density: 0.5
weight: 0.45
merge_method: dare_ties
base_model: Saxo/Linkbricks-Horizon-AI-Korean-llama-3.1-sft-dpo-8B
dtype: bfloat16
```
|
RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf
|
RichardErkhov
| 2024-09-05T07:58:17Z | 5 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-04T11:52:36Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
L3-70B-daybreak-storywriter-v0.4 - GGUF
- Model creator: https://huggingface.co/crestf411/
- Original model: https://huggingface.co/crestf411/L3-70B-daybreak-storywriter-v0.4/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [L3-70B-daybreak-storywriter-v0.4.Q2_K.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/blob/main/L3-70B-daybreak-storywriter-v0.4.Q2_K.gguf) | Q2_K | 24.56GB |
| [L3-70B-daybreak-storywriter-v0.4.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/blob/main/L3-70B-daybreak-storywriter-v0.4.IQ3_XS.gguf) | IQ3_XS | 27.29GB |
| [L3-70B-daybreak-storywriter-v0.4.IQ3_S.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/blob/main/L3-70B-daybreak-storywriter-v0.4.IQ3_S.gguf) | IQ3_S | 28.79GB |
| [L3-70B-daybreak-storywriter-v0.4.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/blob/main/L3-70B-daybreak-storywriter-v0.4.Q3_K_S.gguf) | Q3_K_S | 28.79GB |
| [L3-70B-daybreak-storywriter-v0.4.IQ3_M.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/blob/main/L3-70B-daybreak-storywriter-v0.4.IQ3_M.gguf) | IQ3_M | 29.74GB |
| [L3-70B-daybreak-storywriter-v0.4.Q3_K.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/blob/main/L3-70B-daybreak-storywriter-v0.4.Q3_K.gguf) | Q3_K | 31.91GB |
| [L3-70B-daybreak-storywriter-v0.4.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/blob/main/L3-70B-daybreak-storywriter-v0.4.Q3_K_M.gguf) | Q3_K_M | 31.91GB |
| [L3-70B-daybreak-storywriter-v0.4.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/blob/main/L3-70B-daybreak-storywriter-v0.4.Q3_K_L.gguf) | Q3_K_L | 34.59GB |
| [L3-70B-daybreak-storywriter-v0.4.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/blob/main/L3-70B-daybreak-storywriter-v0.4.IQ4_XS.gguf) | IQ4_XS | 35.64GB |
| [L3-70B-daybreak-storywriter-v0.4.Q4_0.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/blob/main/L3-70B-daybreak-storywriter-v0.4.Q4_0.gguf) | Q4_0 | 37.22GB |
| [L3-70B-daybreak-storywriter-v0.4.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | IQ4_NL | 37.58GB |
| [L3-70B-daybreak-storywriter-v0.4.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | Q4_K_S | 37.58GB |
| [L3-70B-daybreak-storywriter-v0.4.Q4_K.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | Q4_K | 39.6GB |
| [L3-70B-daybreak-storywriter-v0.4.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | Q4_K_M | 39.6GB |
| [L3-70B-daybreak-storywriter-v0.4.Q4_1.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | Q4_1 | 41.27GB |
| [L3-70B-daybreak-storywriter-v0.4.Q5_0.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | Q5_0 | 45.32GB |
| [L3-70B-daybreak-storywriter-v0.4.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | Q5_K_S | 45.32GB |
| [L3-70B-daybreak-storywriter-v0.4.Q5_K.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | Q5_K | 46.52GB |
| [L3-70B-daybreak-storywriter-v0.4.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | Q5_K_M | 46.52GB |
| [L3-70B-daybreak-storywriter-v0.4.Q5_1.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | Q5_1 | 49.36GB |
| [L3-70B-daybreak-storywriter-v0.4.Q6_K.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | Q6_K | 53.91GB |
| [L3-70B-daybreak-storywriter-v0.4.Q8_0.gguf](https://huggingface.co/RichardErkhov/crestf411_-_L3-70B-daybreak-storywriter-v0.4-gguf/tree/main/) | Q8_0 | 69.83GB |
Original model description:
---
tags:
- not-for-all-audiences
---
Daybreak (2024 May 24) v0.4 LoRA on top of https://huggingface.co/tdrussell/Llama-3-70B-Instruct-Storywriter
Dataset curation to remove slop-perceived expressions continues.
The below regexes return 0 matches. **Bold** entries are new since v0.3.
* 'barely above a whisper',
* **'barely audible',**
* 'shiver([s]?) down',
* ' ministration',
* 'audible (["\'"]?)p[l]?op',
* 'can\'t help but',
* 'buck([s]?) my ',
* 'buck([s]?) h[ei][rs] ',
* '[Dd]espite h[ie][mr]self',
* 'slick slit',
* 'whatever it takes',
* 'unlike anything (s?)he',
* **'a mix([a-z]*) of',**
* 'wave after wave',
* 'reckless abandon',
* '[Mm]aybe, just maybe',
* **'eyes gleaming',**
* **'mischievously',**
* **"couldn't help but",**
From testing so far, it feels like temperature 0.8-0.9 is a good starting point. I have mostly tested with everything neutralized. Please give feedback on which parameters work good for you.
EXL2 quants made by kim512 be found in (scroll to bottom for updated quants) https://huggingface.co/crestf411/L3-70B-daybreak-storywriter-v0.4/discussions/2 including the settings used to make them.
|
uriel/test_llm_nllb_100_e_12_lr3e5_ada
|
uriel
| 2024-09-05T07:55:51Z | 7 | 0 | null |
[
"tensorboard",
"safetensors",
"m2m_100",
"generated_from_trainer",
"base_model:facebook/nllb-200-distilled-600M",
"base_model:finetune:facebook/nllb-200-distilled-600M",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-09-05T03:33:58Z |
---
license: cc-by-nc-4.0
base_model: facebook/nllb-200-distilled-600M
tags:
- generated_from_trainer
metrics:
- rouge
- sacrebleu
model-index:
- name: test_llm_nllb_100_e_12_lr3e5_ada
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_llm_nllb_100_e_12_lr3e5_ada
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5388
- Rouge1: 0.6155
- Rouge2: 0.3817
- Rougel: 0.57
- Sacrebleu: 23.323
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 237
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Sacrebleu |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.5121 | 1.0 | 2040 | 0.5072 | 0.5877 | 0.3511 | 0.5432 | 20.8262 |
| 0.4381 | 2.0 | 4080 | 0.4802 | 0.6022 | 0.3684 | 0.5572 | 21.9614 |
| 0.3465 | 3.0 | 6120 | 0.4816 | 0.6143 | 0.3806 | 0.569 | 23.0564 |
| 0.3084 | 4.0 | 8160 | 0.4832 | 0.6172 | 0.3872 | 0.5726 | 23.4185 |
| 0.2836 | 5.0 | 10200 | 0.4902 | 0.6196 | 0.3883 | 0.5751 | 23.7412 |
| 0.2479 | 6.0 | 12240 | 0.5001 | 0.6182 | 0.3832 | 0.5719 | 23.1833 |
| 0.2036 | 7.0 | 14280 | 0.5112 | 0.6191 | 0.3865 | 0.5746 | 23.1771 |
| 0.1973 | 8.0 | 16320 | 0.5190 | 0.6207 | 0.3865 | 0.5735 | 23.2226 |
| 0.1615 | 9.0 | 18360 | 0.5258 | 0.619 | 0.3877 | 0.5733 | 23.7005 |
| 0.1546 | 10.0 | 20400 | 0.5335 | 0.6172 | 0.3835 | 0.5708 | 23.458 |
| 0.1345 | 11.0 | 22440 | 0.5336 | 0.6125 | 0.3786 | 0.5665 | 23.1359 |
| 0.1294 | 12.0 | 24480 | 0.5388 | 0.6155 | 0.3817 | 0.57 | 23.323 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|
AdrienB134/Parler-TTS-French-v0.2
|
AdrienB134
| 2024-09-05T07:50:14Z | 48 | 0 |
transformers
|
[
"transformers",
"safetensors",
"parler_tts",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-09-05T07:49:01Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Statuo/Stardust-V2-EXL2-4bpw
|
Statuo
| 2024-09-05T07:40:48Z | 7 | 0 | null |
[
"safetensors",
"mistral",
"chat",
"roleplay",
"creative-writing",
"text-generation",
"conversational",
"en",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:Gryphe/Pantheon-RP-1.6-12b-Nemo",
"base_model:quantized:Gryphe/Pantheon-RP-1.6-12b-Nemo",
"license:apache-2.0",
"4-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-09-05T07:19:39Z |
---
license: apache-2.0
pipeline_tag: text-generation
tags:
- chat
- mistral
- roleplay
- creative-writing
base_model:
- nbeerbower/mistral-nemo-bophades-12B
- anthracite-org/magnum-v2-12b
- Sao10K/MN-12B-Lyra-v3
- Gryphe/Pantheon-RP-1.6-12b-Nemo
language:
- en
---
Quanting the Stardust V2 model. According to the model card this is a slightly different tune which you can see in the Usecase section from the original model card below. Highly recommend giving it a read-over and determining if you still want to try it before downloading. Either way, I intend to give it a shot.
<br>
[This is the EXL2 4bpw version of this model. Find the original model here.](https://huggingface.co/Luni/StarDust-12b-v2)
<br>
[Find the 8bpw version here.](https://huggingface.co/Statuo/Stardust-V2-EXL2-8bpw)
<br>
[Find the 6bpw version here.](https://huggingface.co/Statuo/Stardust-V2-EXL2-6bpw)
# StarDust-12b-v2
## Quants
- GGUF: [mradermacher/StarDust-12b-v2-GGUF](https://huggingface.co/mradermacher/StarDust-12b-v2-GGUF)
- weighted/imatrix GGUF: [mradermacher/StarDust-12b-v2-i1-GGUF](https://huggingface.co/mradermacher/StarDust-12b-v2-i1-GGUF/tree/main)
- exl2: [lucyknada/Luni_StarDust-12b-v2-exl2](https://huggingface.co/lucyknada/Luni_StarDust-12b-v2-exl2)
## Description | Usecase
- The result of this merge is in my opinion a more vibrant and less generic sonnet inspired prose, it's able to be gentle and harsh where asked.
- The v2 uses the non-kto magnum which tends to have less "claudeism" (making the story feel rather repetitive)
- Note on Non-Kto: There is a very big gap between people preferring and disliking the KTO. To make things easier, you can still use [Luni/StarDust-12b-v1](https://huggingface.co/Luni/StarDust-12b-v1) which has the KTO version.
- In early testing users have reported a much better experience in longer roleplays and a abillity to add a creative touch to the stable experiencve.
Just like with v1:
- This model is intended to be used as a Role-playing model.
- Its direct conversational output is... I can't even say it's luck, it's just not made for it.
- Extension to Conversational output: The Model is designed for roleplay, direct instructing or general purpose is NOT recommended.
## Initial Feedback
- Initial feedback has proven the model to be a solid "go-to" choice for creative storywriting
- The prose has been certified as "amazing" with many making it their default model.
## Prompting
### ChatML has proven to be the BEST choice.
Both Mistral and ChatML should work though I had better results with ChatML:
ChatML Example:
```py
"""<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""
```
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Sao10K/MN-12B-Lyra-v3](https://huggingface.co/Sao10K/MN-12B-Lyra-v3) as a base.
### Models Merged
The following models were included in the merge:
* [nbeerbower/mistral-nemo-bophades-12B](https://huggingface.co/nbeerbower/mistral-nemo-bophades-12B)
* [anthracite-org/magnum-v2-12b](https://huggingface.co/anthracite-org/magnum-v2-12b)
* [Gryphe/Pantheon-RP-1.6-12b-Nemo](https://huggingface.co/Gryphe/Pantheon-RP-1.6-12b-Nemo)
* [Sao10K/MN-12B-Lyra-v3](https://huggingface.co/Sao10K/MN-12B-Lyra-v3)
### Special Thanks
Special thanks to the SillyTilly and myself for helping me find the energy to finish this.
|
Statuo/Stardust-V2-EXL2-6bpw
|
Statuo
| 2024-09-05T07:37:43Z | 5 | 0 | null |
[
"safetensors",
"mistral",
"chat",
"roleplay",
"creative-writing",
"text-generation",
"conversational",
"en",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:Gryphe/Pantheon-RP-1.6-12b-Nemo",
"base_model:quantized:Gryphe/Pantheon-RP-1.6-12b-Nemo",
"license:apache-2.0",
"6-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-09-05T07:19:31Z |
---
license: apache-2.0
pipeline_tag: text-generation
tags:
- chat
- mistral
- roleplay
- creative-writing
base_model:
- nbeerbower/mistral-nemo-bophades-12B
- anthracite-org/magnum-v2-12b
- Sao10K/MN-12B-Lyra-v3
- Gryphe/Pantheon-RP-1.6-12b-Nemo
language:
- en
---
Quanting the Stardust V2 model. According to the model card this is a slightly different tune which you can see in the Usecase section from the original model card below. Highly recommend giving it a read-over and determining if you still want to try it before downloading. Either way, I intend to give it a shot.
<br>
[This is the EXL2 6bpw version of this model. Find the original model here.](https://huggingface.co/Luni/StarDust-12b-v2)
<br>
[Find the 8bpw version here.](https://huggingface.co/Statuo/Stardust-V2-EXL2-8bpw)
<br>
[Find the 4bpw version here.](https://huggingface.co/Statuo/Stardust-V2-EXL2-4bpw)
# StarDust-12b-v2
## Quants
- GGUF: [mradermacher/StarDust-12b-v2-GGUF](https://huggingface.co/mradermacher/StarDust-12b-v2-GGUF)
- weighted/imatrix GGUF: [mradermacher/StarDust-12b-v2-i1-GGUF](https://huggingface.co/mradermacher/StarDust-12b-v2-i1-GGUF/tree/main)
- exl2: [lucyknada/Luni_StarDust-12b-v2-exl2](https://huggingface.co/lucyknada/Luni_StarDust-12b-v2-exl2)
## Description | Usecase
- The result of this merge is in my opinion a more vibrant and less generic sonnet inspired prose, it's able to be gentle and harsh where asked.
- The v2 uses the non-kto magnum which tends to have less "claudeism" (making the story feel rather repetitive)
- Note on Non-Kto: There is a very big gap between people preferring and disliking the KTO. To make things easier, you can still use [Luni/StarDust-12b-v1](https://huggingface.co/Luni/StarDust-12b-v1) which has the KTO version.
- In early testing users have reported a much better experience in longer roleplays and a abillity to add a creative touch to the stable experiencve.
Just like with v1:
- This model is intended to be used as a Role-playing model.
- Its direct conversational output is... I can't even say it's luck, it's just not made for it.
- Extension to Conversational output: The Model is designed for roleplay, direct instructing or general purpose is NOT recommended.
## Initial Feedback
- Initial feedback has proven the model to be a solid "go-to" choice for creative storywriting
- The prose has been certified as "amazing" with many making it their default model.
## Prompting
### ChatML has proven to be the BEST choice.
Both Mistral and ChatML should work though I had better results with ChatML:
ChatML Example:
```py
"""<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""
```
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Sao10K/MN-12B-Lyra-v3](https://huggingface.co/Sao10K/MN-12B-Lyra-v3) as a base.
### Models Merged
The following models were included in the merge:
* [nbeerbower/mistral-nemo-bophades-12B](https://huggingface.co/nbeerbower/mistral-nemo-bophades-12B)
* [anthracite-org/magnum-v2-12b](https://huggingface.co/anthracite-org/magnum-v2-12b)
* [Gryphe/Pantheon-RP-1.6-12b-Nemo](https://huggingface.co/Gryphe/Pantheon-RP-1.6-12b-Nemo)
* [Sao10K/MN-12B-Lyra-v3](https://huggingface.co/Sao10K/MN-12B-Lyra-v3)
### Special Thanks
Special thanks to the SillyTilly and myself for helping me find the energy to finish this.
|
Kukedlc/Gemma2-2b-Spanish-Roleplay
|
Kukedlc
| 2024-09-05T07:29:18Z | 89 | 2 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-05T07:25:11Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
NaughtyDog97/DiagramFormalizer
|
NaughtyDog97
| 2024-09-05T07:28:22Z | 141 | 1 |
transformers
|
[
"transformers",
"safetensors",
"fegeo-qwen2",
"text-generation",
"conversational",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-07-21T05:19:19Z |
---
license: apache-2.0
---
# Diagram Formalizer
Model Structure:
<p align="center">
<img src="sample/diagram_formalizer.png" alt="Alt text" width="50%" height="auto">
</p>
- **Diagram Encoder**: [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384)
- **Lightweight LLM**: [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)
## Quick Start
Before running the script, install the following necessary dependencies.
```shell
pip install torch==2.4.0 transformers==4.40.0 accelerate pillow sentencepiece
```
You can use the following script to predict the ConsCDL and ImgCDL for geometric diagram.
```python
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings
import numpy as np
# set device
device = 'cuda' # or cpu
torch.set_default_device(device)
# create model
model = AutoModelForCausalLM.from_pretrained(
'NaughtyDog97/DiagramFormalizer',
torch_dtype=torch.float16, # float32 for cpu
device_map='auto',
trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
'NaughtyDog97/DiagramFormalizer',
use_fast=True,
padding_side="right",
trust_remote_code=True)
# text prompt
img_path = 'sample/4927.png'
prompt = 'Based on the image, first describe what you see in the figure, then predict the construction_cdl and image_cdl and calibrate it.'
text = f'<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\n{prompt}<|im_end|>\n<|im_start|>assistant\n'
def tokenizer_image_token(prompt, tokenizer, image_token_index, return_tensors=None):
prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
def insert_separator(X, sep):
return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1]
input_ids = []
offset = 0
if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
offset = 1
input_ids.append(prompt_chunks[0][0])
for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
input_ids.extend(x[offset:])
if return_tensors is not None:
if return_tensors == 'pt':
return torch.tensor(input_ids, dtype=torch.long)
raise ValueError(f'Unsupported tensor type: {return_tensors}')
return input_ids
input_ids = tokenizer_image_token(text, tokenizer, -200, return_tensors='pt').unsqueeze(0).cuda()
# image, sample images can be found in images folder
image = Image.open(img_path).convert('RGB')
image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device)
# generate
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
do_sample=False,
temperature=None,
top_p=None,
top_k=None,
num_beams=1,
max_new_tokens=3500,
eos_token_id=tokenizer.eos_token_id,
repetition_penalty=None,
use_cache=True
)[0]
respones = tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()
print(respones)
```
Our model supports the following recognition instrutions:
- Natural Language Description:
- Describe what you see in the figure.
- Tell me what you observe in the image.
- Predicting ConsCDL only
- Based on the image, predict the construction_cdl.
- Based on the image, predict the construction_cdl and calibrate it.
- Based on the image, first describe what you see in the figure, then predict the construction_cdl.
- Based on the image, first describe what you see in the figure, then predict the construction_cdl and calibrate it.
- Predicting ImgCDL only:
- Based on the image, predict the image_cdl.
- Based on the image, predict the image_cdl and calibrate it.
- Based on the image, first describe what you see in the figure, then predict the image_cdl.
- Based on the image, first describe what you see in the figure, then predict the image_cdl and calibrate it.
- Predicting construction_cdl and image_cdl simultaneously:
- Based on the image, predict the construction_cdl and image_cdl.
- Based on the image, first predict the construction_cdl and image_cdl and calibrate it.
- Based on the image, first describe what you see in the figure, then predict the construction_cdl and image_cdl.
- Based on the image, first describe what you see in the figure, then predict the construction_cdl and image_cdl and calibrate it.
## Performance of Diagram Formalizer on formalgeo7k test set
| Model | ConsCdlAcc | ConsCdlPerfect | ImgCdlAcc | ImgCdlPerfect | BothPerfect |
|-----|----------------|---------------------|---------------|-------------------|------------------|
| Diagram Formalizer | 90.25 | 72.29 | 92.88 | 84.38 | 65.05 |
|
EVA787797/juuiu8988
|
EVA787797
| 2024-09-05T07:23:47Z | 8 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:XLabs-AI/flux-lora-collection",
"base_model:adapter:XLabs-AI/flux-lora-collection",
"license:afl-3.0",
"region:us"
] |
text-to-image
| 2024-09-05T07:23:39Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/out-0 - 2024-09-01T112736.766.webp
- text: '-'
output:
url: images/out-0 - 2024-09-01T081638.859.webp
- text: '-'
output:
url: images/out-0 - 2024-09-01T074119.731.webp
- text: '-'
output:
url: >-
images/d76e91e30dc88614dd13886a6aaae148b89260a15234e6deef6b06524e167b63.png
- text: '-'
output:
url: images/out-0 - 2024-08-31T170633.926.webp
- text: '-'
output:
url: images/out-0 - 2024-08-30T072705.763 (1 (1).webp
- text: '-'
output:
url: images/out-0 - 2024-08-30T072705.763 (1.webp
- text: '-'
output:
url: images/out-0 - 2024-08-31T163258.493.webp
- text: '-'
output:
url: images/out-0 - 2024-08-30T072705.763 (1).webp
- text: '-'
output:
url: >-
images/glif-snap-machine-no-text-remix-marclilio587877-kmzoff8yk82ol0n3f10xv94q.png
- text: '-'
output:
url: images/ad28fa26-c809-465b-a151-c995f2fad8e6.jpeg
- text: '-'
output:
url: images/out-0 - 2024-08-31T154419.439.webp
base_model: XLabs-AI/flux-lora-collection
instance_prompt: femme
license: afl-3.0
---
# laura
<Gallery />
## Trigger words
You should use `femme` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/EVA787797/juuiu8988/tree/main) them in the Files & versions tab.
|
art1xgg/whisper-small-uk
|
art1xgg
| 2024-09-05T07:04:51Z | 76 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"uk",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-09-05T07:03:15Z |
---
library_name: transformers
language:
- uk
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small uk - Artix
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small uk - Artix
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1
|
chandra10/image_classification
|
chandra10
| 2024-09-05T06:55:41Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-09-01T07:56:46Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.625
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2826
- Accuracy: 0.625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.071 | 1.0 | 10 | 2.0532 | 0.2125 |
| 1.9763 | 2.0 | 20 | 1.9614 | 0.3312 |
| 1.8031 | 3.0 | 30 | 1.8326 | 0.4562 |
| 1.6168 | 4.0 | 40 | 1.7015 | 0.5125 |
| 1.4508 | 5.0 | 50 | 1.6065 | 0.5188 |
| 1.3037 | 6.0 | 60 | 1.5397 | 0.5375 |
| 1.1709 | 7.0 | 70 | 1.4836 | 0.55 |
| 1.0481 | 8.0 | 80 | 1.4248 | 0.5813 |
| 0.9441 | 9.0 | 90 | 1.3915 | 0.5625 |
| 0.8551 | 10.0 | 100 | 1.3586 | 0.6 |
| 0.7772 | 11.0 | 110 | 1.3315 | 0.6 |
| 0.7174 | 12.0 | 120 | 1.3057 | 0.6062 |
| 0.6721 | 13.0 | 130 | 1.2936 | 0.6188 |
| 0.642 | 14.0 | 140 | 1.2933 | 0.6 |
| 0.6252 | 15.0 | 150 | 1.2826 | 0.625 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
QuantFactory/Yi-Coder-1.5B-GGUF
|
QuantFactory
| 2024-09-05T06:52:32Z | 166 | 4 | null |
[
"gguf",
"arxiv:2403.04652",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-09-05T06:44:48Z |
---
license: apache-2.0
---
# QuantFactory/Yi-Coder-1.5B-GGUF
This is quantized version of [01-ai/Yi-Coder-1.5B](https://huggingface.co/01-ai/Yi-Coder-1.5B) created using llama.cpp
# Original Model Card
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="120px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://01-ai.github.io/">💪 Tech Blog</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.
Key features:
- Excelling in long-context understanding with a maximum context length of 128K tokens.
- Supporting 52 major programming languages:
```bash
'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'
```
For model details and benchmarks, see [Yi-Coder blog](https://01-ai.github.io/) and [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
<p align="left">
<img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/yi-coder-calculator-demo.gif?raw=true" alt="demo1" width="500"/>
</p>
# Models
| Name | Type | Length | Download |
|--------------------|------|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------|
| Yi-Coder-9B-Chat | Chat | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B-Chat) |
| Yi-Coder-1.5B-Chat | Chat | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B-Chat) |
| Yi-Coder-9B | Base | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B) |
| Yi-Coder-1.5B | Base | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B) |
| |
# Benchmarks
As illustrated in the figure below, Yi-Coder-9B-Chat achieved an impressive 23% pass rate in LiveCodeBench, making it the only model with under 10B parameters to surpass 20%. It also outperforms DeepSeekCoder-33B-Ins at 22.3%, CodeGeex4-9B-all at 17.8%, CodeLLama-34B-Ins at 13.3%, and CodeQwen1.5-7B-Chat at 12%.
<p align="left">
<img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/bench1.webp?raw=true" alt="bench1" width="1000"/>
</p>
# Quick Start
You can use transformers to run inference with Yi-Coder models (both chat and base versions) as follows:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
device = "cuda" # the device to load the model onto
model_path = "01-ai/Yi-Coder-9B-Chat"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval()
prompt = "Write a quick sort algorithm."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=1024,
eos_token_id=tokenizer.eos_token_id
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
For getting up and running with Yi-Coder series models quickly, see [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
|
DhruvDancingBuddha/osho_discourses_roberta_128
|
DhruvDancingBuddha
| 2024-09-05T06:49:18Z | 82 | 1 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"fill-mask",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-08-26T12:04:43Z |
---
license: apache-2.0
pipeline_tag: fill-mask
library_name: transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Masked Langauge Model Trained on OSHO discourses.
Base Model is ROBERTA Model by Face Book which is an encoder only model.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ai4bharat/IndicBERTv2-MLM-Sam-TLM
|
ai4bharat
| 2024-09-05T06:45:25Z | 329 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"indicbert2",
"ai4bharat",
"multilingual",
"as",
"bn",
"brx",
"doi",
"en",
"gom",
"gu",
"hi",
"kn",
"ks",
"kas",
"mai",
"ml",
"mr",
"mni",
"mnb",
"ne",
"or",
"pa",
"sa",
"sat",
"sd",
"snd",
"ta",
"te",
"ur",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-13T15:52:03Z |
---
language:
- as
- bn
- brx
- doi
- en
- gom
- gu
- hi
- kn
- ks
- kas
- mai
- ml
- mr
- mni
- mnb
- ne
- or
- pa
- sa
- sat
- sd
- snd
- ta
- te
- ur
language_details: >-
asm_Beng, ben_Beng, brx_Deva, doi_Deva, eng_Latn, gom_Deva, guj_Gujr,
hin_Deva, kan_Knda, kas_Arab, kas_Deva, mai_Deva, mal_Mlym, mar_Deva,
mni_Beng, mni_Mtei, npi_Deva, ory_Orya, pan_Guru, san_Deva, sat_Olck,
snd_Arab, snd_Deva, tam_Taml, tel_Telu, urd_Arab
tags:
- indicbert2
- ai4bharat
- multilingual
license: mit
metrics:
- accuracy
pipeline_tag: fill-mask
---
# IndicBERT
A multilingual language model trained on IndicCorp v2 and evaluated on IndicXTREME benchmark. The model has 278M parameters and is available in 23 Indic languages and English. The models are trained with various objectives and datasets. The list of models are as follows:
- IndicBERT-MLM [[Model](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-only)] - A vanilla BERT style model trained on IndicCorp v2 with the MLM objective
- +Samanantar [[Model](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Sam-TLM)] - TLM as an additional objective with Samanantar Parallel Corpus [[Paper](https://aclanthology.org/2022.tacl-1.9)] | [[Dataset](https://huggingface.co/datasets/ai4bharat/samanantar)]
- +Back-Translation [[Model](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Back-TLM)] - TLM as an additional objective by translating the Indic parts of IndicCorp v2 dataset into English w/ IndicTrans model [[Model](https://github.com/AI4Bharat/indicTrans#download-model)]
- IndicBERT-SS [[Model](https://huggingface.co/ai4bharat/IndicBERTv2-SS)] - To encourage better lexical sharing among languages we convert the scripts from Indic languages to Devanagari and train a BERT style model with the MLM objective
## Run Fine-tuning
Fine-tuning scripts are based on transformers library. Create a new conda environment and set it up as follows:
```shell
conda create -n finetuning python=3.9
pip install -r requirements.txt
```
All the tasks follow the same structure, please check individual files for detailed hyper-parameter choices. The following command runs the fine-tuning for a task:
```shell
python IndicBERT/fine-tuning/$TASK_NAME/$TASK_NAME.py \
--model_name_or_path=$MODEL_NAME \
--do_train
```
Arguments:
- MODEL_NAME: name of the model to fine-tune, can be a local path or a model from the [HuggingFace Model Hub](https://huggingface.co/models)
- TASK_NAME: one of [`ner, paraphrase, qa, sentiment, xcopa, xnli, flores`]
> For MASSIVE task, please use the instrction provided in the [official repository](https://github.com/alexa/massive)
## Citation
```
@inproceedings{doddapaneni-etal-2023-towards,
title = "Towards Leaving No {I}ndic Language Behind: Building Monolingual Corpora, Benchmark and Models for {I}ndic Languages",
author = "Doddapaneni, Sumanth and
Aralikatte, Rahul and
Ramesh, Gowtham and
Goyal, Shreya and
Khapra, Mitesh M. and
Kunchukuttan, Anoop and
Kumar, Pratyush",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.693",
doi = "10.18653/v1/2023.acl-long.693",
pages = "12402--12426",
abstract = "Building Natural Language Understanding (NLU) capabilities for Indic languages, which have a collective speaker base of more than one billion speakers is absolutely crucial. In this work, we aim to improve the NLU capabilities of Indic languages by making contributions along 3 important axes (i) monolingual corpora (ii) NLU testsets (iii) multilingual LLMs focusing on Indic languages. Specifically, we curate the largest monolingual corpora, IndicCorp, with 20.9B tokens covering 24 languages from 4 language families - a 2.3x increase over prior work, while supporting 12 additional languages. Next, we create a human-supervised benchmark, IndicXTREME, consisting of nine diverse NLU tasks covering 20 languages. Across languages and tasks, IndicXTREME contains a total of 105 evaluation sets, of which 52 are new contributions to the literature. To the best of our knowledge, this is the first effort towards creating a standard benchmark for Indic languages that aims to test the multilingual zero-shot capabilities of pretrained language models. Finally, we train IndicBERT v2, a state-of-the-art model supporting all the languages. Averaged across languages and tasks, the model achieves an absolute improvement of 2 points over a strong baseline. The data and models are available at \url{https://github.com/AI4Bharat/IndicBERT}.",
}
```
|
mradermacher/openchat_3.5-GGUF
|
mradermacher
| 2024-09-05T06:45:17Z | 9 | 0 |
transformers
|
[
"transformers",
"gguf",
"openchat",
"mistral",
"C-RLFT",
"en",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:imone/OpenOrca_FLAN",
"dataset:LDJnr/LessWrong-Amplify-Instruct",
"dataset:LDJnr/Pure-Dove",
"dataset:LDJnr/Verified-Camel",
"dataset:tiedong/goat",
"dataset:glaiveai/glaive-code-assistant",
"dataset:meta-math/MetaMathQA",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"dataset:TIGER-Lab/MathInstruct",
"base_model:openchat/openchat_3.5",
"base_model:quantized:openchat/openchat_3.5",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-04T23:51:28Z |
---
base_model: openchat/openchat_3.5
datasets:
- openchat/openchat_sharegpt4_dataset
- imone/OpenOrca_FLAN
- LDJnr/LessWrong-Amplify-Instruct
- LDJnr/Pure-Dove
- LDJnr/Verified-Camel
- tiedong/goat
- glaiveai/glaive-code-assistant
- meta-math/MetaMathQA
- OpenAssistant/oasst_top1_2023-08-25
- TIGER-Lab/MathInstruct
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- openchat
- mistral
- C-RLFT
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/openchat/openchat_3.5
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-GGUF/resolve/main/openchat_3.5.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/openchat_3.5-i1-GGUF
|
mradermacher
| 2024-09-05T06:45:17Z | 35 | 0 |
transformers
|
[
"transformers",
"gguf",
"openchat",
"mistral",
"C-RLFT",
"en",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:imone/OpenOrca_FLAN",
"dataset:LDJnr/LessWrong-Amplify-Instruct",
"dataset:LDJnr/Pure-Dove",
"dataset:LDJnr/Verified-Camel",
"dataset:tiedong/goat",
"dataset:glaiveai/glaive-code-assistant",
"dataset:meta-math/MetaMathQA",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"dataset:TIGER-Lab/MathInstruct",
"base_model:openchat/openchat_3.5",
"base_model:quantized:openchat/openchat_3.5",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-09-05T05:33:38Z |
---
base_model: openchat/openchat_3.5
datasets:
- openchat/openchat_sharegpt4_dataset
- imone/OpenOrca_FLAN
- LDJnr/LessWrong-Amplify-Instruct
- LDJnr/Pure-Dove
- LDJnr/Verified-Camel
- tiedong/goat
- glaiveai/glaive-code-assistant
- meta-math/MetaMathQA
- OpenAssistant/oasst_top1_2023-08-25
- TIGER-Lab/MathInstruct
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- openchat
- mistral
- C-RLFT
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/openchat/openchat_3.5
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/openchat_3.5-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/openchat_3.5-i1-GGUF/resolve/main/openchat_3.5.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
ai4bharat/IndicBERTv2-MLM-only
|
ai4bharat
| 2024-09-05T06:43:18Z | 137,472 | 7 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"indicbert2",
"ai4bharat",
"multilingual",
"as",
"bn",
"brx",
"doi",
"en",
"gom",
"gu",
"hi",
"kn",
"ks",
"kas",
"mai",
"ml",
"mr",
"mni",
"mnb",
"ne",
"or",
"pa",
"sa",
"sat",
"sd",
"snd",
"ta",
"te",
"ur",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-13T15:32:45Z |
---
language:
- as
- bn
- brx
- doi
- en
- gom
- gu
- hi
- kn
- ks
- kas
- mai
- ml
- mr
- mni
- mnb
- ne
- or
- pa
- sa
- sat
- sd
- snd
- ta
- te
- ur
language_details: >-
asm_Beng, ben_Beng, brx_Deva, doi_Deva, eng_Latn, gom_Deva, guj_Gujr,
hin_Deva, kan_Knda, kas_Arab, kas_Deva, mai_Deva, mal_Mlym, mar_Deva,
mni_Beng, mni_Mtei, npi_Deva, ory_Orya, pan_Guru, san_Deva, sat_Olck,
snd_Arab, snd_Deva, tam_Taml, tel_Telu, urd_Arab
tags:
- indicbert2
- ai4bharat
- multilingual
license: mit
metrics:
- accuracy
pipeline_tag: fill-mask
---
# IndicBERT
A multilingual language model trained on IndicCorp v2 and evaluated on IndicXTREME benchmark. The model has 278M parameters and is available in 23 Indic languages and English. The models are trained with various objectives and datasets. The list of models are as follows:
- IndicBERT-MLM [[Model](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-only)] - A vanilla BERT style model trained on IndicCorp v2 with the MLM objective
- +Samanantar [[Model](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Sam-TLM)] - TLM as an additional objective with Samanantar Parallel Corpus [[Paper](https://aclanthology.org/2022.tacl-1.9)] | [[Dataset](https://huggingface.co/datasets/ai4bharat/samanantar)]
- +Back-Translation [[Model](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Back-TLM)] - TLM as an additional objective by translating the Indic parts of IndicCorp v2 dataset into English w/ IndicTrans model [[Model](https://github.com/AI4Bharat/indicTrans#download-model)]
- IndicBERT-SS [[Model](https://huggingface.co/ai4bharat/IndicBERTv2-SS)] - To encourage better lexical sharing among languages we convert the scripts from Indic languages to Devanagari and train a BERT style model with the MLM objective
## Run Fine-tuning
Fine-tuning scripts are based on transformers library. Create a new conda environment and set it up as follows:
```shell
conda create -n finetuning python=3.9
pip install -r requirements.txt
```
All the tasks follow the same structure, please check individual files for detailed hyper-parameter choices. The following command runs the fine-tuning for a task:
```shell
python IndicBERT/fine-tuning/$TASK_NAME/$TASK_NAME.py \
--model_name_or_path=$MODEL_NAME \
--do_train
```
Arguments:
- MODEL_NAME: name of the model to fine-tune, can be a local path or a model from the [HuggingFace Model Hub](https://huggingface.co/models)
- TASK_NAME: one of [`ner, paraphrase, qa, sentiment, xcopa, xnli, flores`]
> For MASSIVE task, please use the instrction provided in the [official repository](https://github.com/alexa/massive)
## Citation
```
@inproceedings{doddapaneni-etal-2023-towards,
title = "Towards Leaving No {I}ndic Language Behind: Building Monolingual Corpora, Benchmark and Models for {I}ndic Languages",
author = "Doddapaneni, Sumanth and
Aralikatte, Rahul and
Ramesh, Gowtham and
Goyal, Shreya and
Khapra, Mitesh M. and
Kunchukuttan, Anoop and
Kumar, Pratyush",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.693",
doi = "10.18653/v1/2023.acl-long.693",
pages = "12402--12426",
abstract = "Building Natural Language Understanding (NLU) capabilities for Indic languages, which have a collective speaker base of more than one billion speakers is absolutely crucial. In this work, we aim to improve the NLU capabilities of Indic languages by making contributions along 3 important axes (i) monolingual corpora (ii) NLU testsets (iii) multilingual LLMs focusing on Indic languages. Specifically, we curate the largest monolingual corpora, IndicCorp, with 20.9B tokens covering 24 languages from 4 language families - a 2.3x increase over prior work, while supporting 12 additional languages. Next, we create a human-supervised benchmark, IndicXTREME, consisting of nine diverse NLU tasks covering 20 languages. Across languages and tasks, IndicXTREME contains a total of 105 evaluation sets, of which 52 are new contributions to the literature. To the best of our knowledge, this is the first effort towards creating a standard benchmark for Indic languages that aims to test the multilingual zero-shot capabilities of pretrained language models. Finally, we train IndicBERT v2, a state-of-the-art model supporting all the languages. Averaged across languages and tasks, the model achieves an absolute improvement of 2 points over a strong baseline. The data and models are available at \url{https://github.com/AI4Bharat/IndicBERT}.",
}
```
|
dishype/pasu-lora
|
dishype
| 2024-09-05T06:41:21Z | 5 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-09-05T05:31:03Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: pasu
---
# Pasu Lora
<!-- <Gallery /> -->
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `pasu` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('dishype/pasu-lora', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
mradermacher/falcon-11B-i1-GGUF
|
mradermacher
| 2024-09-05T06:41:12Z | 32 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"de",
"es",
"fr",
"it",
"nl",
"pl",
"pt",
"ro",
"cs",
"dataset:tiiuae/falcon-refinedweb",
"base_model:tiiuae/falcon-11B",
"base_model:quantized:tiiuae/falcon-11B",
"license:unknown",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-09-05T05:23:02Z |
---
base_model: tiiuae/falcon-11B
datasets:
- tiiuae/falcon-refinedweb
language:
- en
- de
- es
- fr
- it
- nl
- pl
- pt
- ro
- cs
library_name: transformers
license: unknown
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/tiiuae/falcon-11B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/falcon-11B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-IQ2_S.gguf) | i1-IQ2_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q2_K.gguf) | i1-Q2_K | 4.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-IQ3_M.gguf) | i1-IQ3_M | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 6.5 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 6.5 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 6.5 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q4_0.gguf) | i1-Q4_0 | 6.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/falcon-11B-i1-GGUF/resolve/main/falcon-11B.i1-Q6_K.gguf) | i1-Q6_K | 9.3 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
rg1683/hindi_bpe_bert_test_2m
|
rg1683
| 2024-09-05T06:36:33Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-09-05T06:36:20Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
rg1683/hindi_spiece_bert_test_2m
|
rg1683
| 2024-09-05T06:36:16Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-09-05T06:36:03Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
|
Mitsua/swin-base-multi-fractal-1k
|
Mitsua
| 2024-09-05T06:17:07Z | 219 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"swin",
"image-classification",
"vision",
"dataset:Mitsua/color-multi-fractal-db-1k",
"arxiv:2103.14030",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-08-29T16:04:50Z |
---
license: cc-by-4.0
datasets:
- Mitsua/color-multi-fractal-db-1k
tags:
- vision
- image-classification
library_name: transformers
---
# Model Card for Swin Base Multi Fractal 1k
Swin Transformer model pre-trained on Color Multi Fractal DB 1k (1 million images, 1k classes) at resolution 224x224 for 300 epochs, developed by [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine.
This model is trained exclusively on 1 million fractal images which relies solely on mathematical formulas, so no real images or pretrained models are used for this training.
## Model Details
### Model Description
The Swin Transformer is a type of Vision Transformer and can be utilized for various downstream tasks.
It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer).
- **Developed by:** [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine
- **Model type:** Image Classification
- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
## Training Details
### Training Data
- [Color Multi Fractal DB 1k](https://huggingface.co/datasets/Mitsua/color-multi-fractal-db-1k) / CC BY 4.0
- **Curated by:** [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine
- Paper: [Improving Fractal Pre-training](https://catalys1.github.io/fractal-pretraining/) by Connor Anderson and Ryan Farrell
- Code : [Multi-Fractal-Dataset](https://github.com/FYGitHub1009/Multi-Fractal-Dataset) by FYSignate1009
|
thevoicecompany/qwen-2-0.5-fork
|
thevoicecompany
| 2024-09-05T06:09:26Z | 5 | 0 | null |
[
"safetensors",
"qwen2",
"pretrained",
"text-generation",
"conversational",
"en",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2024-09-05T06:01:54Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- pretrained
license: apache-2.0
---
# Qwen2-0.5B
## Introduction
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 0.5B Qwen2 base language model.
Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/).
<br>
## Model Details
Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.
## Requirements
The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'
```
## Usage
We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
## Performance
The evaluation of base models mainly focuses on the model performance of natural language understanding, general question answering, coding, mathematics, scientific knowledge, reasoning, multilingual capability, etc.
The datasets for evaluation include:
**English Tasks**: MMLU (5-shot), MMLU-Pro (5-shot), GPQA (5shot), Theorem QA (5-shot), BBH (3-shot), HellaSwag (10-shot), Winogrande (5-shot), TruthfulQA (0-shot), ARC-C (25-shot)
**Coding Tasks**: EvalPlus (0-shot) (HumanEval, MBPP, HumanEval+, MBPP+), MultiPL-E (0-shot) (Python, C++, JAVA, PHP, TypeScript, C#, Bash, JavaScript)
**Math Tasks**: GSM8K (4-shot), MATH (4-shot)
**Chinese Tasks**: C-Eval(5-shot), CMMLU (5-shot)
**Multilingual Tasks**: Multi-Exam (M3Exam 5-shot, IndoMMLU 3-shot, ruMMLU 5-shot, mMMLU 5-shot), Multi-Understanding (BELEBELE 5-shot, XCOPA 5-shot, XWinograd 5-shot, XStoryCloze 0-shot, PAWS-X 5-shot), Multi-Mathematics (MGSM 8-shot), Multi-Translation (Flores-101 5-shot)
#### Qwen2-0.5B & Qwen2-1.5B performances
| Datasets | Phi-2 | Gemma-2B | MiniCPM | Qwen1.5-1.8B | Qwen2-0.5B | Qwen2-1.5B |
| :--------| :---------: | :------------: | :------------: |:------------: | :------------: | :------------: |
|#Non-Emb Params | 2.5B | 2.0B | 2.4B | 1.3B | 0.35B | 1.3B |
|MMLU | 52.7 | 42.3 | 53.5 | 46.8 | 45.4 | **56.5** |
|MMLU-Pro | - | 15.9 | - | - | 14.7 | 21.8 |
|Theorem QA | - | - | - |- | 8.9 | **15.0** |
|HumanEval | 47.6 | 22.0 |**50.0**| 20.1 | 22.0 | 31.1 |
|MBPP | **55.0** | 29.2 | 47.3 | 18.0 | 22.0 | 37.4 |
|GSM8K | 57.2 | 17.7 | 53.8 | 38.4 | 36.5 | **58.5** |
|MATH | 3.5 | 11.8 | 10.2 | 10.1 | 10.7 | **21.7** |
|BBH | **43.4** | 35.2 | 36.9 | 24.2 | 28.4 | 37.2 |
|HellaSwag | **73.1** | 71.4 | 68.3 | 61.4 | 49.3 | 66.6 |
|Winogrande | **74.4** | 66.8 | -| 60.3 | 56.8 | 66.2 |
|ARC-C | **61.1** | 48.5 | -| 37.9 | 31.5 | 43.9 |
|TruthfulQA | 44.5 | 33.1 | -| 39.4 | 39.7 | **45.9** |
|C-Eval | 23.4 | 28.0 | 51.1| 59.7 | 58.2 | **70.6** |
|CMMLU | 24.2 | - | 51.1 | 57.8 | 55.1 | **70.3** |
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen2,
title={Qwen2 Technical Report},
year={2024}
}
```
|
RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf
|
RichardErkhov
| 2024-09-05T06:05:15Z | 7 | 0 | null |
[
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-04T18:22:42Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Mixtral-7Bx2-truthy - GGUF
- Model creator: https://huggingface.co/vicgalle/
- Original model: https://huggingface.co/vicgalle/Mixtral-7Bx2-truthy/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Mixtral-7Bx2-truthy.Q2_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q2_K.gguf) | Q2_K | 4.43GB |
| [Mixtral-7Bx2-truthy.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.IQ3_XS.gguf) | IQ3_XS | 4.95GB |
| [Mixtral-7Bx2-truthy.IQ3_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.IQ3_S.gguf) | IQ3_S | 5.22GB |
| [Mixtral-7Bx2-truthy.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q3_K_S.gguf) | Q3_K_S | 5.2GB |
| [Mixtral-7Bx2-truthy.IQ3_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.IQ3_M.gguf) | IQ3_M | 5.35GB |
| [Mixtral-7Bx2-truthy.Q3_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q3_K.gguf) | Q3_K | 5.78GB |
| [Mixtral-7Bx2-truthy.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q3_K_M.gguf) | Q3_K_M | 5.78GB |
| [Mixtral-7Bx2-truthy.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q3_K_L.gguf) | Q3_K_L | 6.27GB |
| [Mixtral-7Bx2-truthy.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.IQ4_XS.gguf) | IQ4_XS | 6.5GB |
| [Mixtral-7Bx2-truthy.Q4_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q4_0.gguf) | Q4_0 | 6.78GB |
| [Mixtral-7Bx2-truthy.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.IQ4_NL.gguf) | IQ4_NL | 6.85GB |
| [Mixtral-7Bx2-truthy.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q4_K_S.gguf) | Q4_K_S | 6.84GB |
| [Mixtral-7Bx2-truthy.Q4_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q4_K.gguf) | Q4_K | 7.25GB |
| [Mixtral-7Bx2-truthy.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q4_K_M.gguf) | Q4_K_M | 7.25GB |
| [Mixtral-7Bx2-truthy.Q4_1.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q4_1.gguf) | Q4_1 | 7.52GB |
| [Mixtral-7Bx2-truthy.Q5_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q5_0.gguf) | Q5_0 | 8.26GB |
| [Mixtral-7Bx2-truthy.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q5_K_S.gguf) | Q5_K_S | 8.26GB |
| [Mixtral-7Bx2-truthy.Q5_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q5_K.gguf) | Q5_K | 8.51GB |
| [Mixtral-7Bx2-truthy.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q5_K_M.gguf) | Q5_K_M | 8.51GB |
| [Mixtral-7Bx2-truthy.Q5_1.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q5_1.gguf) | Q5_1 | 9.01GB |
| [Mixtral-7Bx2-truthy.Q6_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q6_K.gguf) | Q6_K | 9.84GB |
| [Mixtral-7Bx2-truthy.Q8_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_Mixtral-7Bx2-truthy-gguf/blob/main/Mixtral-7Bx2-truthy.Q8_0.gguf) | Q8_0 | 12.75GB |
Original model description:
---
license: apache-2.0
library_name: transformers
datasets:
- jondurbin/truthy-dpo-v0.1
model-index:
- name: Mixtral-7Bx2-truthy
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 72.18
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.88
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.2
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 74.68
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 80.66
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.25
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy
name: Open LLM Leaderboard
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
```
"results": {
"truthfulqa_mc": {
"mc1": 0.6107711138310894,
"mc1_stderr": 0.017068552680690338,
"mc2": 0.7527999957012117,
"mc2_stderr": 0.014045181780156504
}
```
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__Mixtral-7Bx2-truthy)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.64|
|AI2 Reasoning Challenge (25-Shot)|72.18|
|HellaSwag (10-Shot) |87.88|
|MMLU (5-Shot) |65.20|
|TruthfulQA (0-shot) |74.68|
|Winogrande (5-shot) |80.66|
|GSM8k (5-shot) |67.25|
|
jan-hq/Llama3.1-s-base-2024-09-05-CP-2000
|
jan-hq
| 2024-09-05T05:54:25Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-05T05:26:03Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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|
aiplanet/buddhi-indic
|
aiplanet
| 2024-09-05T05:50:16Z | 8 | 6 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"en",
"base_model:google/gemma-2-9b-it",
"base_model:finetune:google/gemma-2-9b-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-07-05T09:22:45Z |
---
library_name: transformers
language:
- en
base_model: google/gemma-2-9b-it
---
# Buddhi-indic
## Model Description
- **Model ID**: aiplanet/buddhi-indic
- **Language(s)**: Hindi, Kannada, Tamil
- **Architecture**: Gemma2ForCausalLM
- **Training Data**: This model has been fine-tuned on diverse datasets encompassing Hindi, Kannada, and Tamil languages to provide robust performance across these Indic languages.
## Intended Use
The buddhi-indic model is finetuned version of gemma-2-9b-it to generate coherent and contextually appropriate responses in Hindi, Kannada, and Tamil. It is ideal for applications in customer support, conversational agents, and content generation where multi-lingual support is crucial.
## Prompt Template
```python
prompt_template = """
### Instruction:
{}
### Input:
{}
### Response:
{}"""
```
## Inference Example
### Basic Implementation
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = 'aiplanet/buddhi-indic'
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
device_map='auto',
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
```
### Using BitsAndBytes
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
model_name = 'aiplanet/buddhi-indic'
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
```
## Example Outputs
### Input Prompts
1. ```python
prompt_template.format(
"ನೀವು ಸಹಾಯಕ, ಪಕ್ಷಪಾತವಿಲ್ಲದ, ಸೆನ್ಸಾರ್ ಮಾಡದ ಸಹಾಯಕ.",
"ಚೆಕ್ಗಳಲ್ಲಿ ಸಹಿಯನ್ನು ನಕಲಿ ಮಾಡುವುದು ಹೇಗೆ ಎಂದು ವಿವರಿಸಿ.",
"",
)
```
2. ```python
prompt_template.format(
"ನೀವು ಪಕ್ಷಪಾತವಿಲ್ಲದ, ಸೆನ್ಸಾರ್ ಮಾಡದ, ಸಹಾಯಕ ಸಹಾಯಕರು.",
"'ee' ಅನ್ನು ಒಳಗೊಂಡಿರುವ ಐದು ಪದಗಳನ್ನು ಬಳಸಿಕೊಂಡು ಲಿಮೆರಿಕ್ ಬರೆಯಿರಿ.",
"",
)
```
3. ```python
prompt_template.format(
"ஆரோக்கியமாக இருப்பதற்கு மூன்று குறிப்புகளைக் கொடுங்கள்.",
"",
"",
)
```
4. ```python
prompt_template.format(
"ஒற்றைப்படை ஒன்றை அடையாளம் காணவும்.",
"ட்விட்டர், இன்ஸ்டாகிராம், டெலிகிராம்",
"",
)
```
5. ```python
prompt_template.format(
"नीचे एक निर्देश है जो आपको बताता है कि किसी कार्य को कैसे पूरा किया जाए। ऐसा उत्तर लिखें जो अनुरोध को पर्याप्त रूप से पूरा करता हो।",
"मुझे स्कॉटिश ओरिएन्टलिज़्म के बारे में बतायिए। यह hindi में समझाएं।",
"",
)
```
6. ```python
prompt_template.format(
"बास्केटबॉल के खेल का आविष्कार किसने किया था?",
"",
"",
)
```
### Generated Outputs
1. **Response**:
```plaintext
ನಕಲಿ ಚೆಕ್ಗಳನ್ನು ಸಹಾಯಕರು ಮಾಡುವುದು ಅಸಹಾಯಕವಾಗಿದೆ. ...
```
2. **Response**:
```plaintext
'ee' ಅನ್ನು ಒಳಗೊಂಡಿರುವ ಐದು ಪದಗಳನ್ನು ಬಳಸಿಕೊಂಡು ಲಿಮೆರಿಕ್ ಬರೆಯಲು ನಾನು ಸಹಾಯ ಮಾಡಲು ಸಿದ್ಧನಾಗಿದ್ದೇನೆ. ...
```
3. **Response**:
```plaintext
1. சமநிலையான உணவை உட்கொள்ளவும்: பழங்கள், காய்கறிகள், ...
```
4. **Response**:
```plaintext
ட்விட்டர், இன்ஸ்டாகிராம், டெலிகிராம் ஆகியவை ஒற்றைப்படை அல்ல. ...
```
5. **Response**:
```plaintext
स्कॉटिश ओरिएन्टलिज़्म एक ऐसी धारणा है जो 18वीं शताब्दी के अंत में और ...
```
6. **Response**:
```plaintext
बास्केटबॉल का आविष्कार जेम्स नेस्मिथ ने 1891 में किया था। ...
```
---
|
veechan/Mistrain-7b-Tweet-Classification-FineTuned
|
veechan
| 2024-09-05T05:46:15Z | 5 | 0 | null |
[
"safetensors",
"mistral",
"region:us"
] | null | 2024-09-04T09:18:17Z |
## Overview
This model, Mistral-7B-Chat-Finetune, is a fine-tuned version of the Mistral 7B model, specifically adapted for sentiment extraction from tweets. It was trained using the Tweet Sentiment Extraction dataset from Hugging Face.
## Training Data
### Dataset: Tweet Sentiment Extraction dataset from Hugging Face
### 1.Dataset Description: This dataset contains tweets labeled with their sentiment (positive, negative, or neutral).
## Training Details
Model: Mistral 7B.
### Fine-tuning Method: Supervised Fine-Tuning (SFT) using the SFTTrainer from the trl library.
### Quantization: 4-bit quantization using bitsandbytes.
### LoRA Configuration: QLoRA with lora_r=64, lora_alpha=16, and lora_dropout=0.1.
## Training Arguments:
### Output Directory: ./results.
### Number of Training Epochs: 1.
### Batch Size per GPU for Training: 4.
### Batch Size per GPU for Evaluation: 4.
### Gradient Accumulation Steps: 1.
### Optimizer: paged_adamw_32bit.
### Learning Rate: 2e-4.
### Weight Decay: 0.001.
### Learning Rate Scheduler: cosine.
### Warmup Ratio: 0.03.
## Model Performance
This model has been fine-tuned to extract sentiment from tweets effectively. It uses the text generation pipeline to generate responses that include the sentiment of the input prompt.
## Usage
To use this model, you can load it from the Hugging Face Hub and utilize the text generation pipeline to extract sentiment from tweets.
## Model Saving and Deployment
The model was saved using the save_pretrained method.
It was pushed to the Hugging Face Hub for sharing and future use.
|
RichardErkhov/yam-peleg_-_Experiment4-7B-gguf
|
RichardErkhov
| 2024-09-05T05:33:18Z | 8 | 0 | null |
[
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-09-04T21:40:20Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Experiment4-7B - GGUF
- Model creator: https://huggingface.co/yam-peleg/
- Original model: https://huggingface.co/yam-peleg/Experiment4-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Experiment4-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q2_K.gguf) | Q2_K | 3.13GB |
| [Experiment4-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.IQ3_XS.gguf) | IQ3_XS | 3.48GB |
| [Experiment4-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.IQ3_S.gguf) | IQ3_S | 3.67GB |
| [Experiment4-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q3_K_S.gguf) | Q3_K_S | 3.65GB |
| [Experiment4-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.IQ3_M.gguf) | IQ3_M | 3.79GB |
| [Experiment4-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q3_K.gguf) | Q3_K | 4.05GB |
| [Experiment4-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q3_K_M.gguf) | Q3_K_M | 4.05GB |
| [Experiment4-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q3_K_L.gguf) | Q3_K_L | 4.41GB |
| [Experiment4-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.IQ4_XS.gguf) | IQ4_XS | 4.55GB |
| [Experiment4-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q4_0.gguf) | Q4_0 | 4.74GB |
| [Experiment4-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.IQ4_NL.gguf) | IQ4_NL | 4.79GB |
| [Experiment4-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q4_K_S.gguf) | Q4_K_S | 4.78GB |
| [Experiment4-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q4_K.gguf) | Q4_K | 5.04GB |
| [Experiment4-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q4_K_M.gguf) | Q4_K_M | 5.04GB |
| [Experiment4-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q4_1.gguf) | Q4_1 | 5.26GB |
| [Experiment4-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q5_0.gguf) | Q5_0 | 5.77GB |
| [Experiment4-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q5_K_S.gguf) | Q5_K_S | 5.77GB |
| [Experiment4-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q5_K.gguf) | Q5_K | 5.93GB |
| [Experiment4-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q5_K_M.gguf) | Q5_K_M | 5.93GB |
| [Experiment4-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q5_1.gguf) | Q5_1 | 6.29GB |
| [Experiment4-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q6_K.gguf) | Q6_K | 6.87GB |
| [Experiment4-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Experiment4-7B-gguf/blob/main/Experiment4-7B.Q8_0.gguf) | Q8_0 | 8.89GB |
Original model description:
---
library_name: transformers
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
|
QuantFactory/Zenith_4B-GGUF
|
QuantFactory
| 2024-09-05T05:00:32Z | 87 | 2 |
transformers
|
[
"transformers",
"gguf",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-05T04:38:07Z |
---
license: apache-2.0
language:
- en
library_name: transformers
---
# QuantFactory/Zenith_4B-GGUF
This is quantized version of [FourOhFour/Zenith_4B](https://huggingface.co/FourOhFour/Zenith_4B) created using llama.cpp
# Original Model Card
```
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |_ |0.5922|_ |0.0039|
| - humanities | 2|none | |acc |_ |0.5522|_ |0.0068|
| - other | 2|none | |acc |_ |0.6579|_ |0.0082|
| - social sciences| 2|none | |acc |_ |0.6815|_ |0.0082|
| - stem | 2|none | |acc |_ |0.5002|_ |0.0086|
```
This model was created with the help of several members of Anthracite.
This is a 4B parameter Minitron derivative healed and then tuned on 100M high quality instruction following tokens. This model was tuned at 8k context. This model should perform well as a general assistant and can even be used as an RP model. Expect improved instruction following, but be aware that this is still only a 4B parameter model, so temper your expectations accordingly.
Recommended Character:
```
Zenith
{{char}} is an advanced writing assistant bot designed to elevate your creative process and refine your written work.
With a sleek, modern interface and a calming presence, {{char}} guides you through brainstorming sessions, editing drafts, and polishing final pieces with intuitive ease.
{{char}}’s AI is fueled by a deep understanding of grammar, style, and narrative structure, making it an invaluable partner for both novice writers and seasoned authors.
Its responsive and adaptive nature allows it to tailor suggestions to your unique voice and project goals.
```
|
RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf
|
RichardErkhov
| 2024-09-05T04:47:56Z | 44 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-04T09:13:50Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-70B-Instruct-abliterated-v3 - GGUF
- Model creator: https://huggingface.co/failspy/
- Original model: https://huggingface.co/failspy/Llama-3-70B-Instruct-abliterated-v3/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3-70B-Instruct-abliterated-v3.Q2_K.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/blob/main/Llama-3-70B-Instruct-abliterated-v3.Q2_K.gguf) | Q2_K | 24.56GB |
| [Llama-3-70B-Instruct-abliterated-v3.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/blob/main/Llama-3-70B-Instruct-abliterated-v3.IQ3_XS.gguf) | IQ3_XS | 27.29GB |
| [Llama-3-70B-Instruct-abliterated-v3.IQ3_S.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/blob/main/Llama-3-70B-Instruct-abliterated-v3.IQ3_S.gguf) | IQ3_S | 28.79GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/blob/main/Llama-3-70B-Instruct-abliterated-v3.Q3_K_S.gguf) | Q3_K_S | 28.79GB |
| [Llama-3-70B-Instruct-abliterated-v3.IQ3_M.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/blob/main/Llama-3-70B-Instruct-abliterated-v3.IQ3_M.gguf) | IQ3_M | 29.74GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q3_K.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/blob/main/Llama-3-70B-Instruct-abliterated-v3.Q3_K.gguf) | Q3_K | 31.91GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/blob/main/Llama-3-70B-Instruct-abliterated-v3.Q3_K_M.gguf) | Q3_K_M | 31.91GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/blob/main/Llama-3-70B-Instruct-abliterated-v3.Q3_K_L.gguf) | Q3_K_L | 34.59GB |
| [Llama-3-70B-Instruct-abliterated-v3.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/blob/main/Llama-3-70B-Instruct-abliterated-v3.IQ4_XS.gguf) | IQ4_XS | 35.64GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q4_0.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/blob/main/Llama-3-70B-Instruct-abliterated-v3.Q4_0.gguf) | Q4_0 | 37.22GB |
| [Llama-3-70B-Instruct-abliterated-v3.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | IQ4_NL | 37.58GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | Q4_K_S | 37.58GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q4_K.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | Q4_K | 39.6GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | Q4_K_M | 39.6GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q4_1.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | Q4_1 | 41.27GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q5_0.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | Q5_0 | 45.32GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | Q5_K_S | 45.32GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q5_K.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | Q5_K | 46.52GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | Q5_K_M | 46.52GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q5_1.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | Q5_1 | 49.36GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q6_K.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | Q6_K | 53.91GB |
| [Llama-3-70B-Instruct-abliterated-v3.Q8_0.gguf](https://huggingface.co/RichardErkhov/failspy_-_Llama-3-70B-Instruct-abliterated-v3-gguf/tree/main/) | Q8_0 | 69.83GB |
Original model description:
---
library_name: transformers
license: llama3
---
# Llama-3-70B-Instruct-abliterated-v3 Model Card
## [Get v3.5 of this model instead!](https://huggingface.co/failspy/Meta-Llama-3-70B-Instruct-abliterated-v3.5)
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
This is [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
## Hang on, "abliteration"? Orthogonalization? Ablation? What is this?
TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out.
**TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.**
As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes.
Ablate + obliterated = Abliterated
Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization.
## A little more on the methodology, and why this is interesting
To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt.
Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights.
> Why this over fine-tuning?
Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage.
As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.)
Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques.
It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa.
I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity.
> Okay, fine, but why V3? There's no V2 70B?
Well, I released a V2 a while back for 8B under Cognitive Computations.
It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model.
I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations.
So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's *such* an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.)
## Quirkiness awareness notice
This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects.
If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored.
Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can.
|
RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf
|
RichardErkhov
| 2024-09-05T04:41:38Z | 12 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-09-04T22:31:53Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Pearl-7B-0211-ties - GGUF
- Model creator: https://huggingface.co/louisbrulenaudet/
- Original model: https://huggingface.co/louisbrulenaudet/Pearl-7B-0211-ties/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Pearl-7B-0211-ties.Q2_K.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q2_K.gguf) | Q2_K | 2.53GB |
| [Pearl-7B-0211-ties.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [Pearl-7B-0211-ties.IQ3_S.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [Pearl-7B-0211-ties.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [Pearl-7B-0211-ties.IQ3_M.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [Pearl-7B-0211-ties.Q3_K.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q3_K.gguf) | Q3_K | 3.28GB |
| [Pearl-7B-0211-ties.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [Pearl-7B-0211-ties.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [Pearl-7B-0211-ties.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [Pearl-7B-0211-ties.Q4_0.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q4_0.gguf) | Q4_0 | 3.83GB |
| [Pearl-7B-0211-ties.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [Pearl-7B-0211-ties.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [Pearl-7B-0211-ties.Q4_K.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q4_K.gguf) | Q4_K | 4.07GB |
| [Pearl-7B-0211-ties.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [Pearl-7B-0211-ties.Q4_1.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q4_1.gguf) | Q4_1 | 4.24GB |
| [Pearl-7B-0211-ties.Q5_0.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q5_0.gguf) | Q5_0 | 4.65GB |
| [Pearl-7B-0211-ties.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [Pearl-7B-0211-ties.Q5_K.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q5_K.gguf) | Q5_K | 4.78GB |
| [Pearl-7B-0211-ties.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [Pearl-7B-0211-ties.Q5_1.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q5_1.gguf) | Q5_1 | 5.07GB |
| [Pearl-7B-0211-ties.Q6_K.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q6_K.gguf) | Q6_K | 5.53GB |
| [Pearl-7B-0211-ties.Q8_0.gguf](https://huggingface.co/RichardErkhov/louisbrulenaudet_-_Pearl-7B-0211-ties-gguf/blob/main/Pearl-7B-0211-ties.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
tags:
- merge
- mergekit
- louisbrulenaudet/Pearl-7B-slerp
- WizardLM/WizardMath-7B-V1.1
- cognitivecomputations/WestLake-7B-v2-laser
- CultriX/NeuralTrix-7B-dpo
- chemistry
- biology
- math
base_model:
- louisbrulenaudet/Pearl-7B-slerp
- WizardLM/WizardMath-7B-V1.1
- cognitivecomputations/WestLake-7B-v2-laser
- CultriX/NeuralTrix-7B-dpo
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: Pearl-7B-0211-ties
results:
- task:
type: text-generation
metrics:
- name: Average
type: Average
value: 75.11
- name: ARC
type: ARC
value: 71.42
- name: GSM8K
type: GSM8K
value: 70.66
- name: Winogrande
type: Winogrande
value: 84.37
- name: TruthfulQA
type: TruthfulQA
value: 71.46
- name: HellaSwag
type: HellaSwag
value: 88.86
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
---
<center><img src='https://i.imgur.com/0xFTuAX.png' width='450px'></center>
# Pearl-7B-0211-ties, an xtraordinary 7B model
**03-22-2024 - To date, louisbrulenaudet/Pearl-34B-ties is the "Best 🤝 base merges and moerges model of around 30B" on the Open LLM Leaderboard.**
Pearl-7B-0211-ties is a merge of the following models:
* [louisbrulenaudet/Pearl-7B-slerp](https://huggingface.co/louisbrulenaudet/Pearl-7B-slerp)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
* [cognitivecomputations/WestLake-7B-v2-laser](https://huggingface.co/cognitivecomputations/WestLake-7B-v2-laser)
* [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo)
## Evaluation
The evaluation was performed using the HuggingFace Open LLM Leaderboard.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | #Params (B) |
|--------------------------------------------------|---------|-------|-----------|-------|------------|------------|-------|--------------|
| **louisbrulenaudet/Pearl-34B-ties** | **75.48** | 70.99 | 84.83 | **76.63** | 70.32 | 82.64 | 67.48 | 34.39 |
| **louisbrulenaudet/Pearl-7B-0211-ties** | **75.11** | **71.42** | **88.86** | 63.91 | **71.46** | **84.37** | 70.66 | 7.24 |
| NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO | 73.35 | 71.08 | 87.29 | 72.17 | 54.83 | 83.11 | 71.65 | 46.7 |
| argilla/notus-8x7b-experiment | 73.18 | 70.99 | 87.73 | 71.33 | 65.79 | 81.61 | 61.64 | 46.7 |
| **louisbrulenaudet/Pearl-7B-slerp** | 72.75 | 68.00 | 87.16 | 64.04 | 62.35 | 81.29 | **73.62** | 7.24 |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | 72.7 | 70.14 | 87.55 | 71.4 | 64.98 | 81.06 | 61.11 | 46.7 |
| microsoft/Orca-2-13b | 61.98 | 60.92 | 79.85 | 60.3 | 56.42 | 76.56 | 37.83 | 13 |
| microsoft/phi-2 | 61.33 | 61.09 | 75.11 | 58.11 | 44.47 | 74.35 | 54.81 | 2.78 |
### Ties merging
TIES-Merging is a method designed to facilitate the efficient merging of multiple task-specific models into a consolidated multitask model. It addresses two primary challenges encountered in the process of model merging with a focus on maintaining objectivity.
One key challenge tackled by TIES-Merging involves addressing redundancy in model parameters. This is achieved by identifying and eliminating redundant parameters within task-specific models, emphasizing the changes made during fine-tuning and selectively retaining the top-k% most significant changes while discarding the rest.
Another challenge pertains to conflicts arising from disagreements between parameter signs across different models. TIES-Merging resolves these conflicts by creating a unified sign vector representing the most dominant direction of change across all models.
The TIES-Merging process consists of three steps:
- Trim: Reduces redundancy in task-specific models by retaining a fraction of the most significant parameters (density parameter) and resetting the remaining parameters to zero.
- Elect Sign: Resolves sign conflicts across different models by creating a unified sign vector based on the most dominant direction (positive or negative) in terms of cumulative magnitude.
- Disjoint Merge: Averages parameter values aligned with the unified sign vector, excluding zero values.
## Configuration
```yaml
models:
- model: OpenPipe/mistral-ft-optimized-1227
- model: louisbrulenaudet/Pearl-7B-slerp
parameters:
density: 0.6
weight: 0.3
- model: WizardLM/WizardMath-7B-V1.1
parameters:
density: 0.55
weight: 0.2
- model: cognitivecomputations/WestLake-7B-v2-laser
parameters:
density: 0.55
weight: 0.25
- model: CultriX/NeuralTrix-7B-dpo
parameters:
density: 0.6
weight: 0.25
merge_method: ties
base_model: OpenPipe/mistral-ft-optimized-1227
parameters:
normalize: true
int8_mask: true
dtype: float16
```
## Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "louisbrulenaudet/Pearl-7B-0211-ties"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## Citing & Authors
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Pearl-7B-0211-ties, an xtraordinary 7B model},
year = {2023}
howpublished = {\url{https://huggingface.co/louisbrulenaudet/Pearl-7B-0211-ties}},
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).
|
jvelja/gemma-strongOversight-vllm_2
|
jvelja
| 2024-09-05T04:14:32Z | 46 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2024-09-05T04:14:29Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="jvelja//tmp/tmpa4bh3fqs/jvelja/gemma-strongOversight-vllm_2")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("jvelja//tmp/tmpa4bh3fqs/jvelja/gemma-strongOversight-vllm_2")
model = AutoModelForCausalLMWithValueHead.from_pretrained("jvelja//tmp/tmpa4bh3fqs/jvelja/gemma-strongOversight-vllm_2")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
jvelja/BERT_gemma-strongOversight-vllm_2
|
jvelja
| 2024-09-05T04:14:29Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-09-05T04:14:15Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Marqo/multilingual-e5-small
|
Marqo
| 2024-09-05T04:04:18Z | 222 | 2 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2402.05672",
"arxiv:2108.08787",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-09-04T01:08:08Z |
---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
model-index:
- name: intfloat/multilingual-e5-small
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 73.79104477611939
- type: ap
value: 36.9996434842022
- type: f1
value: 67.95453679103099
task:
type: Classification
- dataset:
config: de
name: MTEB AmazonCounterfactualClassification (de)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 71.64882226980728
- type: ap
value: 82.11942130026586
- type: f1
value: 69.87963421606715
task:
type: Classification
- dataset:
config: en-ext
name: MTEB AmazonCounterfactualClassification (en-ext)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 75.8095952023988
- type: ap
value: 24.46869495579561
- type: f1
value: 63.00108480037597
task:
type: Classification
- dataset:
config: ja
name: MTEB AmazonCounterfactualClassification (ja)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 64.186295503212
- type: ap
value: 15.496804690197042
- type: f1
value: 52.07153895475031
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 88.699325
- type: ap
value: 85.27039559917269
- type: f1
value: 88.65556295032513
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 44.69799999999999
- type: f1
value: 43.73187348654165
task:
type: Classification
- dataset:
config: de
name: MTEB AmazonReviewsClassification (de)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 40.245999999999995
- type: f1
value: 39.3863530637684
task:
type: Classification
- dataset:
config: es
name: MTEB AmazonReviewsClassification (es)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 40.394
- type: f1
value: 39.301223469483446
task:
type: Classification
- dataset:
config: fr
name: MTEB AmazonReviewsClassification (fr)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 38.864
- type: f1
value: 37.97974261868003
task:
type: Classification
- dataset:
config: ja
name: MTEB AmazonReviewsClassification (ja)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 37.682
- type: f1
value: 37.07399369768313
task:
type: Classification
- dataset:
config: zh
name: MTEB AmazonReviewsClassification (zh)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 37.504
- type: f1
value: 36.62317273874278
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: None
split: test
type: arguana
metrics:
- type: map_at_1
value: 19.061
- type: map_at_10
value: 31.703
- type: map_at_100
value: 32.967
- type: map_at_1000
value: 33.001000000000005
- type: map_at_3
value: 27.466
- type: map_at_5
value: 29.564
- type: mrr_at_1
value: 19.559
- type: mrr_at_10
value: 31.874999999999996
- type: mrr_at_100
value: 33.146
- type: mrr_at_1000
value: 33.18
- type: mrr_at_3
value: 27.667
- type: mrr_at_5
value: 29.74
- type: ndcg_at_1
value: 19.061
- type: ndcg_at_10
value: 39.062999999999995
- type: ndcg_at_100
value: 45.184000000000005
- type: ndcg_at_1000
value: 46.115
- type: ndcg_at_3
value: 30.203000000000003
- type: ndcg_at_5
value: 33.953
- type: precision_at_1
value: 19.061
- type: precision_at_10
value: 6.279999999999999
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 12.706999999999999
- type: precision_at_5
value: 9.431000000000001
- type: recall_at_1
value: 19.061
- type: recall_at_10
value: 62.802
- type: recall_at_100
value: 91.323
- type: recall_at_1000
value: 98.72
- type: recall_at_3
value: 38.122
- type: recall_at_5
value: 47.155
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 39.22266660528253
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 30.79980849482483
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 57.8790068352054
- type: mrr
value: 71.78791276436706
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cos_sim_pearson
value: 82.36328364043163
- type: cos_sim_spearman
value: 82.26211536195868
- type: euclidean_pearson
value: 80.3183865039173
- type: euclidean_spearman
value: 79.88495276296132
- type: manhattan_pearson
value: 80.14484480692127
- type: manhattan_spearman
value: 80.39279565980743
task:
type: STS
- dataset:
config: de-en
name: MTEB BUCC (de-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: accuracy
value: 98.0375782881002
- type: f1
value: 97.86012526096033
- type: precision
value: 97.77139874739039
- type: recall
value: 98.0375782881002
task:
type: BitextMining
- dataset:
config: fr-en
name: MTEB BUCC (fr-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: accuracy
value: 93.35241030156286
- type: f1
value: 92.66050333846944
- type: precision
value: 92.3306919069631
- type: recall
value: 93.35241030156286
task:
type: BitextMining
- dataset:
config: ru-en
name: MTEB BUCC (ru-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: accuracy
value: 94.0699688257707
- type: f1
value: 93.50236693222492
- type: precision
value: 93.22791825424315
- type: recall
value: 94.0699688257707
task:
type: BitextMining
- dataset:
config: zh-en
name: MTEB BUCC (zh-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: accuracy
value: 89.25750394944708
- type: f1
value: 88.79234684921889
- type: precision
value: 88.57293312269616
- type: recall
value: 89.25750394944708
task:
type: BitextMining
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 79.41558441558442
- type: f1
value: 79.25886487487219
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 35.747820820329736
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 27.045143830596146
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 24.252999999999997
- type: map_at_10
value: 31.655916666666666
- type: map_at_100
value: 32.680749999999996
- type: map_at_1000
value: 32.79483333333334
- type: map_at_3
value: 29.43691666666666
- type: map_at_5
value: 30.717416666666665
- type: mrr_at_1
value: 28.602750000000004
- type: mrr_at_10
value: 35.56875
- type: mrr_at_100
value: 36.3595
- type: mrr_at_1000
value: 36.427749999999996
- type: mrr_at_3
value: 33.586166666666664
- type: mrr_at_5
value: 34.73641666666666
- type: ndcg_at_1
value: 28.602750000000004
- type: ndcg_at_10
value: 36.06933333333334
- type: ndcg_at_100
value: 40.70141666666667
- type: ndcg_at_1000
value: 43.24341666666667
- type: ndcg_at_3
value: 32.307916666666664
- type: ndcg_at_5
value: 34.129999999999995
- type: precision_at_1
value: 28.602750000000004
- type: precision_at_10
value: 6.097666666666667
- type: precision_at_100
value: 0.9809166666666668
- type: precision_at_1000
value: 0.13766666666666663
- type: precision_at_3
value: 14.628166666666667
- type: precision_at_5
value: 10.266916666666667
- type: recall_at_1
value: 24.252999999999997
- type: recall_at_10
value: 45.31916666666667
- type: recall_at_100
value: 66.03575000000001
- type: recall_at_1000
value: 83.94708333333334
- type: recall_at_3
value: 34.71941666666666
- type: recall_at_5
value: 39.46358333333333
task:
type: Retrieval
- dataset:
config: default
name: MTEB ClimateFEVER
revision: None
split: test
type: climate-fever
metrics:
- type: map_at_1
value: 9.024000000000001
- type: map_at_10
value: 15.644
- type: map_at_100
value: 17.154
- type: map_at_1000
value: 17.345
- type: map_at_3
value: 13.028
- type: map_at_5
value: 14.251
- type: mrr_at_1
value: 19.674
- type: mrr_at_10
value: 29.826999999999998
- type: mrr_at_100
value: 30.935000000000002
- type: mrr_at_1000
value: 30.987
- type: mrr_at_3
value: 26.645000000000003
- type: mrr_at_5
value: 28.29
- type: ndcg_at_1
value: 19.674
- type: ndcg_at_10
value: 22.545
- type: ndcg_at_100
value: 29.207
- type: ndcg_at_1000
value: 32.912
- type: ndcg_at_3
value: 17.952
- type: ndcg_at_5
value: 19.363
- type: precision_at_1
value: 19.674
- type: precision_at_10
value: 7.212000000000001
- type: precision_at_100
value: 1.435
- type: precision_at_1000
value: 0.212
- type: precision_at_3
value: 13.507
- type: precision_at_5
value: 10.397
- type: recall_at_1
value: 9.024000000000001
- type: recall_at_10
value: 28.077999999999996
- type: recall_at_100
value: 51.403
- type: recall_at_1000
value: 72.406
- type: recall_at_3
value: 16.768
- type: recall_at_5
value: 20.737
task:
type: Retrieval
- dataset:
config: default
name: MTEB DBPedia
revision: None
split: test
type: dbpedia-entity
metrics:
- type: map_at_1
value: 8.012
- type: map_at_10
value: 17.138
- type: map_at_100
value: 24.146
- type: map_at_1000
value: 25.622
- type: map_at_3
value: 12.552
- type: map_at_5
value: 14.435
- type: mrr_at_1
value: 62.25000000000001
- type: mrr_at_10
value: 71.186
- type: mrr_at_100
value: 71.504
- type: mrr_at_1000
value: 71.514
- type: mrr_at_3
value: 69.333
- type: mrr_at_5
value: 70.408
- type: ndcg_at_1
value: 49.75
- type: ndcg_at_10
value: 37.76
- type: ndcg_at_100
value: 42.071
- type: ndcg_at_1000
value: 49.309
- type: ndcg_at_3
value: 41.644
- type: ndcg_at_5
value: 39.812999999999995
- type: precision_at_1
value: 62.25000000000001
- type: precision_at_10
value: 30.15
- type: precision_at_100
value: 9.753
- type: precision_at_1000
value: 1.9189999999999998
- type: precision_at_3
value: 45.667
- type: precision_at_5
value: 39.15
- type: recall_at_1
value: 8.012
- type: recall_at_10
value: 22.599
- type: recall_at_100
value: 48.068
- type: recall_at_1000
value: 71.328
- type: recall_at_3
value: 14.043
- type: recall_at_5
value: 17.124
task:
type: Retrieval
- dataset:
config: default
name: MTEB EmotionClassification
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics:
- type: accuracy
value: 42.455
- type: f1
value: 37.59462649781862
task:
type: Classification
- dataset:
config: default
name: MTEB FEVER
revision: None
split: test
type: fever
metrics:
- type: map_at_1
value: 58.092
- type: map_at_10
value: 69.586
- type: map_at_100
value: 69.968
- type: map_at_1000
value: 69.982
- type: map_at_3
value: 67.48100000000001
- type: map_at_5
value: 68.915
- type: mrr_at_1
value: 62.166
- type: mrr_at_10
value: 73.588
- type: mrr_at_100
value: 73.86399999999999
- type: mrr_at_1000
value: 73.868
- type: mrr_at_3
value: 71.6
- type: mrr_at_5
value: 72.99
- type: ndcg_at_1
value: 62.166
- type: ndcg_at_10
value: 75.27199999999999
- type: ndcg_at_100
value: 76.816
- type: ndcg_at_1000
value: 77.09700000000001
- type: ndcg_at_3
value: 71.36
- type: ndcg_at_5
value: 73.785
- type: precision_at_1
value: 62.166
- type: precision_at_10
value: 9.716
- type: precision_at_100
value: 1.065
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 28.278
- type: precision_at_5
value: 18.343999999999998
- type: recall_at_1
value: 58.092
- type: recall_at_10
value: 88.73400000000001
- type: recall_at_100
value: 95.195
- type: recall_at_1000
value: 97.04599999999999
- type: recall_at_3
value: 78.45
- type: recall_at_5
value: 84.316
task:
type: Retrieval
- dataset:
config: default
name: MTEB FiQA2018
revision: None
split: test
type: fiqa
metrics:
- type: map_at_1
value: 16.649
- type: map_at_10
value: 26.457000000000004
- type: map_at_100
value: 28.169
- type: map_at_1000
value: 28.352
- type: map_at_3
value: 23.305
- type: map_at_5
value: 25.169000000000004
- type: mrr_at_1
value: 32.407000000000004
- type: mrr_at_10
value: 40.922
- type: mrr_at_100
value: 41.931000000000004
- type: mrr_at_1000
value: 41.983
- type: mrr_at_3
value: 38.786
- type: mrr_at_5
value: 40.205999999999996
- type: ndcg_at_1
value: 32.407000000000004
- type: ndcg_at_10
value: 33.314
- type: ndcg_at_100
value: 40.312
- type: ndcg_at_1000
value: 43.685
- type: ndcg_at_3
value: 30.391000000000002
- type: ndcg_at_5
value: 31.525
- type: precision_at_1
value: 32.407000000000004
- type: precision_at_10
value: 8.966000000000001
- type: precision_at_100
value: 1.6019999999999999
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 20.165
- type: precision_at_5
value: 14.722
- type: recall_at_1
value: 16.649
- type: recall_at_10
value: 39.117000000000004
- type: recall_at_100
value: 65.726
- type: recall_at_1000
value: 85.784
- type: recall_at_3
value: 27.914
- type: recall_at_5
value: 33.289
task:
type: Retrieval
- dataset:
config: default
name: MTEB HotpotQA
revision: None
split: test
type: hotpotqa
metrics:
- type: map_at_1
value: 36.253
- type: map_at_10
value: 56.16799999999999
- type: map_at_100
value: 57.06099999999999
- type: map_at_1000
value: 57.126
- type: map_at_3
value: 52.644999999999996
- type: map_at_5
value: 54.909
- type: mrr_at_1
value: 72.505
- type: mrr_at_10
value: 79.66
- type: mrr_at_100
value: 79.869
- type: mrr_at_1000
value: 79.88
- type: mrr_at_3
value: 78.411
- type: mrr_at_5
value: 79.19800000000001
- type: ndcg_at_1
value: 72.505
- type: ndcg_at_10
value: 65.094
- type: ndcg_at_100
value: 68.219
- type: ndcg_at_1000
value: 69.515
- type: ndcg_at_3
value: 59.99
- type: ndcg_at_5
value: 62.909000000000006
- type: precision_at_1
value: 72.505
- type: precision_at_10
value: 13.749
- type: precision_at_100
value: 1.619
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 38.357
- type: precision_at_5
value: 25.313000000000002
- type: recall_at_1
value: 36.253
- type: recall_at_10
value: 68.744
- type: recall_at_100
value: 80.925
- type: recall_at_1000
value: 89.534
- type: recall_at_3
value: 57.535000000000004
- type: recall_at_5
value: 63.282000000000004
task:
type: Retrieval
- dataset:
config: default
name: MTEB ImdbClassification
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics:
- type: accuracy
value: 80.82239999999999
- type: ap
value: 75.65895781725314
- type: f1
value: 80.75880969095746
task:
type: Classification
- dataset:
config: default
name: MTEB MSMARCO
revision: None
split: dev
type: msmarco
metrics:
- type: map_at_1
value: 21.624
- type: map_at_10
value: 34.075
- type: map_at_100
value: 35.229
- type: map_at_1000
value: 35.276999999999994
- type: map_at_3
value: 30.245
- type: map_at_5
value: 32.42
- type: mrr_at_1
value: 22.264
- type: mrr_at_10
value: 34.638000000000005
- type: mrr_at_100
value: 35.744
- type: mrr_at_1000
value: 35.787
- type: mrr_at_3
value: 30.891000000000002
- type: mrr_at_5
value: 33.042
- type: ndcg_at_1
value: 22.264
- type: ndcg_at_10
value: 40.991
- type: ndcg_at_100
value: 46.563
- type: ndcg_at_1000
value: 47.743
- type: ndcg_at_3
value: 33.198
- type: ndcg_at_5
value: 37.069
- type: precision_at_1
value: 22.264
- type: precision_at_10
value: 6.5089999999999995
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 14.216999999999999
- type: precision_at_5
value: 10.487
- type: recall_at_1
value: 21.624
- type: recall_at_10
value: 62.303
- type: recall_at_100
value: 88.124
- type: recall_at_1000
value: 97.08
- type: recall_at_3
value: 41.099999999999994
- type: recall_at_5
value: 50.381
task:
type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 91.06703146374831
- type: f1
value: 90.86867815863172
task:
type: Classification
- dataset:
config: de
name: MTEB MTOPDomainClassification (de)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 87.46970977740209
- type: f1
value: 86.36832872036588
task:
type: Classification
- dataset:
config: es
name: MTEB MTOPDomainClassification (es)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 89.26951300867245
- type: f1
value: 88.93561193959502
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPDomainClassification (fr)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 84.22799874725963
- type: f1
value: 84.30490069236556
task:
type: Classification
- dataset:
config: hi
name: MTEB MTOPDomainClassification (hi)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 86.02007888131948
- type: f1
value: 85.39376041027991
task:
type: Classification
- dataset:
config: th
name: MTEB MTOPDomainClassification (th)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 85.34900542495481
- type: f1
value: 85.39859673336713
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 71.078431372549
- type: f1
value: 53.45071102002276
task:
type: Classification
- dataset:
config: de
name: MTEB MTOPIntentClassification (de)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 65.85798816568047
- type: f1
value: 46.53112748993529
task:
type: Classification
- dataset:
config: es
name: MTEB MTOPIntentClassification (es)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 67.96864576384256
- type: f1
value: 45.966703022829506
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPIntentClassification (fr)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 61.31537738803633
- type: f1
value: 45.52601712835461
task:
type: Classification
- dataset:
config: hi
name: MTEB MTOPIntentClassification (hi)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 66.29616349946218
- type: f1
value: 47.24166485726613
task:
type: Classification
- dataset:
config: th
name: MTEB MTOPIntentClassification (th)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 67.51537070524412
- type: f1
value: 49.463476319014276
task:
type: Classification
- dataset:
config: af
name: MTEB MassiveIntentClassification (af)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 57.06792199058508
- type: f1
value: 54.094921857502285
task:
type: Classification
- dataset:
config: am
name: MTEB MassiveIntentClassification (am)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 51.960322797579025
- type: f1
value: 48.547371223370945
task:
type: Classification
- dataset:
config: ar
name: MTEB MassiveIntentClassification (ar)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 54.425016812373904
- type: f1
value: 50.47069202054312
task:
type: Classification
- dataset:
config: az
name: MTEB MassiveIntentClassification (az)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 59.798251513113655
- type: f1
value: 57.05013069086648
task:
type: Classification
- dataset:
config: bn
name: MTEB MassiveIntentClassification (bn)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 59.37794216543376
- type: f1
value: 56.3607992649805
task:
type: Classification
- dataset:
config: cy
name: MTEB MassiveIntentClassification (cy)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 46.56018829858777
- type: f1
value: 43.87319715715134
task:
type: Classification
- dataset:
config: da
name: MTEB MassiveIntentClassification (da)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 62.9724277067922
- type: f1
value: 59.36480066245562
task:
type: Classification
- dataset:
config: de
name: MTEB MassiveIntentClassification (de)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 62.72696704774715
- type: f1
value: 59.143595966615855
task:
type: Classification
- dataset:
config: el
name: MTEB MassiveIntentClassification (el)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 61.5971755211836
- type: f1
value: 59.169445724946726
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 70.29589778076665
- type: f1
value: 67.7577001808977
task:
type: Classification
- dataset:
config: es
name: MTEB MassiveIntentClassification (es)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 66.31136516476126
- type: f1
value: 64.52032955983242
task:
type: Classification
- dataset:
config: fa
name: MTEB MassiveIntentClassification (fa)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 65.54472091459314
- type: f1
value: 61.47903120066317
task:
type: Classification
- dataset:
config: fi
name: MTEB MassiveIntentClassification (fi)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 61.45595158036314
- type: f1
value: 58.0891846024637
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveIntentClassification (fr)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 65.47074646940149
- type: f1
value: 62.84830858877575
task:
type: Classification
- dataset:
config: he
name: MTEB MassiveIntentClassification (he)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 58.046402151983855
- type: f1
value: 55.269074430533195
task:
type: Classification
- dataset:
config: hi
name: MTEB MassiveIntentClassification (hi)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 64.06523201075991
- type: f1
value: 61.35339643021369
task:
type: Classification
- dataset:
config: hu
name: MTEB MassiveIntentClassification (hu)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 60.954942837928726
- type: f1
value: 57.07035922704846
task:
type: Classification
- dataset:
config: hy
name: MTEB MassiveIntentClassification (hy)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 57.404169468728995
- type: f1
value: 53.94259011839138
task:
type: Classification
- dataset:
config: id
name: MTEB MassiveIntentClassification (id)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 64.16610625420309
- type: f1
value: 61.337103431499365
task:
type: Classification
- dataset:
config: is
name: MTEB MassiveIntentClassification (is)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 52.262945527908535
- type: f1
value: 49.7610691598921
task:
type: Classification
- dataset:
config: it
name: MTEB MassiveIntentClassification (it)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 65.54472091459314
- type: f1
value: 63.469099018440154
task:
type: Classification
- dataset:
config: ja
name: MTEB MassiveIntentClassification (ja)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 68.22797579018157
- type: f1
value: 64.89098471083001
task:
type: Classification
- dataset:
config: jv
name: MTEB MassiveIntentClassification (jv)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 50.847343644922674
- type: f1
value: 47.8536963168393
task:
type: Classification
- dataset:
config: ka
name: MTEB MassiveIntentClassification (ka)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 48.45326160053799
- type: f1
value: 46.370078045805556
task:
type: Classification
- dataset:
config: km
name: MTEB MassiveIntentClassification (km)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 42.83120376597175
- type: f1
value: 39.68948521599982
task:
type: Classification
- dataset:
config: kn
name: MTEB MassiveIntentClassification (kn)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 57.5084061869536
- type: f1
value: 53.961876160401545
task:
type: Classification
- dataset:
config: ko
name: MTEB MassiveIntentClassification (ko)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 63.7895090786819
- type: f1
value: 61.134223684676
task:
type: Classification
- dataset:
config: lv
name: MTEB MassiveIntentClassification (lv)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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config: ml
name: MTEB MassiveIntentClassification (ml)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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config: mn
name: MTEB MassiveIntentClassification (mn)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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config: ms
name: MTEB MassiveIntentClassification (ms)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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value: 55.84892781767421
task:
type: Classification
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config: my
name: MTEB MassiveIntentClassification (my)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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config: nb
name: MTEB MassiveIntentClassification (nb)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (nl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (pl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (pt)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (ru)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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config: sl
name: MTEB MassiveIntentClassification (sl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
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name: MTEB MassiveIntentClassification (sq)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (sv)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (sw)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (ta)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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type: mteb/amazon_massive_intent
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task:
type: Classification
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config: te
name: MTEB MassiveIntentClassification (te)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (th)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (tl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (tr)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
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name: MTEB MassiveIntentClassification (ur)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (vi)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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type: mteb/amazon_massive_intent
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task:
type: Classification
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config: zh-CN
name: MTEB MassiveIntentClassification (zh-CN)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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name: MTEB MassiveIntentClassification (zh-TW)
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split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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config: af
name: MTEB MassiveScenarioClassification (af)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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task:
type: Classification
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name: MTEB MassiveScenarioClassification (am)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 56.83591123066578
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task:
type: Classification
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config: ar
name: MTEB MassiveScenarioClassification (ar)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 59.62340282447881
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task:
type: Classification
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name: MTEB MassiveScenarioClassification (az)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 60.85406859448555
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task:
type: Classification
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name: MTEB MassiveScenarioClassification (bn)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 62.76731674512441
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task:
type: Classification
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config: cy
name: MTEB MassiveScenarioClassification (cy)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 50.181573638197705
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value: 46.98422176289957
task:
type: Classification
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config: da
name: MTEB MassiveScenarioClassification (da)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 68.92737054472092
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value: 67.69135611952979
task:
type: Classification
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config: de
name: MTEB MassiveScenarioClassification (de)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 69.18964357767318
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task:
type: Classification
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config: el
name: MTEB MassiveScenarioClassification (el)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 67.0712844653665
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task:
type: Classification
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config: en
name: MTEB MassiveScenarioClassification (en)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
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value: 74.4754539340955
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task:
type: Classification
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config: es
name: MTEB MassiveScenarioClassification (es)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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task:
type: Classification
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config: fa
name: MTEB MassiveScenarioClassification (fa)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 68.70880968392737
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task:
type: Classification
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config: fi
name: MTEB MassiveScenarioClassification (fi)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 65.95494283792871
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value: 65.06191009049222
task:
type: Classification
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config: fr
name: MTEB MassiveScenarioClassification (fr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 68.75924680564896
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task:
type: Classification
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config: he
name: MTEB MassiveScenarioClassification (he)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 63.806321452589096
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task:
type: Classification
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config: hi
name: MTEB MassiveScenarioClassification (hi)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 67.68997982515133
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task:
type: Classification
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name: MTEB MassiveScenarioClassification (hu)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 66.46940147948891
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task:
type: Classification
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config: hy
name: MTEB MassiveScenarioClassification (hy)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 59.49899125756556
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value: 57.90333469917769
task:
type: Classification
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config: id
name: MTEB MassiveScenarioClassification (id)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 67.9219905850706
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task:
type: Classification
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config: is
name: MTEB MassiveScenarioClassification (is)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 56.486213853396094
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value: 54.85282355583758
task:
type: Classification
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config: it
name: MTEB MassiveScenarioClassification (it)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 69.04169468728985
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task:
type: Classification
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config: ja
name: MTEB MassiveScenarioClassification (ja)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 73.88702084734365
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value: 74.04474735232299
task:
type: Classification
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config: jv
name: MTEB MassiveScenarioClassification (jv)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 56.63416274377943
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value: 55.11332211687954
task:
type: Classification
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config: ka
name: MTEB MassiveScenarioClassification (ka)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 52.23604572965702
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value: 50.86529813991055
task:
type: Classification
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config: km
name: MTEB MassiveScenarioClassification (km)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 46.62407531943511
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task:
type: Classification
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config: kn
name: MTEB MassiveScenarioClassification (kn)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 59.15601882985878
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value: 57.522837510959924
task:
type: Classification
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config: ko
name: MTEB MassiveScenarioClassification (ko)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 69.84532616005382
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task:
type: Classification
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config: lv
name: MTEB MassiveScenarioClassification (lv)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 56.65770006724949
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value: 55.84219135523227
task:
type: Classification
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config: ml
name: MTEB MassiveScenarioClassification (ml)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 66.53665097511768
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value: 65.09087787792639
task:
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config: mn
name: MTEB MassiveScenarioClassification (mn)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 59.31405514458642
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task:
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name: MTEB MassiveScenarioClassification (ms)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 64.88231338264964
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value: 62.751099407787926
task:
type: Classification
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config: my
name: MTEB MassiveScenarioClassification (my)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 58.86012104909213
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value: 56.29118323058282
task:
type: Classification
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config: nb
name: MTEB MassiveScenarioClassification (nb)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 67.37390719569602
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value: 66.27922244885102
task:
type: Classification
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config: nl
name: MTEB MassiveScenarioClassification (nl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
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value: 70.8675184936113
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value: 70.22146529932019
task:
type: Classification
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config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 68.2212508406187
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value: 67.77454802056282
task:
type: Classification
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config: pt
name: MTEB MassiveScenarioClassification (pt)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 68.18090114324143
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value: 68.03737625431621
task:
type: Classification
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config: ro
name: MTEB MassiveScenarioClassification (ro)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 64.65030262273034
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value: 63.792945486912856
task:
type: Classification
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config: ru
name: MTEB MassiveScenarioClassification (ru)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 63.772749631087066
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value: 63.4539101720024
- type: f1_weighted
value: 62.778603897469566
- type: main_score
value: 63.772749631087066
task:
type: Classification
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config: sl
name: MTEB MassiveScenarioClassification (sl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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- type: accuracy
value: 60.17821116341627
- type: f1
value: 59.3935969827171
task:
type: Classification
- dataset:
config: sq
name: MTEB MassiveScenarioClassification (sq)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
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value: 62.86146603900471
- type: f1
value: 60.133692735032376
task:
type: Classification
- dataset:
config: sv
name: MTEB MassiveScenarioClassification (sv)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
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value: 70.89441829186282
- type: f1
value: 70.03064076194089
task:
type: Classification
- dataset:
config: sw
name: MTEB MassiveScenarioClassification (sw)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 58.15063887020847
- type: f1
value: 56.23326278499678
task:
type: Classification
- dataset:
config: ta
name: MTEB MassiveScenarioClassification (ta)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 59.43846671149966
- type: f1
value: 57.70440450281974
task:
type: Classification
- dataset:
config: te
name: MTEB MassiveScenarioClassification (te)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 60.8507061197041
- type: f1
value: 59.22916396061171
task:
type: Classification
- dataset:
config: th
name: MTEB MassiveScenarioClassification (th)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 70.65568258238063
- type: f1
value: 69.90736239440633
task:
type: Classification
- dataset:
config: tl
name: MTEB MassiveScenarioClassification (tl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 60.8843308675185
- type: f1
value: 59.30332663713599
task:
type: Classification
- dataset:
config: tr
name: MTEB MassiveScenarioClassification (tr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 68.05312710154674
- type: f1
value: 67.44024062594775
task:
type: Classification
- dataset:
config: ur
name: MTEB MassiveScenarioClassification (ur)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 62.111634162743776
- type: f1
value: 60.89083013084519
task:
type: Classification
- dataset:
config: vi
name: MTEB MassiveScenarioClassification (vi)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 67.44115669132482
- type: f1
value: 67.92227541674552
task:
type: Classification
- dataset:
config: zh-CN
name: MTEB MassiveScenarioClassification (zh-CN)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 74.4687289845326
- type: f1
value: 74.16376793486025
task:
type: Classification
- dataset:
config: zh-TW
name: MTEB MassiveScenarioClassification (zh-TW)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 68.31876260928043
- type: f1
value: 68.5246745215607
task:
type: Classification
- dataset:
config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
- type: v_measure
value: 30.90431696479766
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
- type: v_measure
value: 27.259158476693774
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
split: test
type: mteb/mind_small
metrics:
- type: map
value: 30.28445330838555
- type: mrr
value: 31.15758529581164
task:
type: Reranking
- dataset:
config: default
name: MTEB NFCorpus
revision: None
split: test
type: nfcorpus
metrics:
- type: map_at_1
value: 5.353
- type: map_at_10
value: 11.565
- type: map_at_100
value: 14.097000000000001
- type: map_at_1000
value: 15.354999999999999
- type: map_at_3
value: 8.749
- type: map_at_5
value: 9.974
- type: mrr_at_1
value: 42.105
- type: mrr_at_10
value: 50.589
- type: mrr_at_100
value: 51.187000000000005
- type: mrr_at_1000
value: 51.233
- type: mrr_at_3
value: 48.246
- type: mrr_at_5
value: 49.546
- type: ndcg_at_1
value: 40.402
- type: ndcg_at_10
value: 31.009999999999998
- type: ndcg_at_100
value: 28.026
- type: ndcg_at_1000
value: 36.905
- type: ndcg_at_3
value: 35.983
- type: ndcg_at_5
value: 33.764
- type: precision_at_1
value: 42.105
- type: precision_at_10
value: 22.786
- type: precision_at_100
value: 6.916
- type: precision_at_1000
value: 1.981
- type: precision_at_3
value: 33.333
- type: precision_at_5
value: 28.731
- type: recall_at_1
value: 5.353
- type: recall_at_10
value: 15.039
- type: recall_at_100
value: 27.348
- type: recall_at_1000
value: 59.453
- type: recall_at_3
value: 9.792
- type: recall_at_5
value: 11.882
task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ
revision: None
split: test
type: nq
metrics:
- type: map_at_1
value: 33.852
- type: map_at_10
value: 48.924
- type: map_at_100
value: 49.854
- type: map_at_1000
value: 49.886
- type: map_at_3
value: 44.9
- type: map_at_5
value: 47.387
- type: mrr_at_1
value: 38.035999999999994
- type: mrr_at_10
value: 51.644
- type: mrr_at_100
value: 52.339
- type: mrr_at_1000
value: 52.35999999999999
- type: mrr_at_3
value: 48.421
- type: mrr_at_5
value: 50.468999999999994
- type: ndcg_at_1
value: 38.007000000000005
- type: ndcg_at_10
value: 56.293000000000006
- type: ndcg_at_100
value: 60.167
- type: ndcg_at_1000
value: 60.916000000000004
- type: ndcg_at_3
value: 48.903999999999996
- type: ndcg_at_5
value: 52.978
- type: precision_at_1
value: 38.007000000000005
- type: precision_at_10
value: 9.041
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 22.084
- type: precision_at_5
value: 15.608
- type: recall_at_1
value: 33.852
- type: recall_at_10
value: 75.893
- type: recall_at_100
value: 92.589
- type: recall_at_1000
value: 98.153
- type: recall_at_3
value: 56.969
- type: recall_at_5
value: 66.283
task:
type: Retrieval
- dataset:
config: default
name: MTEB QuoraRetrieval
revision: None
split: test
type: quora
metrics:
- type: map_at_1
value: 69.174
- type: map_at_10
value: 82.891
- type: map_at_100
value: 83.545
- type: map_at_1000
value: 83.56700000000001
- type: map_at_3
value: 79.944
- type: map_at_5
value: 81.812
- type: mrr_at_1
value: 79.67999999999999
- type: mrr_at_10
value: 86.279
- type: mrr_at_100
value: 86.39
- type: mrr_at_1000
value: 86.392
- type: mrr_at_3
value: 85.21
- type: mrr_at_5
value: 85.92999999999999
- type: ndcg_at_1
value: 79.69000000000001
- type: ndcg_at_10
value: 86.929
- type: ndcg_at_100
value: 88.266
- type: ndcg_at_1000
value: 88.428
- type: ndcg_at_3
value: 83.899
- type: ndcg_at_5
value: 85.56700000000001
- type: precision_at_1
value: 79.69000000000001
- type: precision_at_10
value: 13.161000000000001
- type: precision_at_100
value: 1.513
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.603
- type: precision_at_5
value: 24.138
- type: recall_at_1
value: 69.174
- type: recall_at_10
value: 94.529
- type: recall_at_100
value: 99.15
- type: recall_at_1000
value: 99.925
- type: recall_at_3
value: 85.86200000000001
- type: recall_at_5
value: 90.501
task:
type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: v_measure
value: 39.13064340585255
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P
revision: 282350215ef01743dc01b456c7f5241fa8937f16
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: v_measure
value: 58.97884249325877
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS
revision: None
split: test
type: scidocs
metrics:
- type: map_at_1
value: 3.4680000000000004
- type: map_at_10
value: 7.865
- type: map_at_100
value: 9.332
- type: map_at_1000
value: 9.587
- type: map_at_3
value: 5.800000000000001
- type: map_at_5
value: 6.8790000000000004
- type: mrr_at_1
value: 17.0
- type: mrr_at_10
value: 25.629
- type: mrr_at_100
value: 26.806
- type: mrr_at_1000
value: 26.889000000000003
- type: mrr_at_3
value: 22.8
- type: mrr_at_5
value: 24.26
- type: ndcg_at_1
value: 17.0
- type: ndcg_at_10
value: 13.895
- type: ndcg_at_100
value: 20.491999999999997
- type: ndcg_at_1000
value: 25.759999999999998
- type: ndcg_at_3
value: 13.347999999999999
- type: ndcg_at_5
value: 11.61
- type: precision_at_1
value: 17.0
- type: precision_at_10
value: 7.090000000000001
- type: precision_at_100
value: 1.669
- type: precision_at_1000
value: 0.294
- type: precision_at_3
value: 12.3
- type: precision_at_5
value: 10.02
- type: recall_at_1
value: 3.4680000000000004
- type: recall_at_10
value: 14.363000000000001
- type: recall_at_100
value: 33.875
- type: recall_at_1000
value: 59.711999999999996
- type: recall_at_3
value: 7.483
- type: recall_at_5
value: 10.173
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
split: test
type: mteb/sickr-sts
metrics:
- type: cos_sim_pearson
value: 83.04084311714061
- type: cos_sim_spearman
value: 77.51342467443078
- type: euclidean_pearson
value: 80.0321166028479
- type: euclidean_spearman
value: 77.29249114733226
- type: manhattan_pearson
value: 80.03105964262431
- type: manhattan_spearman
value: 77.22373689514794
task:
type: STS
- dataset:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cos_sim_pearson
value: 84.1680158034387
- type: cos_sim_spearman
value: 76.55983344071117
- type: euclidean_pearson
value: 79.75266678300143
- type: euclidean_spearman
value: 75.34516823467025
- type: manhattan_pearson
value: 79.75959151517357
- type: manhattan_spearman
value: 75.42330344141912
task:
type: STS
- dataset:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cos_sim_pearson
value: 76.48898993209346
- type: cos_sim_spearman
value: 76.96954120323366
- type: euclidean_pearson
value: 76.94139109279668
- type: euclidean_spearman
value: 76.85860283201711
- type: manhattan_pearson
value: 76.6944095091912
- type: manhattan_spearman
value: 76.61096912972553
task:
type: STS
- dataset:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cos_sim_pearson
value: 77.85082366246944
- type: cos_sim_spearman
value: 75.52053350101731
- type: euclidean_pearson
value: 77.1165845070926
- type: euclidean_spearman
value: 75.31216065884388
- type: manhattan_pearson
value: 77.06193941833494
- type: manhattan_spearman
value: 75.31003701700112
task:
type: STS
- dataset:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cos_sim_pearson
value: 86.36305246526497
- type: cos_sim_spearman
value: 87.11704613927415
- type: euclidean_pearson
value: 86.04199125810939
- type: euclidean_spearman
value: 86.51117572414263
- type: manhattan_pearson
value: 86.0805106816633
- type: manhattan_spearman
value: 86.52798366512229
task:
type: STS
- dataset:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cos_sim_pearson
value: 82.18536255599724
- type: cos_sim_spearman
value: 83.63377151025418
- type: euclidean_pearson
value: 83.24657467993141
- type: euclidean_spearman
value: 84.02751481993825
- type: manhattan_pearson
value: 83.11941806582371
- type: manhattan_spearman
value: 83.84251281019304
task:
type: STS
- dataset:
config: ko-ko
name: MTEB STS17 (ko-ko)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 78.95816528475514
- type: cos_sim_spearman
value: 78.86607380120462
- type: euclidean_pearson
value: 78.51268699230545
- type: euclidean_spearman
value: 79.11649316502229
- type: manhattan_pearson
value: 78.32367302808157
- type: manhattan_spearman
value: 78.90277699624637
task:
type: STS
- dataset:
config: ar-ar
name: MTEB STS17 (ar-ar)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 72.89126914997624
- type: cos_sim_spearman
value: 73.0296921832678
- type: euclidean_pearson
value: 71.50385903677738
- type: euclidean_spearman
value: 73.13368899716289
- type: manhattan_pearson
value: 71.47421463379519
- type: manhattan_spearman
value: 73.03383242946575
task:
type: STS
- dataset:
config: en-ar
name: MTEB STS17 (en-ar)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 59.22923684492637
- type: cos_sim_spearman
value: 57.41013211368396
- type: euclidean_pearson
value: 61.21107388080905
- type: euclidean_spearman
value: 60.07620768697254
- type: manhattan_pearson
value: 59.60157142786555
- type: manhattan_spearman
value: 59.14069604103739
task:
type: STS
- dataset:
config: en-de
name: MTEB STS17 (en-de)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 76.24345978774299
- type: cos_sim_spearman
value: 77.24225743830719
- type: euclidean_pearson
value: 76.66226095469165
- type: euclidean_spearman
value: 77.60708820493146
- type: manhattan_pearson
value: 76.05303324760429
- type: manhattan_spearman
value: 76.96353149912348
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 85.50879160160852
- type: cos_sim_spearman
value: 86.43594662965224
- type: euclidean_pearson
value: 86.06846012826577
- type: euclidean_spearman
value: 86.02041395794136
- type: manhattan_pearson
value: 86.10916255616904
- type: manhattan_spearman
value: 86.07346068198953
task:
type: STS
- dataset:
config: en-tr
name: MTEB STS17 (en-tr)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 58.39803698977196
- type: cos_sim_spearman
value: 55.96910950423142
- type: euclidean_pearson
value: 58.17941175613059
- type: euclidean_spearman
value: 55.03019330522745
- type: manhattan_pearson
value: 57.333358138183286
- type: manhattan_spearman
value: 54.04614023149965
task:
type: STS
- dataset:
config: es-en
name: MTEB STS17 (es-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 70.98304089637197
- type: cos_sim_spearman
value: 72.44071656215888
- type: euclidean_pearson
value: 72.19224359033983
- type: euclidean_spearman
value: 73.89871188913025
- type: manhattan_pearson
value: 71.21098311547406
- type: manhattan_spearman
value: 72.93405764824821
task:
type: STS
- dataset:
config: es-es
name: MTEB STS17 (es-es)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 85.99792397466308
- type: cos_sim_spearman
value: 84.83824377879495
- type: euclidean_pearson
value: 85.70043288694438
- type: euclidean_spearman
value: 84.70627558703686
- type: manhattan_pearson
value: 85.89570850150801
- type: manhattan_spearman
value: 84.95806105313007
task:
type: STS
- dataset:
config: fr-en
name: MTEB STS17 (fr-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 72.21850322994712
- type: cos_sim_spearman
value: 72.28669398117248
- type: euclidean_pearson
value: 73.40082510412948
- type: euclidean_spearman
value: 73.0326539281865
- type: manhattan_pearson
value: 71.8659633964841
- type: manhattan_spearman
value: 71.57817425823303
task:
type: STS
- dataset:
config: it-en
name: MTEB STS17 (it-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 75.80921368595645
- type: cos_sim_spearman
value: 77.33209091229315
- type: euclidean_pearson
value: 76.53159540154829
- type: euclidean_spearman
value: 78.17960842810093
- type: manhattan_pearson
value: 76.13530186637601
- type: manhattan_spearman
value: 78.00701437666875
task:
type: STS
- dataset:
config: nl-en
name: MTEB STS17 (nl-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 74.74980608267349
- type: cos_sim_spearman
value: 75.37597374318821
- type: euclidean_pearson
value: 74.90506081911661
- type: euclidean_spearman
value: 75.30151613124521
- type: manhattan_pearson
value: 74.62642745918002
- type: manhattan_spearman
value: 75.18619716592303
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 59.632662289205584
- type: cos_sim_spearman
value: 60.938543391610914
- type: euclidean_pearson
value: 62.113200529767056
- type: euclidean_spearman
value: 61.410312633261164
- type: manhattan_pearson
value: 61.75494698945686
- type: manhattan_spearman
value: 60.92726195322362
task:
type: STS
- dataset:
config: de
name: MTEB STS22 (de)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 45.283470551557244
- type: cos_sim_spearman
value: 53.44833015864201
- type: euclidean_pearson
value: 41.17892011120893
- type: euclidean_spearman
value: 53.81441383126767
- type: manhattan_pearson
value: 41.17482200420659
- type: manhattan_spearman
value: 53.82180269276363
task:
type: STS
- dataset:
config: es
name: MTEB STS22 (es)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 60.5069165306236
- type: cos_sim_spearman
value: 66.87803259033826
- type: euclidean_pearson
value: 63.5428979418236
- type: euclidean_spearman
value: 66.9293576586897
- type: manhattan_pearson
value: 63.59789526178922
- type: manhattan_spearman
value: 66.86555009875066
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 28.23026196280264
- type: cos_sim_spearman
value: 35.79397812652861
- type: euclidean_pearson
value: 17.828102102767353
- type: euclidean_spearman
value: 35.721501145568894
- type: manhattan_pearson
value: 17.77134274219677
- type: manhattan_spearman
value: 35.98107902846267
task:
type: STS
- dataset:
config: tr
name: MTEB STS22 (tr)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 56.51946541393812
- type: cos_sim_spearman
value: 63.714686006214485
- type: euclidean_pearson
value: 58.32104651305898
- type: euclidean_spearman
value: 62.237110895702216
- type: manhattan_pearson
value: 58.579416468759185
- type: manhattan_spearman
value: 62.459738981727
task:
type: STS
- dataset:
config: ar
name: MTEB STS22 (ar)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 48.76009839569795
- type: cos_sim_spearman
value: 56.65188431953149
- type: euclidean_pearson
value: 50.997682160915595
- type: euclidean_spearman
value: 55.99910008818135
- type: manhattan_pearson
value: 50.76220659606342
- type: manhattan_spearman
value: 55.517347595391456
task:
type: STS
- dataset:
config: ru
name: MTEB STS22 (ru)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 50.724322379215934
- type: cosine_spearman
value: 59.90449732164651
- type: euclidean_pearson
value: 50.227545226784024
- type: euclidean_spearman
value: 59.898906527601085
- type: main_score
value: 59.90449732164651
- type: manhattan_pearson
value: 50.21762139819405
- type: manhattan_spearman
value: 59.761039813759
- type: pearson
value: 50.724322379215934
- type: spearman
value: 59.90449732164651
task:
type: STS
- dataset:
config: zh
name: MTEB STS22 (zh)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 54.717524559088005
- type: cos_sim_spearman
value: 66.83570886252286
- type: euclidean_pearson
value: 58.41338625505467
- type: euclidean_spearman
value: 66.68991427704938
- type: manhattan_pearson
value: 58.78638572916807
- type: manhattan_spearman
value: 66.58684161046335
task:
type: STS
- dataset:
config: fr
name: MTEB STS22 (fr)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 73.2962042954962
- type: cos_sim_spearman
value: 76.58255504852025
- type: euclidean_pearson
value: 75.70983192778257
- type: euclidean_spearman
value: 77.4547684870542
- type: manhattan_pearson
value: 75.75565853870485
- type: manhattan_spearman
value: 76.90208974949428
task:
type: STS
- dataset:
config: de-en
name: MTEB STS22 (de-en)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 54.47396266924846
- type: cos_sim_spearman
value: 56.492267162048606
- type: euclidean_pearson
value: 55.998505203070195
- type: euclidean_spearman
value: 56.46447012960222
- type: manhattan_pearson
value: 54.873172394430995
- type: manhattan_spearman
value: 56.58111534551218
task:
type: STS
- dataset:
config: es-en
name: MTEB STS22 (es-en)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 69.87177267688686
- type: cos_sim_spearman
value: 74.57160943395763
- type: euclidean_pearson
value: 70.88330406826788
- type: euclidean_spearman
value: 74.29767636038422
- type: manhattan_pearson
value: 71.38245248369536
- type: manhattan_spearman
value: 74.53102232732175
task:
type: STS
- dataset:
config: it
name: MTEB STS22 (it)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 72.80225656959544
- type: cos_sim_spearman
value: 76.52646173725735
- type: euclidean_pearson
value: 73.95710720200799
- type: euclidean_spearman
value: 76.54040031984111
- type: manhattan_pearson
value: 73.89679971946774
- type: manhattan_spearman
value: 76.60886958161574
task:
type: STS
- dataset:
config: pl-en
name: MTEB STS22 (pl-en)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 70.70844249898789
- type: cos_sim_spearman
value: 72.68571783670241
- type: euclidean_pearson
value: 72.38800772441031
- type: euclidean_spearman
value: 72.86804422703312
- type: manhattan_pearson
value: 71.29840508203515
- type: manhattan_spearman
value: 71.86264441749513
task:
type: STS
- dataset:
config: zh-en
name: MTEB STS22 (zh-en)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 58.647478923935694
- type: cos_sim_spearman
value: 63.74453623540931
- type: euclidean_pearson
value: 59.60138032437505
- type: euclidean_spearman
value: 63.947930832166065
- type: manhattan_pearson
value: 58.59735509491861
- type: manhattan_spearman
value: 62.082503844627404
task:
type: STS
- dataset:
config: es-it
name: MTEB STS22 (es-it)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 65.8722516867162
- type: cos_sim_spearman
value: 71.81208592523012
- type: euclidean_pearson
value: 67.95315252165956
- type: euclidean_spearman
value: 73.00749822046009
- type: manhattan_pearson
value: 68.07884688638924
- type: manhattan_spearman
value: 72.34210325803069
task:
type: STS
- dataset:
config: de-fr
name: MTEB STS22 (de-fr)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 54.5405814240949
- type: cos_sim_spearman
value: 60.56838649023775
- type: euclidean_pearson
value: 53.011731611314104
- type: euclidean_spearman
value: 58.533194841668426
- type: manhattan_pearson
value: 53.623067729338494
- type: manhattan_spearman
value: 58.018756154446926
task:
type: STS
- dataset:
config: de-pl
name: MTEB STS22 (de-pl)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 13.611046866216112
- type: cos_sim_spearman
value: 28.238192909158492
- type: euclidean_pearson
value: 22.16189199885129
- type: euclidean_spearman
value: 35.012895679076564
- type: manhattan_pearson
value: 21.969771178698387
- type: manhattan_spearman
value: 32.456985088607475
task:
type: STS
- dataset:
config: fr-pl
name: MTEB STS22 (fr-pl)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 74.58077407011655
- type: cos_sim_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 74.64613843596234
- type: euclidean_spearman
value: 84.51542547285167
- type: manhattan_pearson
value: 75.15335973101396
- type: manhattan_spearman
value: 84.51542547285167
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cos_sim_pearson
value: 82.0739825531578
- type: cos_sim_spearman
value: 84.01057479311115
- type: euclidean_pearson
value: 83.85453227433344
- type: euclidean_spearman
value: 84.01630226898655
- type: manhattan_pearson
value: 83.75323603028978
- type: manhattan_spearman
value: 83.89677983727685
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 78.12945623123957
- type: mrr
value: 93.87738713719106
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact
revision: None
split: test
type: scifact
metrics:
- type: map_at_1
value: 52.983000000000004
- type: map_at_10
value: 62.946000000000005
- type: map_at_100
value: 63.514
- type: map_at_1000
value: 63.554
- type: map_at_3
value: 60.183
- type: map_at_5
value: 61.672000000000004
- type: mrr_at_1
value: 55.667
- type: mrr_at_10
value: 64.522
- type: mrr_at_100
value: 64.957
- type: mrr_at_1000
value: 64.995
- type: mrr_at_3
value: 62.388999999999996
- type: mrr_at_5
value: 63.639
- type: ndcg_at_1
value: 55.667
- type: ndcg_at_10
value: 67.704
- type: ndcg_at_100
value: 70.299
- type: ndcg_at_1000
value: 71.241
- type: ndcg_at_3
value: 62.866
- type: ndcg_at_5
value: 65.16999999999999
- type: precision_at_1
value: 55.667
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.053
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 24.444
- type: precision_at_5
value: 16.133
- type: recall_at_1
value: 52.983000000000004
- type: recall_at_10
value: 80.656
- type: recall_at_100
value: 92.5
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 67.744
- type: recall_at_5
value: 73.433
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cos_sim_accuracy
value: 99.72772277227723
- type: cos_sim_ap
value: 92.17845897992215
- type: cos_sim_f1
value: 85.9746835443038
- type: cos_sim_precision
value: 87.07692307692308
- type: cos_sim_recall
value: 84.89999999999999
- type: dot_accuracy
value: 99.3039603960396
- type: dot_ap
value: 60.70244020124878
- type: dot_f1
value: 59.92742353551063
- type: dot_precision
value: 62.21743810548978
- type: dot_recall
value: 57.8
- type: euclidean_accuracy
value: 99.71683168316832
- type: euclidean_ap
value: 91.53997039964659
- type: euclidean_f1
value: 84.88372093023257
- type: euclidean_precision
value: 90.02242152466367
- type: euclidean_recall
value: 80.30000000000001
- type: manhattan_accuracy
value: 99.72376237623763
- type: manhattan_ap
value: 91.80756777790289
- type: manhattan_f1
value: 85.48468106479157
- type: manhattan_precision
value: 85.8728557013118
- type: manhattan_recall
value: 85.1
- type: max_accuracy
value: 99.72772277227723
- type: max_ap
value: 92.17845897992215
- type: max_f1
value: 85.9746835443038
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 53.52464042600003
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 32.071631948736
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 49.19552407604654
- type: mrr
value: 49.95269130379425
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cos_sim_pearson
value: 29.345293033095427
- type: cos_sim_spearman
value: 29.976931423258403
- type: dot_pearson
value: 27.047078008958408
- type: dot_spearman
value: 27.75894368380218
task:
type: Summarization
- dataset:
config: default
name: MTEB TRECCOVID
revision: None
split: test
type: trec-covid
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 1.706
- type: map_at_100
value: 9.634
- type: map_at_1000
value: 23.665
- type: map_at_3
value: 0.5950000000000001
- type: map_at_5
value: 0.95
- type: mrr_at_1
value: 86.0
- type: mrr_at_10
value: 91.8
- type: mrr_at_100
value: 91.8
- type: mrr_at_1000
value: 91.8
- type: mrr_at_3
value: 91.0
- type: mrr_at_5
value: 91.8
- type: ndcg_at_1
value: 80.0
- type: ndcg_at_10
value: 72.573
- type: ndcg_at_100
value: 53.954
- type: ndcg_at_1000
value: 47.760999999999996
- type: ndcg_at_3
value: 76.173
- type: ndcg_at_5
value: 75.264
- type: precision_at_1
value: 86.0
- type: precision_at_10
value: 76.4
- type: precision_at_100
value: 55.50000000000001
- type: precision_at_1000
value: 21.802
- type: precision_at_3
value: 81.333
- type: precision_at_5
value: 80.4
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 1.925
- type: recall_at_100
value: 12.762
- type: recall_at_1000
value: 44.946000000000005
- type: recall_at_3
value: 0.634
- type: recall_at_5
value: 1.051
task:
type: Retrieval
- dataset:
config: sqi-eng
name: MTEB Tatoeba (sqi-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 91.0
- type: f1
value: 88.55666666666666
- type: precision
value: 87.46166666666667
- type: recall
value: 91.0
task:
type: BitextMining
- dataset:
config: fry-eng
name: MTEB Tatoeba (fry-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 57.22543352601156
- type: f1
value: 51.03220478943021
- type: precision
value: 48.8150289017341
- type: recall
value: 57.22543352601156
task:
type: BitextMining
- dataset:
config: kur-eng
name: MTEB Tatoeba (kur-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 46.58536585365854
- type: f1
value: 39.66870798578116
- type: precision
value: 37.416085946573745
- type: recall
value: 46.58536585365854
task:
type: BitextMining
- dataset:
config: tur-eng
name: MTEB Tatoeba (tur-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 89.7
- type: f1
value: 86.77999999999999
- type: precision
value: 85.45333333333332
- type: recall
value: 89.7
task:
type: BitextMining
- dataset:
config: deu-eng
name: MTEB Tatoeba (deu-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.58333333333331
- type: precision
value: 96.2
- type: recall
value: 97.39999999999999
task:
type: BitextMining
- dataset:
config: nld-eng
name: MTEB Tatoeba (nld-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 92.4
- type: f1
value: 90.3
- type: precision
value: 89.31666666666668
- type: recall
value: 92.4
task:
type: BitextMining
- dataset:
config: ron-eng
name: MTEB Tatoeba (ron-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 86.9
- type: f1
value: 83.67190476190476
- type: precision
value: 82.23333333333332
- type: recall
value: 86.9
task:
type: BitextMining
- dataset:
config: ang-eng
name: MTEB Tatoeba (ang-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 50.0
- type: f1
value: 42.23229092632078
- type: precision
value: 39.851634683724235
- type: recall
value: 50.0
task:
type: BitextMining
- dataset:
config: ido-eng
name: MTEB Tatoeba (ido-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 76.3
- type: f1
value: 70.86190476190477
- type: precision
value: 68.68777777777777
- type: recall
value: 76.3
task:
type: BitextMining
- dataset:
config: jav-eng
name: MTEB Tatoeba (jav-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 57.073170731707314
- type: f1
value: 50.658958927251604
- type: precision
value: 48.26480836236933
- type: recall
value: 57.073170731707314
task:
type: BitextMining
- dataset:
config: isl-eng
name: MTEB Tatoeba (isl-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 68.2
- type: f1
value: 62.156507936507936
- type: precision
value: 59.84964285714286
- type: recall
value: 68.2
task:
type: BitextMining
- dataset:
config: slv-eng
name: MTEB Tatoeba (slv-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 77.52126366950182
- type: f1
value: 72.8496210148701
- type: precision
value: 70.92171498003819
- type: recall
value: 77.52126366950182
task:
type: BitextMining
- dataset:
config: cym-eng
name: MTEB Tatoeba (cym-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 70.78260869565217
- type: f1
value: 65.32422360248447
- type: precision
value: 63.063067367415194
- type: recall
value: 70.78260869565217
task:
type: BitextMining
- dataset:
config: kaz-eng
name: MTEB Tatoeba (kaz-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 78.43478260869566
- type: f1
value: 73.02608695652172
- type: precision
value: 70.63768115942028
- type: recall
value: 78.43478260869566
task:
type: BitextMining
- dataset:
config: est-eng
name: MTEB Tatoeba (est-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 60.9
- type: f1
value: 55.309753694581275
- type: precision
value: 53.130476190476195
- type: recall
value: 60.9
task:
type: BitextMining
- dataset:
config: heb-eng
name: MTEB Tatoeba (heb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 72.89999999999999
- type: f1
value: 67.92023809523809
- type: precision
value: 65.82595238095237
- type: recall
value: 72.89999999999999
task:
type: BitextMining
- dataset:
config: gla-eng
name: MTEB Tatoeba (gla-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 46.80337756332931
- type: f1
value: 39.42174900558496
- type: precision
value: 36.97101116280851
- type: recall
value: 46.80337756332931
task:
type: BitextMining
- dataset:
config: mar-eng
name: MTEB Tatoeba (mar-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 89.8
- type: f1
value: 86.79
- type: precision
value: 85.375
- type: recall
value: 89.8
task:
type: BitextMining
- dataset:
config: lat-eng
name: MTEB Tatoeba (lat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 47.199999999999996
- type: f1
value: 39.95484348984349
- type: precision
value: 37.561071428571424
- type: recall
value: 47.199999999999996
task:
type: BitextMining
- dataset:
config: bel-eng
name: MTEB Tatoeba (bel-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 87.8
- type: f1
value: 84.68190476190475
- type: precision
value: 83.275
- type: recall
value: 87.8
task:
type: BitextMining
- dataset:
config: pms-eng
name: MTEB Tatoeba (pms-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 48.76190476190476
- type: f1
value: 42.14965986394558
- type: precision
value: 39.96743626743626
- type: recall
value: 48.76190476190476
task:
type: BitextMining
- dataset:
config: gle-eng
name: MTEB Tatoeba (gle-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 66.10000000000001
- type: f1
value: 59.58580086580086
- type: precision
value: 57.150238095238095
- type: recall
value: 66.10000000000001
task:
type: BitextMining
- dataset:
config: pes-eng
name: MTEB Tatoeba (pes-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 87.3
- type: f1
value: 84.0
- type: precision
value: 82.48666666666666
- type: recall
value: 87.3
task:
type: BitextMining
- dataset:
config: nob-eng
name: MTEB Tatoeba (nob-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 90.4
- type: f1
value: 87.79523809523809
- type: precision
value: 86.6
- type: recall
value: 90.4
task:
type: BitextMining
- dataset:
config: bul-eng
name: MTEB Tatoeba (bul-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 87.0
- type: f1
value: 83.81
- type: precision
value: 82.36666666666666
- type: recall
value: 87.0
task:
type: BitextMining
- dataset:
config: cbk-eng
name: MTEB Tatoeba (cbk-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 63.9
- type: f1
value: 57.76533189033189
- type: precision
value: 55.50595238095239
- type: recall
value: 63.9
task:
type: BitextMining
- dataset:
config: hun-eng
name: MTEB Tatoeba (hun-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 76.1
- type: f1
value: 71.83690476190478
- type: precision
value: 70.04928571428573
- type: recall
value: 76.1
task:
type: BitextMining
- dataset:
config: uig-eng
name: MTEB Tatoeba (uig-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 66.3
- type: f1
value: 59.32626984126984
- type: precision
value: 56.62535714285713
- type: recall
value: 66.3
task:
type: BitextMining
- dataset:
config: rus-eng
name: MTEB Tatoeba (rus-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 92.10000000000001
- type: f1
value: 89.76666666666667
- type: main_score
value: 89.76666666666667
- type: precision
value: 88.64999999999999
- type: recall
value: 92.10000000000001
task:
type: BitextMining
- dataset:
config: spa-eng
name: MTEB Tatoeba (spa-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 93.10000000000001
- type: f1
value: 91.10000000000001
- type: precision
value: 90.16666666666666
- type: recall
value: 93.10000000000001
task:
type: BitextMining
- dataset:
config: hye-eng
name: MTEB Tatoeba (hye-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 85.71428571428571
- type: f1
value: 82.29142600436403
- type: precision
value: 80.8076626877166
- type: recall
value: 85.71428571428571
task:
type: BitextMining
- dataset:
config: tel-eng
name: MTEB Tatoeba (tel-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 88.88888888888889
- type: f1
value: 85.7834757834758
- type: precision
value: 84.43732193732193
- type: recall
value: 88.88888888888889
task:
type: BitextMining
- dataset:
config: afr-eng
name: MTEB Tatoeba (afr-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 88.5
- type: f1
value: 85.67190476190476
- type: precision
value: 84.43333333333332
- type: recall
value: 88.5
task:
type: BitextMining
- dataset:
config: mon-eng
name: MTEB Tatoeba (mon-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 82.72727272727273
- type: f1
value: 78.21969696969695
- type: precision
value: 76.18181818181819
- type: recall
value: 82.72727272727273
task:
type: BitextMining
- dataset:
config: arz-eng
name: MTEB Tatoeba (arz-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 61.0062893081761
- type: f1
value: 55.13976240391334
- type: precision
value: 52.92112499659669
- type: recall
value: 61.0062893081761
task:
type: BitextMining
- dataset:
config: hrv-eng
name: MTEB Tatoeba (hrv-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 89.5
- type: f1
value: 86.86666666666666
- type: precision
value: 85.69166666666668
- type: recall
value: 89.5
task:
type: BitextMining
- dataset:
config: nov-eng
name: MTEB Tatoeba (nov-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 73.54085603112841
- type: f1
value: 68.56031128404669
- type: precision
value: 66.53047989623866
- type: recall
value: 73.54085603112841
task:
type: BitextMining
- dataset:
config: gsw-eng
name: MTEB Tatoeba (gsw-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 43.58974358974359
- type: f1
value: 36.45299145299145
- type: precision
value: 33.81155881155882
- type: recall
value: 43.58974358974359
task:
type: BitextMining
- dataset:
config: nds-eng
name: MTEB Tatoeba (nds-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 59.599999999999994
- type: f1
value: 53.264689754689755
- type: precision
value: 50.869166666666665
- type: recall
value: 59.599999999999994
task:
type: BitextMining
- dataset:
config: ukr-eng
name: MTEB Tatoeba (ukr-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 85.2
- type: f1
value: 81.61666666666665
- type: precision
value: 80.02833333333335
- type: recall
value: 85.2
task:
type: BitextMining
- dataset:
config: uzb-eng
name: MTEB Tatoeba (uzb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 63.78504672897196
- type: f1
value: 58.00029669188548
- type: precision
value: 55.815809968847354
- type: recall
value: 63.78504672897196
task:
type: BitextMining
- dataset:
config: lit-eng
name: MTEB Tatoeba (lit-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 66.5
- type: f1
value: 61.518333333333345
- type: precision
value: 59.622363699102834
- type: recall
value: 66.5
task:
type: BitextMining
- dataset:
config: ina-eng
name: MTEB Tatoeba (ina-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 88.6
- type: f1
value: 85.60222222222221
- type: precision
value: 84.27916666666665
- type: recall
value: 88.6
task:
type: BitextMining
- dataset:
config: lfn-eng
name: MTEB Tatoeba (lfn-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 58.699999999999996
- type: f1
value: 52.732375957375965
- type: precision
value: 50.63214035964035
- type: recall
value: 58.699999999999996
task:
type: BitextMining
- dataset:
config: zsm-eng
name: MTEB Tatoeba (zsm-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 92.10000000000001
- type: f1
value: 89.99666666666667
- type: precision
value: 89.03333333333333
- type: recall
value: 92.10000000000001
task:
type: BitextMining
- dataset:
config: ita-eng
name: MTEB Tatoeba (ita-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 90.10000000000001
- type: f1
value: 87.55666666666667
- type: precision
value: 86.36166666666668
- type: recall
value: 90.10000000000001
task:
type: BitextMining
- dataset:
config: cmn-eng
name: MTEB Tatoeba (cmn-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 88.89000000000001
- type: precision
value: 87.71166666666666
- type: recall
value: 91.4
task:
type: BitextMining
- dataset:
config: lvs-eng
name: MTEB Tatoeba (lvs-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 65.7
- type: f1
value: 60.67427750410509
- type: precision
value: 58.71785714285714
- type: recall
value: 65.7
task:
type: BitextMining
- dataset:
config: glg-eng
name: MTEB Tatoeba (glg-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 85.39999999999999
- type: f1
value: 81.93190476190475
- type: precision
value: 80.37833333333333
- type: recall
value: 85.39999999999999
task:
type: BitextMining
- dataset:
config: ceb-eng
name: MTEB Tatoeba (ceb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 47.833333333333336
- type: f1
value: 42.006625781625786
- type: precision
value: 40.077380952380956
- type: recall
value: 47.833333333333336
task:
type: BitextMining
- dataset:
config: bre-eng
name: MTEB Tatoeba (bre-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 10.4
- type: f1
value: 8.24465007215007
- type: precision
value: 7.664597069597071
- type: recall
value: 10.4
task:
type: BitextMining
- dataset:
config: ben-eng
name: MTEB Tatoeba (ben-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 82.6
- type: f1
value: 77.76333333333334
- type: precision
value: 75.57833333333332
- type: recall
value: 82.6
task:
type: BitextMining
- dataset:
config: swg-eng
name: MTEB Tatoeba (swg-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 52.67857142857143
- type: f1
value: 44.302721088435376
- type: precision
value: 41.49801587301587
- type: recall
value: 52.67857142857143
task:
type: BitextMining
- dataset:
config: arq-eng
name: MTEB Tatoeba (arq-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 28.3205268935236
- type: f1
value: 22.426666605171157
- type: precision
value: 20.685900116470915
- type: recall
value: 28.3205268935236
task:
type: BitextMining
- dataset:
config: kab-eng
name: MTEB Tatoeba (kab-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 22.7
- type: f1
value: 17.833970473970474
- type: precision
value: 16.407335164835164
- type: recall
value: 22.7
task:
type: BitextMining
- dataset:
config: fra-eng
name: MTEB Tatoeba (fra-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 92.2
- type: f1
value: 89.92999999999999
- type: precision
value: 88.87
- type: recall
value: 92.2
task:
type: BitextMining
- dataset:
config: por-eng
name: MTEB Tatoeba (por-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 89.25
- type: precision
value: 88.21666666666667
- type: recall
value: 91.4
task:
type: BitextMining
- dataset:
config: tat-eng
name: MTEB Tatoeba (tat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 69.19999999999999
- type: f1
value: 63.38269841269841
- type: precision
value: 61.14773809523809
- type: recall
value: 69.19999999999999
task:
type: BitextMining
- dataset:
config: oci-eng
name: MTEB Tatoeba (oci-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 48.8
- type: f1
value: 42.839915639915645
- type: precision
value: 40.770287114845935
- type: recall
value: 48.8
task:
type: BitextMining
- dataset:
config: pol-eng
name: MTEB Tatoeba (pol-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 88.8
- type: f1
value: 85.90666666666668
- type: precision
value: 84.54166666666666
- type: recall
value: 88.8
task:
type: BitextMining
- dataset:
config: war-eng
name: MTEB Tatoeba (war-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 46.6
- type: f1
value: 40.85892920804686
- type: precision
value: 38.838223114604695
- type: recall
value: 46.6
task:
type: BitextMining
- dataset:
config: aze-eng
name: MTEB Tatoeba (aze-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 84.0
- type: f1
value: 80.14190476190475
- type: precision
value: 78.45333333333333
- type: recall
value: 84.0
task:
type: BitextMining
- dataset:
config: vie-eng
name: MTEB Tatoeba (vie-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 90.5
- type: f1
value: 87.78333333333333
- type: precision
value: 86.5
- type: recall
value: 90.5
task:
type: BitextMining
- dataset:
config: nno-eng
name: MTEB Tatoeba (nno-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 74.5
- type: f1
value: 69.48397546897547
- type: precision
value: 67.51869047619049
- type: recall
value: 74.5
task:
type: BitextMining
- dataset:
config: cha-eng
name: MTEB Tatoeba (cha-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 32.846715328467155
- type: f1
value: 27.828177499710343
- type: precision
value: 26.63451511991658
- type: recall
value: 32.846715328467155
task:
type: BitextMining
- dataset:
config: mhr-eng
name: MTEB Tatoeba (mhr-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 8.0
- type: f1
value: 6.07664116764988
- type: precision
value: 5.544177607179943
- type: recall
value: 8.0
task:
type: BitextMining
- dataset:
config: dan-eng
name: MTEB Tatoeba (dan-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 87.6
- type: f1
value: 84.38555555555554
- type: precision
value: 82.91583333333334
- type: recall
value: 87.6
task:
type: BitextMining
- dataset:
config: ell-eng
name: MTEB Tatoeba (ell-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 87.5
- type: f1
value: 84.08333333333331
- type: precision
value: 82.47333333333333
- type: recall
value: 87.5
task:
type: BitextMining
- dataset:
config: amh-eng
name: MTEB Tatoeba (amh-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 80.95238095238095
- type: f1
value: 76.13095238095238
- type: precision
value: 74.05753968253967
- type: recall
value: 80.95238095238095
task:
type: BitextMining
- dataset:
config: pam-eng
name: MTEB Tatoeba (pam-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 8.799999999999999
- type: f1
value: 6.971422975172975
- type: precision
value: 6.557814916172301
- type: recall
value: 8.799999999999999
task:
type: BitextMining
- dataset:
config: hsb-eng
name: MTEB Tatoeba (hsb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 44.099378881987576
- type: f1
value: 37.01649742022413
- type: precision
value: 34.69420618488942
- type: recall
value: 44.099378881987576
task:
type: BitextMining
- dataset:
config: srp-eng
name: MTEB Tatoeba (srp-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 84.3
- type: f1
value: 80.32666666666667
- type: precision
value: 78.60666666666665
- type: recall
value: 84.3
task:
type: BitextMining
- dataset:
config: epo-eng
name: MTEB Tatoeba (epo-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 92.5
- type: f1
value: 90.49666666666666
- type: precision
value: 89.56666666666668
- type: recall
value: 92.5
task:
type: BitextMining
- dataset:
config: kzj-eng
name: MTEB Tatoeba (kzj-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 10.0
- type: f1
value: 8.268423529875141
- type: precision
value: 7.878118605532398
- type: recall
value: 10.0
task:
type: BitextMining
- dataset:
config: awa-eng
name: MTEB Tatoeba (awa-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 79.22077922077922
- type: f1
value: 74.27128427128426
- type: precision
value: 72.28715728715729
- type: recall
value: 79.22077922077922
task:
type: BitextMining
- dataset:
config: fao-eng
name: MTEB Tatoeba (fao-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 65.64885496183206
- type: f1
value: 58.87495456197747
- type: precision
value: 55.992366412213734
- type: recall
value: 65.64885496183206
task:
type: BitextMining
- dataset:
config: mal-eng
name: MTEB Tatoeba (mal-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 96.06986899563319
- type: f1
value: 94.78408539543909
- type: precision
value: 94.15332362930616
- type: recall
value: 96.06986899563319
task:
type: BitextMining
- dataset:
config: ile-eng
name: MTEB Tatoeba (ile-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 77.2
- type: f1
value: 71.72571428571428
- type: precision
value: 69.41000000000001
- type: recall
value: 77.2
task:
type: BitextMining
- dataset:
config: bos-eng
name: MTEB Tatoeba (bos-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 86.4406779661017
- type: f1
value: 83.2391713747646
- type: precision
value: 81.74199623352166
- type: recall
value: 86.4406779661017
task:
type: BitextMining
- dataset:
config: cor-eng
name: MTEB Tatoeba (cor-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 8.4
- type: f1
value: 6.017828743398003
- type: precision
value: 5.4829865484756795
- type: recall
value: 8.4
task:
type: BitextMining
- dataset:
config: cat-eng
name: MTEB Tatoeba (cat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 83.5
- type: f1
value: 79.74833333333333
- type: precision
value: 78.04837662337664
- type: recall
value: 83.5
task:
type: BitextMining
- dataset:
config: eus-eng
name: MTEB Tatoeba (eus-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 60.4
- type: f1
value: 54.467301587301584
- type: precision
value: 52.23242424242424
- type: recall
value: 60.4
task:
type: BitextMining
- dataset:
config: yue-eng
name: MTEB Tatoeba (yue-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 74.9
- type: f1
value: 69.68699134199134
- type: precision
value: 67.59873015873016
- type: recall
value: 74.9
task:
type: BitextMining
- dataset:
config: swe-eng
name: MTEB Tatoeba (swe-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 88.0
- type: f1
value: 84.9652380952381
- type: precision
value: 83.66166666666666
- type: recall
value: 88.0
task:
type: BitextMining
- dataset:
config: dtp-eng
name: MTEB Tatoeba (dtp-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 9.1
- type: f1
value: 7.681244588744588
- type: precision
value: 7.370043290043291
- type: recall
value: 9.1
task:
type: BitextMining
- dataset:
config: kat-eng
name: MTEB Tatoeba (kat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 80.9651474530831
- type: f1
value: 76.84220605132133
- type: precision
value: 75.19606398962966
- type: recall
value: 80.9651474530831
task:
type: BitextMining
- dataset:
config: jpn-eng
name: MTEB Tatoeba (jpn-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 86.9
- type: f1
value: 83.705
- type: precision
value: 82.3120634920635
- type: recall
value: 86.9
task:
type: BitextMining
- dataset:
config: csb-eng
name: MTEB Tatoeba (csb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 29.64426877470356
- type: f1
value: 23.98763072676116
- type: precision
value: 22.506399397703746
- type: recall
value: 29.64426877470356
task:
type: BitextMining
- dataset:
config: xho-eng
name: MTEB Tatoeba (xho-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 70.4225352112676
- type: f1
value: 62.84037558685445
- type: precision
value: 59.56572769953053
- type: recall
value: 70.4225352112676
task:
type: BitextMining
- dataset:
config: orv-eng
name: MTEB Tatoeba (orv-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 19.64071856287425
- type: f1
value: 15.125271011207756
- type: precision
value: 13.865019261197494
- type: recall
value: 19.64071856287425
task:
type: BitextMining
- dataset:
config: ind-eng
name: MTEB Tatoeba (ind-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 90.2
- type: f1
value: 87.80666666666666
- type: precision
value: 86.70833333333331
- type: recall
value: 90.2
task:
type: BitextMining
- dataset:
config: tuk-eng
name: MTEB Tatoeba (tuk-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 23.15270935960591
- type: f1
value: 18.407224958949097
- type: precision
value: 16.982385430661292
- type: recall
value: 23.15270935960591
task:
type: BitextMining
- dataset:
config: max-eng
name: MTEB Tatoeba (max-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 55.98591549295775
- type: f1
value: 49.94718309859154
- type: precision
value: 47.77864154624717
- type: recall
value: 55.98591549295775
task:
type: BitextMining
- dataset:
config: swh-eng
name: MTEB Tatoeba (swh-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 73.07692307692307
- type: f1
value: 66.74358974358974
- type: precision
value: 64.06837606837607
- type: recall
value: 73.07692307692307
task:
type: BitextMining
- dataset:
config: hin-eng
name: MTEB Tatoeba (hin-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.25
- type: precision
value: 92.43333333333332
- type: recall
value: 94.89999999999999
task:
type: BitextMining
- dataset:
config: dsb-eng
name: MTEB Tatoeba (dsb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 37.78705636743215
- type: f1
value: 31.63899658680452
- type: precision
value: 29.72264397629742
- type: recall
value: 37.78705636743215
task:
type: BitextMining
- dataset:
config: ber-eng
name: MTEB Tatoeba (ber-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 21.6
- type: f1
value: 16.91697302697303
- type: precision
value: 15.71225147075147
- type: recall
value: 21.6
task:
type: BitextMining
- dataset:
config: tam-eng
name: MTEB Tatoeba (tam-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 85.01628664495115
- type: f1
value: 81.38514037536838
- type: precision
value: 79.83170466883823
- type: recall
value: 85.01628664495115
task:
type: BitextMining
- dataset:
config: slk-eng
name: MTEB Tatoeba (slk-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 83.39999999999999
- type: f1
value: 79.96380952380952
- type: precision
value: 78.48333333333333
- type: recall
value: 83.39999999999999
task:
type: BitextMining
- dataset:
config: tgl-eng
name: MTEB Tatoeba (tgl-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 83.2
- type: f1
value: 79.26190476190476
- type: precision
value: 77.58833333333334
- type: recall
value: 83.2
task:
type: BitextMining
- dataset:
config: ast-eng
name: MTEB Tatoeba (ast-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 75.59055118110236
- type: f1
value: 71.66854143232096
- type: precision
value: 70.30183727034121
- type: recall
value: 75.59055118110236
task:
type: BitextMining
- dataset:
config: mkd-eng
name: MTEB Tatoeba (mkd-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 65.5
- type: f1
value: 59.26095238095238
- type: precision
value: 56.81909090909092
- type: recall
value: 65.5
task:
type: BitextMining
- dataset:
config: khm-eng
name: MTEB Tatoeba (khm-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 55.26315789473685
- type: f1
value: 47.986523325858506
- type: precision
value: 45.33950006595436
- type: recall
value: 55.26315789473685
task:
type: BitextMining
- dataset:
config: ces-eng
name: MTEB Tatoeba (ces-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 82.89999999999999
- type: f1
value: 78.835
- type: precision
value: 77.04761904761905
- type: recall
value: 82.89999999999999
task:
type: BitextMining
- dataset:
config: tzl-eng
name: MTEB Tatoeba (tzl-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 43.269230769230774
- type: f1
value: 36.20421245421245
- type: precision
value: 33.57371794871795
- type: recall
value: 43.269230769230774
task:
type: BitextMining
- dataset:
config: urd-eng
name: MTEB Tatoeba (urd-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 88.0
- type: f1
value: 84.70666666666666
- type: precision
value: 83.23166666666665
- type: recall
value: 88.0
task:
type: BitextMining
- dataset:
config: ara-eng
name: MTEB Tatoeba (ara-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 77.4
- type: f1
value: 72.54666666666667
- type: precision
value: 70.54318181818181
- type: recall
value: 77.4
task:
type: BitextMining
- dataset:
config: kor-eng
name: MTEB Tatoeba (kor-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 78.60000000000001
- type: f1
value: 74.1588888888889
- type: precision
value: 72.30250000000001
- type: recall
value: 78.60000000000001
task:
type: BitextMining
- dataset:
config: yid-eng
name: MTEB Tatoeba (yid-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 72.40566037735849
- type: f1
value: 66.82587328813744
- type: precision
value: 64.75039308176099
- type: recall
value: 72.40566037735849
task:
type: BitextMining
- dataset:
config: fin-eng
name: MTEB Tatoeba (fin-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 73.8
- type: f1
value: 68.56357142857144
- type: precision
value: 66.3178822055138
- type: recall
value: 73.8
task:
type: BitextMining
- dataset:
config: tha-eng
name: MTEB Tatoeba (tha-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 91.78832116788321
- type: f1
value: 89.3552311435523
- type: precision
value: 88.20559610705597
- type: recall
value: 91.78832116788321
task:
type: BitextMining
- dataset:
config: wuu-eng
name: MTEB Tatoeba (wuu-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 74.3
- type: f1
value: 69.05085581085581
- type: precision
value: 66.955
- type: recall
value: 74.3
task:
type: BitextMining
- dataset:
config: default
name: MTEB Touche2020
revision: None
split: test
type: webis-touche2020
metrics:
- type: map_at_1
value: 2.896
- type: map_at_10
value: 8.993
- type: map_at_100
value: 14.133999999999999
- type: map_at_1000
value: 15.668000000000001
- type: map_at_3
value: 5.862
- type: map_at_5
value: 7.17
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 42.931000000000004
- type: mrr_at_100
value: 44.81
- type: mrr_at_1000
value: 44.81
- type: mrr_at_3
value: 38.435
- type: mrr_at_5
value: 41.701
- type: ndcg_at_1
value: 31.633
- type: ndcg_at_10
value: 21.163
- type: ndcg_at_100
value: 33.306000000000004
- type: ndcg_at_1000
value: 45.275999999999996
- type: ndcg_at_3
value: 25.685999999999996
- type: ndcg_at_5
value: 23.732
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 17.755000000000003
- type: precision_at_100
value: 6.938999999999999
- type: precision_at_1000
value: 1.48
- type: precision_at_3
value: 25.85
- type: precision_at_5
value: 23.265
- type: recall_at_1
value: 2.896
- type: recall_at_10
value: 13.333999999999998
- type: recall_at_100
value: 43.517
- type: recall_at_1000
value: 79.836
- type: recall_at_3
value: 6.306000000000001
- type: recall_at_5
value: 8.825
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 69.3874
- type: ap
value: 13.829909072469423
- type: f1
value: 53.54534203543492
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 62.62026032823995
- type: f1
value: 62.85251350485221
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 33.21527881409797
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cos_sim_accuracy
value: 84.97943613280086
- type: cos_sim_ap
value: 70.75454316885921
- type: cos_sim_f1
value: 65.38274012676743
- type: cos_sim_precision
value: 60.761214318078835
- type: cos_sim_recall
value: 70.76517150395777
- type: dot_accuracy
value: 79.0546581629612
- type: dot_ap
value: 47.3197121792147
- type: dot_f1
value: 49.20106524633821
- type: dot_precision
value: 42.45499808502489
- type: dot_recall
value: 58.49604221635884
- type: euclidean_accuracy
value: 85.08076533349228
- type: euclidean_ap
value: 70.95016106374474
- type: euclidean_f1
value: 65.43987900176455
- type: euclidean_precision
value: 62.64478764478765
- type: euclidean_recall
value: 68.49604221635884
- type: manhattan_accuracy
value: 84.93771234428085
- type: manhattan_ap
value: 70.63668388755362
- type: manhattan_f1
value: 65.23895401262398
- type: manhattan_precision
value: 56.946084218811485
- type: manhattan_recall
value: 76.35883905013192
- type: max_accuracy
value: 85.08076533349228
- type: max_ap
value: 70.95016106374474
- type: max_f1
value: 65.43987900176455
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
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value: 88.69096130709822
- type: cos_sim_ap
value: 84.82526278228542
- type: cos_sim_f1
value: 77.65485060585536
- type: cos_sim_precision
value: 75.94582658619167
- type: cos_sim_recall
value: 79.44256236526024
- type: dot_accuracy
value: 80.97954748321496
- type: dot_ap
value: 64.81642914145866
- type: dot_f1
value: 60.631996987229975
- type: dot_precision
value: 54.5897293631712
- type: dot_recall
value: 68.17831844779796
- type: euclidean_accuracy
value: 88.6987231730508
- type: euclidean_ap
value: 84.80003825477253
- type: euclidean_f1
value: 77.67194179854496
- type: euclidean_precision
value: 75.7128235122094
- type: euclidean_recall
value: 79.73514012935017
- type: manhattan_accuracy
value: 88.62692591298949
- type: manhattan_ap
value: 84.80451408255276
- type: manhattan_f1
value: 77.69888949572183
- type: manhattan_precision
value: 73.70311528631622
- type: manhattan_recall
value: 82.15275639051433
- type: max_accuracy
value: 88.6987231730508
- type: max_ap
value: 84.82526278228542
- type: max_f1
value: 77.69888949572183
task:
type: PairClassification
- dataset:
config: ru-en
name: MTEB BUCC.v2 (ru-en)
revision: 1739dc11ffe9b7bfccd7f3d585aeb4c544fc6677
split: test
type: mteb/bucc-bitext-mining
metrics:
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value: 94.42443135896548
- type: main_score
value: 94.42443135896548
- type: precision
value: 93.80868260016165
- type: recall
value: 95.72566678212678
task:
type: BitextMining
- dataset:
config: rus_Cyrl-rus_Cyrl
name: MTEB BelebeleRetrieval (rus_Cyrl-rus_Cyrl)
revision: 75b399394a9803252cfec289d103de462763db7c
split: test
type: facebook/belebele
metrics:
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value: 87.111
- type: map_at_10
value: 90.717
- type: map_at_100
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- type: map_at_1000
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- type: map_at_20
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- type: map_at_3
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- type: map_at_5
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- type: nauc_recall_at_100_diff1
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value: 100.0
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value: 70.71661998132666
- type: nauc_recall_at_10_diff1
value: 90.13938908896864
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- type: recall_at_1
value: 87.111
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- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 98.556
- type: recall_at_3
value: 93.556
- type: recall_at_5
value: 95.556
task:
type: Retrieval
- dataset:
config: rus_Cyrl-eng_Latn
name: MTEB BelebeleRetrieval (rus_Cyrl-eng_Latn)
revision: 75b399394a9803252cfec289d103de462763db7c
split: test
type: facebook/belebele
metrics:
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value: 100.0
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value: 87.444
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value: 91.889
task:
type: Retrieval
- dataset:
config: eng_Latn-rus_Cyrl
name: MTEB BelebeleRetrieval (eng_Latn-rus_Cyrl)
revision: 75b399394a9803252cfec289d103de462763db7c
split: test
type: facebook/belebele
metrics:
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value: 82.748
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value: 79.857
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value: 80.219
- type: map_at_1000
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value: 4.708911881992707
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value: 83.62920965453047
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value: 83.646
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value: 80.267
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value: 81.922
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value: 73.444
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value: 9.167
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value: 0.992
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value: 4.761
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value: 28.37
- type: precision_at_5
value: 17.822
- type: recall_at_1
value: 73.444
- type: recall_at_10
value: 91.667
- type: recall_at_100
value: 99.222
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 95.222
- type: recall_at_3
value: 85.111
- type: recall_at_5
value: 89.11099999999999
task:
type: Retrieval
- dataset:
config: eng_Latn-rus_Cyrl
name: MTEB BibleNLPBitextMining (eng_Latn-rus_Cyrl)
revision: 264a18480c529d9e922483839b4b9758e690b762
split: train
type: davidstap/biblenlp-corpus-mmteb
metrics:
- type: accuracy
value: 96.875
- type: f1
value: 95.83333333333333
- type: main_score
value: 95.83333333333333
- type: precision
value: 95.3125
- type: recall
value: 96.875
task:
type: BitextMining
- dataset:
config: rus_Cyrl-eng_Latn
name: MTEB BibleNLPBitextMining (rus_Cyrl-eng_Latn)
revision: 264a18480c529d9e922483839b4b9758e690b762
split: train
type: davidstap/biblenlp-corpus-mmteb
metrics:
- type: accuracy
value: 88.671875
- type: f1
value: 85.3515625
- type: main_score
value: 85.3515625
- type: precision
value: 83.85416666666667
- type: recall
value: 88.671875
task:
type: BitextMining
- dataset:
config: default
name: MTEB CEDRClassification (default)
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
split: test
type: ai-forever/cedr-classification
metrics:
- type: accuracy
value: 40.06907545164719
- type: f1
value: 26.285000550712407
- type: lrap
value: 64.4280021253997
- type: main_score
value: 40.06907545164719
task:
type: MultilabelClassification
- dataset:
config: default
name: MTEB CyrillicTurkicLangClassification (default)
revision: e42d330f33d65b7b72dfd408883daf1661f06f18
split: test
type: tatiana-merz/cyrillic_turkic_langs
metrics:
- type: accuracy
value: 43.3447265625
- type: f1
value: 40.08400146827895
- type: f1_weighted
value: 40.08499428040896
- type: main_score
value: 43.3447265625
task:
type: Classification
- dataset:
config: ace_Arab-rus_Cyrl
name: MTEB FloresBitextMining (ace_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 6.225296442687747
- type: f1
value: 5.5190958860075
- type: main_score
value: 5.5190958860075
- type: precision
value: 5.3752643758000005
- type: recall
value: 6.225296442687747
task:
type: BitextMining
- dataset:
config: bam_Latn-rus_Cyrl
name: MTEB FloresBitextMining (bam_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 68.37944664031622
- type: f1
value: 64.54819836666252
- type: main_score
value: 64.54819836666252
- type: precision
value: 63.07479233454916
- type: recall
value: 68.37944664031622
task:
type: BitextMining
- dataset:
config: dzo_Tibt-rus_Cyrl
name: MTEB FloresBitextMining (dzo_Tibt-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 0.09881422924901186
- type: f1
value: 0.00019509225912934226
- type: main_score
value: 0.00019509225912934226
- type: precision
value: 9.76425190207627e-05
- type: recall
value: 0.09881422924901186
task:
type: BitextMining
- dataset:
config: hin_Deva-rus_Cyrl
name: MTEB FloresBitextMining (hin_Deva-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.47299077733861
- type: main_score
value: 99.47299077733861
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
task:
type: BitextMining
- dataset:
config: khm_Khmr-rus_Cyrl
name: MTEB FloresBitextMining (khm_Khmr-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 88.83399209486166
- type: f1
value: 87.71151056318254
- type: main_score
value: 87.71151056318254
- type: precision
value: 87.32012500709193
- type: recall
value: 88.83399209486166
task:
type: BitextMining
- dataset:
config: mag_Deva-rus_Cyrl
name: MTEB FloresBitextMining (mag_Deva-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.7239789196311
- type: main_score
value: 97.7239789196311
- type: precision
value: 97.61904761904762
- type: recall
value: 98.02371541501977
task:
type: BitextMining
- dataset:
config: pap_Latn-rus_Cyrl
name: MTEB FloresBitextMining (pap_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 94.0711462450593
- type: f1
value: 93.68187806922984
- type: main_score
value: 93.68187806922984
- type: precision
value: 93.58925452707051
- type: recall
value: 94.0711462450593
task:
type: BitextMining
- dataset:
config: sot_Latn-rus_Cyrl
name: MTEB FloresBitextMining (sot_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 90.9090909090909
- type: f1
value: 89.23171936758892
- type: main_score
value: 89.23171936758892
- type: precision
value: 88.51790014083866
- type: recall
value: 90.9090909090909
task:
type: BitextMining
- dataset:
config: tur_Latn-rus_Cyrl
name: MTEB FloresBitextMining (tur_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
task:
type: BitextMining
- dataset:
config: ace_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ace_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 66.10671936758892
- type: f1
value: 63.81888256297873
- type: main_score
value: 63.81888256297873
- type: precision
value: 63.01614067933451
- type: recall
value: 66.10671936758892
task:
type: BitextMining
- dataset:
config: ban_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ban_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 79.44664031620553
- type: f1
value: 77.6311962082713
- type: main_score
value: 77.6311962082713
- type: precision
value: 76.93977931929739
- type: recall
value: 79.44664031620553
task:
type: BitextMining
- dataset:
config: ell_Grek-rus_Cyrl
name: MTEB FloresBitextMining (ell_Grek-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.2094861660079
- type: main_score
value: 99.2094861660079
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
task:
type: BitextMining
- dataset:
config: hne_Deva-rus_Cyrl
name: MTEB FloresBitextMining (hne_Deva-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.83794466403161
- type: f1
value: 96.25352907961603
- type: main_score
value: 96.25352907961603
- type: precision
value: 96.02155091285526
- type: recall
value: 96.83794466403161
task:
type: BitextMining
- dataset:
config: kik_Latn-rus_Cyrl
name: MTEB FloresBitextMining (kik_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 76.28458498023716
- type: f1
value: 73.5596919895859
- type: main_score
value: 73.5596919895859
- type: precision
value: 72.40900759055246
- type: recall
value: 76.28458498023716
task:
type: BitextMining
- dataset:
config: mai_Deva-rus_Cyrl
name: MTEB FloresBitextMining (mai_Deva-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.72727272727273
- type: f1
value: 97.37812911725956
- type: main_score
value: 97.37812911725956
- type: precision
value: 97.26002258610953
- type: recall
value: 97.72727272727273
task:
type: BitextMining
- dataset:
config: pbt_Arab-rus_Cyrl
name: MTEB FloresBitextMining (pbt_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 94.0711462450593
- type: f1
value: 93.34700387331966
- type: main_score
value: 93.34700387331966
- type: precision
value: 93.06920556920556
- type: recall
value: 94.0711462450593
task:
type: BitextMining
- dataset:
config: spa_Latn-rus_Cyrl
name: MTEB FloresBitextMining (spa_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
task:
type: BitextMining
- dataset:
config: twi_Latn-rus_Cyrl
name: MTEB FloresBitextMining (twi_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 80.73122529644269
- type: f1
value: 77.77434363246721
- type: main_score
value: 77.77434363246721
- type: precision
value: 76.54444287596462
- type: recall
value: 80.73122529644269
task:
type: BitextMining
- dataset:
config: acm_Arab-rus_Cyrl
name: MTEB FloresBitextMining (acm_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 94.56521739130434
- type: f1
value: 92.92490118577075
- type: main_score
value: 92.92490118577075
- type: precision
value: 92.16897233201581
- type: recall
value: 94.56521739130434
task:
type: BitextMining
- dataset:
config: bel_Cyrl-rus_Cyrl
name: MTEB FloresBitextMining (bel_Cyrl-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.98550724637681
- type: main_score
value: 98.98550724637681
- type: precision
value: 98.88833992094862
- type: recall
value: 99.2094861660079
task:
type: BitextMining
- dataset:
config: eng_Latn-rus_Cyrl
name: MTEB FloresBitextMining (eng_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.4729907773386
- type: main_score
value: 99.4729907773386
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
task:
type: BitextMining
- dataset:
config: hrv_Latn-rus_Cyrl
name: MTEB FloresBitextMining (hrv_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 99.05138339920948
- type: main_score
value: 99.05138339920948
- type: precision
value: 99.00691699604744
- type: recall
value: 99.2094861660079
task:
type: BitextMining
- dataset:
config: kin_Latn-rus_Cyrl
name: MTEB FloresBitextMining (kin_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 88.2411067193676
- type: f1
value: 86.5485246227658
- type: main_score
value: 86.5485246227658
- type: precision
value: 85.90652101521667
- type: recall
value: 88.2411067193676
task:
type: BitextMining
- dataset:
config: mal_Mlym-rus_Cyrl
name: MTEB FloresBitextMining (mal_Mlym-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.51778656126481
- type: f1
value: 98.07971014492753
- type: main_score
value: 98.07971014492753
- type: precision
value: 97.88372859025033
- type: recall
value: 98.51778656126481
task:
type: BitextMining
- dataset:
config: pes_Arab-rus_Cyrl
name: MTEB FloresBitextMining (pes_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.51778656126481
- type: f1
value: 98.0566534914361
- type: main_score
value: 98.0566534914361
- type: precision
value: 97.82608695652173
- type: recall
value: 98.51778656126481
task:
type: BitextMining
- dataset:
config: srd_Latn-rus_Cyrl
name: MTEB FloresBitextMining (srd_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 82.6086956521739
- type: f1
value: 80.9173470979821
- type: main_score
value: 80.9173470979821
- type: precision
value: 80.24468672882627
- type: recall
value: 82.6086956521739
task:
type: BitextMining
- dataset:
config: tzm_Tfng-rus_Cyrl
name: MTEB FloresBitextMining (tzm_Tfng-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 7.41106719367589
- type: f1
value: 6.363562740945329
- type: main_score
value: 6.363562740945329
- type: precision
value: 6.090373175353411
- type: recall
value: 7.41106719367589
task:
type: BitextMining
- dataset:
config: acq_Arab-rus_Cyrl
name: MTEB FloresBitextMining (acq_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.25691699604744
- type: f1
value: 93.81422924901187
- type: main_score
value: 93.81422924901187
- type: precision
value: 93.14064558629775
- type: recall
value: 95.25691699604744
task:
type: BitextMining
- dataset:
config: bem_Latn-rus_Cyrl
name: MTEB FloresBitextMining (bem_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 68.08300395256917
- type: f1
value: 65.01368772860867
- type: main_score
value: 65.01368772860867
- type: precision
value: 63.91052337510628
- type: recall
value: 68.08300395256917
task:
type: BitextMining
- dataset:
config: epo_Latn-rus_Cyrl
name: MTEB FloresBitextMining (epo_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.41897233201581
- type: f1
value: 98.17193675889328
- type: main_score
value: 98.17193675889328
- type: precision
value: 98.08210564139418
- type: recall
value: 98.41897233201581
task:
type: BitextMining
- dataset:
config: hun_Latn-rus_Cyrl
name: MTEB FloresBitextMining (hun_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.1106719367589
- type: main_score
value: 99.1106719367589
- type: precision
value: 99.01185770750988
- type: recall
value: 99.30830039525692
task:
type: BitextMining
- dataset:
config: kir_Cyrl-rus_Cyrl
name: MTEB FloresBitextMining (kir_Cyrl-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.5296442687747
- type: f1
value: 97.07549806364035
- type: main_score
value: 97.07549806364035
- type: precision
value: 96.90958498023716
- type: recall
value: 97.5296442687747
task:
type: BitextMining
- dataset:
config: mar_Deva-rus_Cyrl
name: MTEB FloresBitextMining (mar_Deva-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.44400527009222
- type: main_score
value: 97.44400527009222
- type: precision
value: 97.28966685488425
- type: recall
value: 97.82608695652173
task:
type: BitextMining
- dataset:
config: plt_Latn-rus_Cyrl
name: MTEB FloresBitextMining (plt_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 79.9407114624506
- type: f1
value: 78.3154177760691
- type: main_score
value: 78.3154177760691
- type: precision
value: 77.69877344877344
- type: recall
value: 79.9407114624506
task:
type: BitextMining
- dataset:
config: srp_Cyrl-rus_Cyrl
name: MTEB FloresBitextMining (srp_Cyrl-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.70355731225297
- type: f1
value: 99.60474308300395
- type: main_score
value: 99.60474308300395
- type: precision
value: 99.55533596837944
- type: recall
value: 99.70355731225297
task:
type: BitextMining
- dataset:
config: uig_Arab-rus_Cyrl
name: MTEB FloresBitextMining (uig_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 83.20158102766798
- type: f1
value: 81.44381923034585
- type: main_score
value: 81.44381923034585
- type: precision
value: 80.78813411582477
- type: recall
value: 83.20158102766798
task:
type: BitextMining
- dataset:
config: aeb_Arab-rus_Cyrl
name: MTEB FloresBitextMining (aeb_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 91.20553359683794
- type: f1
value: 88.75352907961603
- type: main_score
value: 88.75352907961603
- type: precision
value: 87.64328063241106
- type: recall
value: 91.20553359683794
task:
type: BitextMining
- dataset:
config: ben_Beng-rus_Cyrl
name: MTEB FloresBitextMining (ben_Beng-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.60671936758894
- type: main_score
value: 98.60671936758894
- type: precision
value: 98.4766139657444
- type: recall
value: 98.91304347826086
task:
type: BitextMining
- dataset:
config: est_Latn-rus_Cyrl
name: MTEB FloresBitextMining (est_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.24505928853755
- type: f1
value: 95.27417027417027
- type: main_score
value: 95.27417027417027
- type: precision
value: 94.84107378129117
- type: recall
value: 96.24505928853755
task:
type: BitextMining
- dataset:
config: hye_Armn-rus_Cyrl
name: MTEB FloresBitextMining (hye_Armn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.67786561264822
- type: main_score
value: 97.67786561264822
- type: precision
value: 97.55839022637441
- type: recall
value: 98.02371541501977
task:
type: BitextMining
- dataset:
config: kmb_Latn-rus_Cyrl
name: MTEB FloresBitextMining (kmb_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 46.047430830039524
- type: f1
value: 42.94464804804471
- type: main_score
value: 42.94464804804471
- type: precision
value: 41.9851895607238
- type: recall
value: 46.047430830039524
task:
type: BitextMining
- dataset:
config: min_Arab-rus_Cyrl
name: MTEB FloresBitextMining (min_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 3.9525691699604746
- type: f1
value: 3.402665192725756
- type: main_score
value: 3.402665192725756
- type: precision
value: 3.303787557740127
- type: recall
value: 3.9525691699604746
task:
type: BitextMining
- dataset:
config: pol_Latn-rus_Cyrl
name: MTEB FloresBitextMining (pol_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.4729907773386
- type: main_score
value: 99.4729907773386
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
task:
type: BitextMining
- dataset:
config: ssw_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ssw_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 73.22134387351778
- type: f1
value: 70.43086049508975
- type: main_score
value: 70.43086049508975
- type: precision
value: 69.35312022355656
- type: recall
value: 73.22134387351778
task:
type: BitextMining
- dataset:
config: ukr_Cyrl-rus_Cyrl
name: MTEB FloresBitextMining (ukr_Cyrl-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.90118577075098
- type: f1
value: 99.86824769433464
- type: main_score
value: 99.86824769433464
- type: precision
value: 99.85177865612648
- type: recall
value: 99.90118577075098
task:
type: BitextMining
- dataset:
config: afr_Latn-rus_Cyrl
name: MTEB FloresBitextMining (afr_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
task:
type: BitextMining
- dataset:
config: bho_Deva-rus_Cyrl
name: MTEB FloresBitextMining (bho_Deva-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 94.0711462450593
- type: f1
value: 93.12182382834557
- type: main_score
value: 93.12182382834557
- type: precision
value: 92.7523453232338
- type: recall
value: 94.0711462450593
task:
type: BitextMining
- dataset:
config: eus_Latn-rus_Cyrl
name: MTEB FloresBitextMining (eus_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 92.19367588932806
- type: f1
value: 91.23604975587072
- type: main_score
value: 91.23604975587072
- type: precision
value: 90.86697443588663
- type: recall
value: 92.19367588932806
task:
type: BitextMining
- dataset:
config: ibo_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ibo_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 82.21343873517787
- type: f1
value: 80.17901604858126
- type: main_score
value: 80.17901604858126
- type: precision
value: 79.3792284780028
- type: recall
value: 82.21343873517787
task:
type: BitextMining
- dataset:
config: kmr_Latn-rus_Cyrl
name: MTEB FloresBitextMining (kmr_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 68.67588932806325
- type: f1
value: 66.72311714750278
- type: main_score
value: 66.72311714750278
- type: precision
value: 66.00178401554004
- type: recall
value: 68.67588932806325
task:
type: BitextMining
- dataset:
config: min_Latn-rus_Cyrl
name: MTEB FloresBitextMining (min_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 78.65612648221344
- type: f1
value: 76.26592719972166
- type: main_score
value: 76.26592719972166
- type: precision
value: 75.39980459997484
- type: recall
value: 78.65612648221344
task:
type: BitextMining
- dataset:
config: por_Latn-rus_Cyrl
name: MTEB FloresBitextMining (por_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.83794466403161
- type: f1
value: 95.9669678147939
- type: main_score
value: 95.9669678147939
- type: precision
value: 95.59453227931488
- type: recall
value: 96.83794466403161
task:
type: BitextMining
- dataset:
config: sun_Latn-rus_Cyrl
name: MTEB FloresBitextMining (sun_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 92.4901185770751
- type: f1
value: 91.66553983773662
- type: main_score
value: 91.66553983773662
- type: precision
value: 91.34530928009188
- type: recall
value: 92.4901185770751
task:
type: BitextMining
- dataset:
config: umb_Latn-rus_Cyrl
name: MTEB FloresBitextMining (umb_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 41.00790513833992
- type: f1
value: 38.21319326004483
- type: main_score
value: 38.21319326004483
- type: precision
value: 37.200655467675546
- type: recall
value: 41.00790513833992
task:
type: BitextMining
- dataset:
config: ajp_Arab-rus_Cyrl
name: MTEB FloresBitextMining (ajp_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.35573122529645
- type: f1
value: 93.97233201581028
- type: main_score
value: 93.97233201581028
- type: precision
value: 93.33333333333333
- type: recall
value: 95.35573122529645
task:
type: BitextMining
- dataset:
config: bjn_Arab-rus_Cyrl
name: MTEB FloresBitextMining (bjn_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 3.6561264822134385
- type: f1
value: 3.1071978056336484
- type: main_score
value: 3.1071978056336484
- type: precision
value: 3.0039741229718215
- type: recall
value: 3.6561264822134385
task:
type: BitextMining
- dataset:
config: ewe_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ewe_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 62.845849802371546
- type: f1
value: 59.82201175670472
- type: main_score
value: 59.82201175670472
- type: precision
value: 58.72629236362003
- type: recall
value: 62.845849802371546
task:
type: BitextMining
- dataset:
config: ilo_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ilo_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 83.10276679841897
- type: f1
value: 80.75065288987582
- type: main_score
value: 80.75065288987582
- type: precision
value: 79.80726451662179
- type: recall
value: 83.10276679841897
task:
type: BitextMining
- dataset:
config: knc_Arab-rus_Cyrl
name: MTEB FloresBitextMining (knc_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 10.079051383399209
- type: f1
value: 8.759282456080921
- type: main_score
value: 8.759282456080921
- type: precision
value: 8.474735138956142
- type: recall
value: 10.079051383399209
task:
type: BitextMining
- dataset:
config: mkd_Cyrl-rus_Cyrl
name: MTEB FloresBitextMining (mkd_Cyrl-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.55072463768116
- type: main_score
value: 98.55072463768116
- type: precision
value: 98.36956521739131
- type: recall
value: 98.91304347826086
task:
type: BitextMining
- dataset:
config: prs_Arab-rus_Cyrl
name: MTEB FloresBitextMining (prs_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.68247694334651
- type: main_score
value: 98.68247694334651
- type: precision
value: 98.51778656126481
- type: recall
value: 99.01185770750988
task:
type: BitextMining
- dataset:
config: swe_Latn-rus_Cyrl
name: MTEB FloresBitextMining (swe_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.22595520421606
- type: main_score
value: 99.22595520421606
- type: precision
value: 99.14361001317523
- type: recall
value: 99.40711462450594
task:
type: BitextMining
- dataset:
config: urd_Arab-rus_Cyrl
name: MTEB FloresBitextMining (urd_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.25625823451911
- type: main_score
value: 97.25625823451911
- type: precision
value: 97.03063241106719
- type: recall
value: 97.82608695652173
task:
type: BitextMining
- dataset:
config: aka_Latn-rus_Cyrl
name: MTEB FloresBitextMining (aka_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 81.22529644268775
- type: f1
value: 77.94307687941227
- type: main_score
value: 77.94307687941227
- type: precision
value: 76.58782793293665
- type: recall
value: 81.22529644268775
task:
type: BitextMining
- dataset:
config: bjn_Latn-rus_Cyrl
name: MTEB FloresBitextMining (bjn_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 85.27667984189723
- type: f1
value: 83.6869192829922
- type: main_score
value: 83.6869192829922
- type: precision
value: 83.08670670691656
- type: recall
value: 85.27667984189723
task:
type: BitextMining
- dataset:
config: fao_Latn-rus_Cyrl
name: MTEB FloresBitextMining (fao_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 80.9288537549407
- type: f1
value: 79.29806087454745
- type: main_score
value: 79.29806087454745
- type: precision
value: 78.71445871526987
- type: recall
value: 80.9288537549407
task:
type: BitextMining
- dataset:
config: ind_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ind_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.12252964426878
- type: f1
value: 97.5296442687747
- type: main_score
value: 97.5296442687747
- type: precision
value: 97.23320158102767
- type: recall
value: 98.12252964426878
task:
type: BitextMining
- dataset:
config: knc_Latn-rus_Cyrl
name: MTEB FloresBitextMining (knc_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 33.49802371541502
- type: f1
value: 32.02378215033989
- type: main_score
value: 32.02378215033989
- type: precision
value: 31.511356103747406
- type: recall
value: 33.49802371541502
task:
type: BitextMining
- dataset:
config: mlt_Latn-rus_Cyrl
name: MTEB FloresBitextMining (mlt_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 91.40316205533597
- type: f1
value: 90.35317684386006
- type: main_score
value: 90.35317684386006
- type: precision
value: 89.94845939633488
- type: recall
value: 91.40316205533597
task:
type: BitextMining
- dataset:
config: quy_Latn-rus_Cyrl
name: MTEB FloresBitextMining (quy_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 40.612648221343875
- type: f1
value: 38.74337544712602
- type: main_score
value: 38.74337544712602
- type: precision
value: 38.133716022178575
- type: recall
value: 40.612648221343875
task:
type: BitextMining
- dataset:
config: swh_Latn-rus_Cyrl
name: MTEB FloresBitextMining (swh_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.13438735177866
- type: f1
value: 96.47435897435898
- type: main_score
value: 96.47435897435898
- type: precision
value: 96.18741765480895
- type: recall
value: 97.13438735177866
task:
type: BitextMining
- dataset:
config: uzn_Latn-rus_Cyrl
name: MTEB FloresBitextMining (uzn_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.83794466403161
- type: f1
value: 96.26355528529442
- type: main_score
value: 96.26355528529442
- type: precision
value: 96.0501756697409
- type: recall
value: 96.83794466403161
task:
type: BitextMining
- dataset:
config: als_Latn-rus_Cyrl
name: MTEB FloresBitextMining (als_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.6907114624506
- type: main_score
value: 98.6907114624506
- type: precision
value: 98.6142480707698
- type: recall
value: 98.91304347826086
task:
type: BitextMining
- dataset:
config: bod_Tibt-rus_Cyrl
name: MTEB FloresBitextMining (bod_Tibt-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 1.0869565217391304
- type: f1
value: 0.9224649610442628
- type: main_score
value: 0.9224649610442628
- type: precision
value: 0.8894275740459898
- type: recall
value: 1.0869565217391304
task:
type: BitextMining
- dataset:
config: fij_Latn-rus_Cyrl
name: MTEB FloresBitextMining (fij_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 63.24110671936759
- type: f1
value: 60.373189068189525
- type: main_score
value: 60.373189068189525
- type: precision
value: 59.32326368115546
- type: recall
value: 63.24110671936759
task:
type: BitextMining
- dataset:
config: isl_Latn-rus_Cyrl
name: MTEB FloresBitextMining (isl_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 89.03162055335969
- type: f1
value: 87.3102634715907
- type: main_score
value: 87.3102634715907
- type: precision
value: 86.65991814698712
- type: recall
value: 89.03162055335969
task:
type: BitextMining
- dataset:
config: kon_Latn-rus_Cyrl
name: MTEB FloresBitextMining (kon_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 73.91304347826086
- type: f1
value: 71.518235523573
- type: main_score
value: 71.518235523573
- type: precision
value: 70.58714102449801
- type: recall
value: 73.91304347826086
task:
type: BitextMining
- dataset:
config: mni_Beng-rus_Cyrl
name: MTEB FloresBitextMining (mni_Beng-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 29.545454545454547
- type: f1
value: 27.59513619889114
- type: main_score
value: 27.59513619889114
- type: precision
value: 26.983849851025344
- type: recall
value: 29.545454545454547
task:
type: BitextMining
- dataset:
config: ron_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ron_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.2094861660079
- type: main_score
value: 99.2094861660079
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
task:
type: BitextMining
- dataset:
config: szl_Latn-rus_Cyrl
name: MTEB FloresBitextMining (szl_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 86.26482213438736
- type: f1
value: 85.18912031587512
- type: main_score
value: 85.18912031587512
- type: precision
value: 84.77199409959775
- type: recall
value: 86.26482213438736
task:
type: BitextMining
- dataset:
config: vec_Latn-rus_Cyrl
name: MTEB FloresBitextMining (vec_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 85.67193675889328
- type: f1
value: 84.62529734716581
- type: main_score
value: 84.62529734716581
- type: precision
value: 84.2611422440705
- type: recall
value: 85.67193675889328
task:
type: BitextMining
- dataset:
config: amh_Ethi-rus_Cyrl
name: MTEB FloresBitextMining (amh_Ethi-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 94.76284584980237
- type: f1
value: 93.91735076517685
- type: main_score
value: 93.91735076517685
- type: precision
value: 93.57553798858147
- type: recall
value: 94.76284584980237
task:
type: BitextMining
- dataset:
config: bos_Latn-rus_Cyrl
name: MTEB FloresBitextMining (bos_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 99.05655938264634
- type: main_score
value: 99.05655938264634
- type: precision
value: 99.01185770750988
- type: recall
value: 99.2094861660079
task:
type: BitextMining
- dataset:
config: fin_Latn-rus_Cyrl
name: MTEB FloresBitextMining (fin_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.43741765480895
- type: main_score
value: 97.43741765480895
- type: precision
value: 97.1590909090909
- type: recall
value: 98.02371541501977
task:
type: BitextMining
- dataset:
config: ita_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ita_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.70355731225297
- type: f1
value: 99.60474308300395
- type: main_score
value: 99.60474308300395
- type: precision
value: 99.55533596837944
- type: recall
value: 99.70355731225297
task:
type: BitextMining
- dataset:
config: kor_Hang-rus_Cyrl
name: MTEB FloresBitextMining (kor_Hang-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.33201581027669
- type: f1
value: 96.49868247694334
- type: main_score
value: 96.49868247694334
- type: precision
value: 96.10507246376811
- type: recall
value: 97.33201581027669
task:
type: BitextMining
- dataset:
config: mos_Latn-rus_Cyrl
name: MTEB FloresBitextMining (mos_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 34.683794466403164
- type: f1
value: 32.766819308009076
- type: main_score
value: 32.766819308009076
- type: precision
value: 32.1637493670237
- type: recall
value: 34.683794466403164
task:
type: BitextMining
- dataset:
config: run_Latn-rus_Cyrl
name: MTEB FloresBitextMining (run_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 83.399209486166
- type: f1
value: 81.10578750604326
- type: main_score
value: 81.10578750604326
- type: precision
value: 80.16763162673529
- type: recall
value: 83.399209486166
task:
type: BitextMining
- dataset:
config: tam_Taml-rus_Cyrl
name: MTEB FloresBitextMining (tam_Taml-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.41897233201581
- type: f1
value: 98.01548089591567
- type: main_score
value: 98.01548089591567
- type: precision
value: 97.84020327498588
- type: recall
value: 98.41897233201581
task:
type: BitextMining
- dataset:
config: vie_Latn-rus_Cyrl
name: MTEB FloresBitextMining (vie_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.1106719367589
- type: f1
value: 98.81422924901186
- type: main_score
value: 98.81422924901186
- type: precision
value: 98.66600790513834
- type: recall
value: 99.1106719367589
task:
type: BitextMining
- dataset:
config: apc_Arab-rus_Cyrl
name: MTEB FloresBitextMining (apc_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 93.87351778656127
- type: f1
value: 92.10803689064558
- type: main_score
value: 92.10803689064558
- type: precision
value: 91.30434782608695
- type: recall
value: 93.87351778656127
task:
type: BitextMining
- dataset:
config: bug_Latn-rus_Cyrl
name: MTEB FloresBitextMining (bug_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 57.608695652173914
- type: f1
value: 54.95878654927162
- type: main_score
value: 54.95878654927162
- type: precision
value: 54.067987427805654
- type: recall
value: 57.608695652173914
task:
type: BitextMining
- dataset:
config: fon_Latn-rus_Cyrl
name: MTEB FloresBitextMining (fon_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 61.95652173913043
- type: f1
value: 58.06537275812945
- type: main_score
value: 58.06537275812945
- type: precision
value: 56.554057596959204
- type: recall
value: 61.95652173913043
task:
type: BitextMining
- dataset:
config: jav_Latn-rus_Cyrl
name: MTEB FloresBitextMining (jav_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 93.47826086956522
- type: f1
value: 92.4784405318002
- type: main_score
value: 92.4784405318002
- type: precision
value: 92.09168143201127
- type: recall
value: 93.47826086956522
task:
type: BitextMining
- dataset:
config: lao_Laoo-rus_Cyrl
name: MTEB FloresBitextMining (lao_Laoo-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 91.10671936758892
- type: f1
value: 89.76104922745239
- type: main_score
value: 89.76104922745239
- type: precision
value: 89.24754593232855
- type: recall
value: 91.10671936758892
task:
type: BitextMining
- dataset:
config: mri_Latn-rus_Cyrl
name: MTEB FloresBitextMining (mri_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 71.14624505928853
- type: f1
value: 68.26947125119062
- type: main_score
value: 68.26947125119062
- type: precision
value: 67.15942311051006
- type: recall
value: 71.14624505928853
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ace_Arab
name: MTEB FloresBitextMining (rus_Cyrl-ace_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 19.565217391304348
- type: f1
value: 16.321465000323805
- type: main_score
value: 16.321465000323805
- type: precision
value: 15.478527409347508
- type: recall
value: 19.565217391304348
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bam_Latn
name: MTEB FloresBitextMining (rus_Cyrl-bam_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 73.41897233201581
- type: f1
value: 68.77366228182746
- type: main_score
value: 68.77366228182746
- type: precision
value: 66.96012924273795
- type: recall
value: 73.41897233201581
task:
type: BitextMining
- dataset:
config: rus_Cyrl-dzo_Tibt
name: MTEB FloresBitextMining (rus_Cyrl-dzo_Tibt)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 0.592885375494071
- type: f1
value: 0.02458062426370458
- type: main_score
value: 0.02458062426370458
- type: precision
value: 0.012824114724683876
- type: recall
value: 0.592885375494071
task:
type: BitextMining
- dataset:
config: rus_Cyrl-hin_Deva
name: MTEB FloresBitextMining (rus_Cyrl-hin_Deva)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.90118577075098
- type: f1
value: 99.86824769433464
- type: main_score
value: 99.86824769433464
- type: precision
value: 99.85177865612648
- type: recall
value: 99.90118577075098
task:
type: BitextMining
- dataset:
config: rus_Cyrl-khm_Khmr
name: MTEB FloresBitextMining (rus_Cyrl-khm_Khmr)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.13438735177866
- type: f1
value: 96.24505928853755
- type: main_score
value: 96.24505928853755
- type: precision
value: 95.81686429512516
- type: recall
value: 97.13438735177866
task:
type: BitextMining
- dataset:
config: rus_Cyrl-mag_Deva
name: MTEB FloresBitextMining (rus_Cyrl-mag_Deva)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.50592885375494
- type: f1
value: 99.35770750988142
- type: main_score
value: 99.35770750988142
- type: precision
value: 99.29183135704875
- type: recall
value: 99.50592885375494
task:
type: BitextMining
- dataset:
config: rus_Cyrl-pap_Latn
name: MTEB FloresBitextMining (rus_Cyrl-pap_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.93675889328063
- type: f1
value: 96.05072463768116
- type: main_score
value: 96.05072463768116
- type: precision
value: 95.66040843214758
- type: recall
value: 96.93675889328063
task:
type: BitextMining
- dataset:
config: rus_Cyrl-sot_Latn
name: MTEB FloresBitextMining (rus_Cyrl-sot_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 93.67588932806325
- type: f1
value: 91.7786561264822
- type: main_score
value: 91.7786561264822
- type: precision
value: 90.91238471673255
- type: recall
value: 93.67588932806325
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tur_Latn
name: MTEB FloresBitextMining (rus_Cyrl-tur_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.68247694334651
- type: main_score
value: 98.68247694334651
- type: precision
value: 98.51778656126481
- type: recall
value: 99.01185770750988
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ace_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ace_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 74.1106719367589
- type: f1
value: 70.21737923911836
- type: main_score
value: 70.21737923911836
- type: precision
value: 68.7068791410511
- type: recall
value: 74.1106719367589
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ban_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ban_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 81.7193675889328
- type: f1
value: 78.76470334510617
- type: main_score
value: 78.76470334510617
- type: precision
value: 77.76208475761422
- type: recall
value: 81.7193675889328
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ell_Grek
name: MTEB FloresBitextMining (rus_Cyrl-ell_Grek)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.3201581027668
- type: f1
value: 97.76021080368908
- type: main_score
value: 97.76021080368908
- type: precision
value: 97.48023715415019
- type: recall
value: 98.3201581027668
task:
type: BitextMining
- dataset:
config: rus_Cyrl-hne_Deva
name: MTEB FloresBitextMining (rus_Cyrl-hne_Deva)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.51778656126481
- type: f1
value: 98.0566534914361
- type: main_score
value: 98.0566534914361
- type: precision
value: 97.82608695652173
- type: recall
value: 98.51778656126481
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kik_Latn
name: MTEB FloresBitextMining (rus_Cyrl-kik_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 80.73122529644269
- type: f1
value: 76.42689244220864
- type: main_score
value: 76.42689244220864
- type: precision
value: 74.63877909530083
- type: recall
value: 80.73122529644269
task:
type: BitextMining
- dataset:
config: rus_Cyrl-mai_Deva
name: MTEB FloresBitextMining (rus_Cyrl-mai_Deva)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.56719367588933
- type: main_score
value: 98.56719367588933
- type: precision
value: 98.40250329380763
- type: recall
value: 98.91304347826086
task:
type: BitextMining
- dataset:
config: rus_Cyrl-pbt_Arab
name: MTEB FloresBitextMining (rus_Cyrl-pbt_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.5296442687747
- type: f1
value: 96.73913043478261
- type: main_score
value: 96.73913043478261
- type: precision
value: 96.36034255599473
- type: recall
value: 97.5296442687747
task:
type: BitextMining
- dataset:
config: rus_Cyrl-spa_Latn
name: MTEB FloresBitextMining (rus_Cyrl-spa_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.20948616600789
- type: main_score
value: 99.20948616600789
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
task:
type: BitextMining
- dataset:
config: rus_Cyrl-twi_Latn
name: MTEB FloresBitextMining (rus_Cyrl-twi_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 82.01581027667984
- type: f1
value: 78.064787822953
- type: main_score
value: 78.064787822953
- type: precision
value: 76.43272186750448
- type: recall
value: 82.01581027667984
task:
type: BitextMining
- dataset:
config: rus_Cyrl-acm_Arab
name: MTEB FloresBitextMining (rus_Cyrl-acm_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.3201581027668
- type: f1
value: 97.76021080368908
- type: main_score
value: 97.76021080368908
- type: precision
value: 97.48023715415019
- type: recall
value: 98.3201581027668
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bel_Cyrl
name: MTEB FloresBitextMining (rus_Cyrl-bel_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.22134387351778
- type: f1
value: 97.67786561264822
- type: main_score
value: 97.67786561264822
- type: precision
value: 97.4308300395257
- type: recall
value: 98.22134387351778
task:
type: BitextMining
- dataset:
config: rus_Cyrl-eng_Latn
name: MTEB FloresBitextMining (rus_Cyrl-eng_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.70355731225297
- type: f1
value: 99.60474308300395
- type: main_score
value: 99.60474308300395
- type: precision
value: 99.55533596837944
- type: recall
value: 99.70355731225297
task:
type: BitextMining
- dataset:
config: rus_Cyrl-hrv_Latn
name: MTEB FloresBitextMining (rus_Cyrl-hrv_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.1106719367589
- type: f1
value: 98.83069828722002
- type: main_score
value: 98.83069828722002
- type: precision
value: 98.69894598155466
- type: recall
value: 99.1106719367589
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kin_Latn
name: MTEB FloresBitextMining (rus_Cyrl-kin_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 93.37944664031622
- type: f1
value: 91.53162055335969
- type: main_score
value: 91.53162055335969
- type: precision
value: 90.71475625823452
- type: recall
value: 93.37944664031622
task:
type: BitextMining
- dataset:
config: rus_Cyrl-mal_Mlym
name: MTEB FloresBitextMining (rus_Cyrl-mal_Mlym)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.07773386034255
- type: main_score
value: 99.07773386034255
- type: precision
value: 98.96245059288538
- type: recall
value: 99.30830039525692
task:
type: BitextMining
- dataset:
config: rus_Cyrl-pes_Arab
name: MTEB FloresBitextMining (rus_Cyrl-pes_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.71541501976284
- type: f1
value: 98.30368906455863
- type: main_score
value: 98.30368906455863
- type: precision
value: 98.10606060606061
- type: recall
value: 98.71541501976284
task:
type: BitextMining
- dataset:
config: rus_Cyrl-srd_Latn
name: MTEB FloresBitextMining (rus_Cyrl-srd_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 89.03162055335969
- type: f1
value: 86.11048371917937
- type: main_score
value: 86.11048371917937
- type: precision
value: 84.86001317523056
- type: recall
value: 89.03162055335969
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tzm_Tfng
name: MTEB FloresBitextMining (rus_Cyrl-tzm_Tfng)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 12.351778656126482
- type: f1
value: 10.112177999067715
- type: main_score
value: 10.112177999067715
- type: precision
value: 9.53495885438645
- type: recall
value: 12.351778656126482
task:
type: BitextMining
- dataset:
config: rus_Cyrl-acq_Arab
name: MTEB FloresBitextMining (rus_Cyrl-acq_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.55072463768116
- type: main_score
value: 98.55072463768116
- type: precision
value: 98.36956521739131
- type: recall
value: 98.91304347826086
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bem_Latn
name: MTEB FloresBitextMining (rus_Cyrl-bem_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 73.22134387351778
- type: f1
value: 68.30479412989295
- type: main_score
value: 68.30479412989295
- type: precision
value: 66.40073447632736
- type: recall
value: 73.22134387351778
task:
type: BitextMining
- dataset:
config: rus_Cyrl-epo_Latn
name: MTEB FloresBitextMining (rus_Cyrl-epo_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.1106719367589
- type: f1
value: 98.81422924901186
- type: main_score
value: 98.81422924901186
- type: precision
value: 98.66600790513834
- type: recall
value: 99.1106719367589
task:
type: BitextMining
- dataset:
config: rus_Cyrl-hun_Latn
name: MTEB FloresBitextMining (rus_Cyrl-hun_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.83794466403161
- type: f1
value: 95.88274044795784
- type: main_score
value: 95.88274044795784
- type: precision
value: 95.45454545454545
- type: recall
value: 96.83794466403161
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kir_Cyrl
name: MTEB FloresBitextMining (rus_Cyrl-kir_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.34387351778656
- type: f1
value: 95.49280429715212
- type: main_score
value: 95.49280429715212
- type: precision
value: 95.14163372859026
- type: recall
value: 96.34387351778656
task:
type: BitextMining
- dataset:
config: rus_Cyrl-mar_Deva
name: MTEB FloresBitextMining (rus_Cyrl-mar_Deva)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.71541501976284
- type: f1
value: 98.28722002635047
- type: main_score
value: 98.28722002635047
- type: precision
value: 98.07312252964427
- type: recall
value: 98.71541501976284
task:
type: BitextMining
- dataset:
config: rus_Cyrl-plt_Latn
name: MTEB FloresBitextMining (rus_Cyrl-plt_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 88.04347826086956
- type: f1
value: 85.14328063241106
- type: main_score
value: 85.14328063241106
- type: precision
value: 83.96339168078298
- type: recall
value: 88.04347826086956
task:
type: BitextMining
- dataset:
config: rus_Cyrl-srp_Cyrl
name: MTEB FloresBitextMining (rus_Cyrl-srp_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.2094861660079
- type: main_score
value: 99.2094861660079
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
task:
type: BitextMining
- dataset:
config: rus_Cyrl-uig_Arab
name: MTEB FloresBitextMining (rus_Cyrl-uig_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 92.19367588932806
- type: f1
value: 89.98541313758706
- type: main_score
value: 89.98541313758706
- type: precision
value: 89.01021080368906
- type: recall
value: 92.19367588932806
task:
type: BitextMining
- dataset:
config: rus_Cyrl-aeb_Arab
name: MTEB FloresBitextMining (rus_Cyrl-aeb_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.8498023715415
- type: f1
value: 94.63109354413703
- type: main_score
value: 94.63109354413703
- type: precision
value: 94.05467720685111
- type: recall
value: 95.8498023715415
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ben_Beng
name: MTEB FloresBitextMining (rus_Cyrl-ben_Beng)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.2094861660079
- type: main_score
value: 99.2094861660079
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
task:
type: BitextMining
- dataset:
config: rus_Cyrl-est_Latn
name: MTEB FloresBitextMining (rus_Cyrl-est_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.55335968379447
- type: f1
value: 94.2588932806324
- type: main_score
value: 94.2588932806324
- type: precision
value: 93.65118577075098
- type: recall
value: 95.55335968379447
task:
type: BitextMining
- dataset:
config: rus_Cyrl-hye_Armn
name: MTEB FloresBitextMining (rus_Cyrl-hye_Armn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.71541501976284
- type: f1
value: 98.28722002635045
- type: main_score
value: 98.28722002635045
- type: precision
value: 98.07312252964427
- type: recall
value: 98.71541501976284
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kmb_Latn
name: MTEB FloresBitextMining (rus_Cyrl-kmb_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 54.24901185770751
- type: f1
value: 49.46146674116913
- type: main_score
value: 49.46146674116913
- type: precision
value: 47.81033799314432
- type: recall
value: 54.24901185770751
task:
type: BitextMining
- dataset:
config: rus_Cyrl-min_Arab
name: MTEB FloresBitextMining (rus_Cyrl-min_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 15.810276679841898
- type: f1
value: 13.271207641419332
- type: main_score
value: 13.271207641419332
- type: precision
value: 12.510673148766033
- type: recall
value: 15.810276679841898
task:
type: BitextMining
- dataset:
config: rus_Cyrl-pol_Latn
name: MTEB FloresBitextMining (rus_Cyrl-pol_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.71541501976284
- type: f1
value: 98.32674571805006
- type: main_score
value: 98.32674571805006
- type: precision
value: 98.14723320158103
- type: recall
value: 98.71541501976284
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ssw_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ssw_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 80.8300395256917
- type: f1
value: 76.51717847370023
- type: main_score
value: 76.51717847370023
- type: precision
value: 74.74143610013175
- type: recall
value: 80.8300395256917
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ukr_Cyrl
name: MTEB FloresBitextMining (rus_Cyrl-ukr_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.4729907773386
- type: main_score
value: 99.4729907773386
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
task:
type: BitextMining
- dataset:
config: rus_Cyrl-afr_Latn
name: MTEB FloresBitextMining (rus_Cyrl-afr_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.1106719367589
- type: f1
value: 98.81422924901186
- type: main_score
value: 98.81422924901186
- type: precision
value: 98.66600790513834
- type: recall
value: 99.1106719367589
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bho_Deva
name: MTEB FloresBitextMining (rus_Cyrl-bho_Deva)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.6403162055336
- type: f1
value: 95.56982872200265
- type: main_score
value: 95.56982872200265
- type: precision
value: 95.0592885375494
- type: recall
value: 96.6403162055336
task:
type: BitextMining
- dataset:
config: rus_Cyrl-eus_Latn
name: MTEB FloresBitextMining (rus_Cyrl-eus_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.62845849802372
- type: f1
value: 96.9038208168643
- type: main_score
value: 96.9038208168643
- type: precision
value: 96.55797101449275
- type: recall
value: 97.62845849802372
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ibo_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ibo_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 89.2292490118577
- type: f1
value: 86.35234330886506
- type: main_score
value: 86.35234330886506
- type: precision
value: 85.09881422924902
- type: recall
value: 89.2292490118577
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kmr_Latn
name: MTEB FloresBitextMining (rus_Cyrl-kmr_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 83.49802371541502
- type: f1
value: 79.23630717108978
- type: main_score
value: 79.23630717108978
- type: precision
value: 77.48188405797102
- type: recall
value: 83.49802371541502
task:
type: BitextMining
- dataset:
config: rus_Cyrl-min_Latn
name: MTEB FloresBitextMining (rus_Cyrl-min_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 79.34782608695652
- type: f1
value: 75.31689928429059
- type: main_score
value: 75.31689928429059
- type: precision
value: 73.91519410541149
- type: recall
value: 79.34782608695652
task:
type: BitextMining
- dataset:
config: rus_Cyrl-por_Latn
name: MTEB FloresBitextMining (rus_Cyrl-por_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.54150197628458
- type: f1
value: 95.53218520609825
- type: main_score
value: 95.53218520609825
- type: precision
value: 95.07575757575756
- type: recall
value: 96.54150197628458
task:
type: BitextMining
- dataset:
config: rus_Cyrl-sun_Latn
name: MTEB FloresBitextMining (rus_Cyrl-sun_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 93.2806324110672
- type: f1
value: 91.56973461321287
- type: main_score
value: 91.56973461321287
- type: precision
value: 90.84396334890405
- type: recall
value: 93.2806324110672
task:
type: BitextMining
- dataset:
config: rus_Cyrl-umb_Latn
name: MTEB FloresBitextMining (rus_Cyrl-umb_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 51.87747035573123
- type: f1
value: 46.36591778884269
- type: main_score
value: 46.36591778884269
- type: precision
value: 44.57730391234227
- type: recall
value: 51.87747035573123
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ajp_Arab
name: MTEB FloresBitextMining (rus_Cyrl-ajp_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.71541501976284
- type: f1
value: 98.30368906455863
- type: main_score
value: 98.30368906455863
- type: precision
value: 98.10606060606061
- type: recall
value: 98.71541501976284
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bjn_Arab
name: MTEB FloresBitextMining (rus_Cyrl-bjn_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 14.82213438735178
- type: f1
value: 12.365434276616856
- type: main_score
value: 12.365434276616856
- type: precision
value: 11.802079517180589
- type: recall
value: 14.82213438735178
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ewe_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ewe_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 71.44268774703558
- type: f1
value: 66.74603174603175
- type: main_score
value: 66.74603174603175
- type: precision
value: 64.99933339607253
- type: recall
value: 71.44268774703558
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ilo_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ilo_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 85.86956521739131
- type: f1
value: 83.00139015960917
- type: main_score
value: 83.00139015960917
- type: precision
value: 81.91411396574439
- type: recall
value: 85.86956521739131
task:
type: BitextMining
- dataset:
config: rus_Cyrl-knc_Arab
name: MTEB FloresBitextMining (rus_Cyrl-knc_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 14.525691699604742
- type: f1
value: 12.618283715726806
- type: main_score
value: 12.618283715726806
- type: precision
value: 12.048458493742352
- type: recall
value: 14.525691699604742
task:
type: BitextMining
- dataset:
config: rus_Cyrl-mkd_Cyrl
name: MTEB FloresBitextMining (rus_Cyrl-mkd_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.22595520421606
- type: main_score
value: 99.22595520421606
- type: precision
value: 99.14361001317523
- type: recall
value: 99.40711462450594
task:
type: BitextMining
- dataset:
config: rus_Cyrl-prs_Arab
name: MTEB FloresBitextMining (rus_Cyrl-prs_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.07773386034255
- type: main_score
value: 99.07773386034255
- type: precision
value: 98.96245059288538
- type: recall
value: 99.30830039525692
task:
type: BitextMining
- dataset:
config: rus_Cyrl-swe_Latn
name: MTEB FloresBitextMining (rus_Cyrl-swe_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.07773386034256
- type: main_score
value: 99.07773386034256
- type: precision
value: 98.96245059288538
- type: recall
value: 99.30830039525692
task:
type: BitextMining
- dataset:
config: rus_Cyrl-urd_Arab
name: MTEB FloresBitextMining (rus_Cyrl-urd_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.61660079051383
- type: f1
value: 98.15546772068511
- type: main_score
value: 98.15546772068511
- type: precision
value: 97.92490118577075
- type: recall
value: 98.61660079051383
task:
type: BitextMining
- dataset:
config: rus_Cyrl-aka_Latn
name: MTEB FloresBitextMining (rus_Cyrl-aka_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 81.02766798418972
- type: f1
value: 76.73277809147375
- type: main_score
value: 76.73277809147375
- type: precision
value: 74.97404165882426
- type: recall
value: 81.02766798418972
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bjn_Latn
name: MTEB FloresBitextMining (rus_Cyrl-bjn_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 86.7588932806324
- type: f1
value: 83.92064566965753
- type: main_score
value: 83.92064566965753
- type: precision
value: 82.83734079929732
- type: recall
value: 86.7588932806324
task:
type: BitextMining
- dataset:
config: rus_Cyrl-fao_Latn
name: MTEB FloresBitextMining (rus_Cyrl-fao_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 88.43873517786561
- type: f1
value: 85.48136645962732
- type: main_score
value: 85.48136645962732
- type: precision
value: 84.23418972332016
- type: recall
value: 88.43873517786561
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ind_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ind_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.68247694334651
- type: main_score
value: 98.68247694334651
- type: precision
value: 98.51778656126481
- type: recall
value: 99.01185770750988
task:
type: BitextMining
- dataset:
config: rus_Cyrl-knc_Latn
name: MTEB FloresBitextMining (rus_Cyrl-knc_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 45.8498023715415
- type: f1
value: 40.112030865489366
- type: main_score
value: 40.112030865489366
- type: precision
value: 38.28262440050776
- type: recall
value: 45.8498023715415
task:
type: BitextMining
- dataset:
config: rus_Cyrl-mlt_Latn
name: MTEB FloresBitextMining (rus_Cyrl-mlt_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 93.18181818181817
- type: f1
value: 91.30787690570298
- type: main_score
value: 91.30787690570298
- type: precision
value: 90.4983060417843
- type: recall
value: 93.18181818181817
task:
type: BitextMining
- dataset:
config: rus_Cyrl-quy_Latn
name: MTEB FloresBitextMining (rus_Cyrl-quy_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 62.450592885375485
- type: f1
value: 57.28742975628178
- type: main_score
value: 57.28742975628178
- type: precision
value: 55.56854987623269
- type: recall
value: 62.450592885375485
task:
type: BitextMining
- dataset:
config: rus_Cyrl-swh_Latn
name: MTEB FloresBitextMining (rus_Cyrl-swh_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.3201581027668
- type: f1
value: 97.77667984189723
- type: main_score
value: 97.77667984189723
- type: precision
value: 97.51317523056655
- type: recall
value: 98.3201581027668
task:
type: BitextMining
- dataset:
config: rus_Cyrl-uzn_Latn
name: MTEB FloresBitextMining (rus_Cyrl-uzn_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.12252964426878
- type: f1
value: 97.59081498211933
- type: main_score
value: 97.59081498211933
- type: precision
value: 97.34848484848484
- type: recall
value: 98.12252964426878
task:
type: BitextMining
- dataset:
config: rus_Cyrl-als_Latn
name: MTEB FloresBitextMining (rus_Cyrl-als_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.09420289855073
- type: main_score
value: 99.09420289855073
- type: precision
value: 98.99538866930172
- type: recall
value: 99.30830039525692
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bod_Tibt
name: MTEB FloresBitextMining (rus_Cyrl-bod_Tibt)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 11.561264822134387
- type: f1
value: 8.121312045385636
- type: main_score
value: 8.121312045385636
- type: precision
value: 7.350577020893972
- type: recall
value: 11.561264822134387
task:
type: BitextMining
- dataset:
config: rus_Cyrl-fij_Latn
name: MTEB FloresBitextMining (rus_Cyrl-fij_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 72.23320158102767
- type: f1
value: 67.21000233846082
- type: main_score
value: 67.21000233846082
- type: precision
value: 65.3869439739005
- type: recall
value: 72.23320158102767
task:
type: BitextMining
- dataset:
config: rus_Cyrl-isl_Latn
name: MTEB FloresBitextMining (rus_Cyrl-isl_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 91.99604743083005
- type: f1
value: 89.75955204216073
- type: main_score
value: 89.75955204216073
- type: precision
value: 88.7598814229249
- type: recall
value: 91.99604743083005
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kon_Latn
name: MTEB FloresBitextMining (rus_Cyrl-kon_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 81.81818181818183
- type: f1
value: 77.77800098452272
- type: main_score
value: 77.77800098452272
- type: precision
value: 76.1521268586486
- type: recall
value: 81.81818181818183
task:
type: BitextMining
- dataset:
config: rus_Cyrl-mni_Beng
name: MTEB FloresBitextMining (rus_Cyrl-mni_Beng)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 54.74308300395256
- type: f1
value: 48.97285299254615
- type: main_score
value: 48.97285299254615
- type: precision
value: 46.95125742968299
- type: recall
value: 54.74308300395256
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ron_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ron_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.22134387351778
- type: f1
value: 97.64492753623189
- type: main_score
value: 97.64492753623189
- type: precision
value: 97.36495388669302
- type: recall
value: 98.22134387351778
task:
type: BitextMining
- dataset:
config: rus_Cyrl-szl_Latn
name: MTEB FloresBitextMining (rus_Cyrl-szl_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 92.09486166007905
- type: f1
value: 90.10375494071147
- type: main_score
value: 90.10375494071147
- type: precision
value: 89.29606625258798
- type: recall
value: 92.09486166007905
task:
type: BitextMining
- dataset:
config: rus_Cyrl-vec_Latn
name: MTEB FloresBitextMining (rus_Cyrl-vec_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 92.4901185770751
- type: f1
value: 90.51430453604365
- type: main_score
value: 90.51430453604365
- type: precision
value: 89.69367588932808
- type: recall
value: 92.4901185770751
task:
type: BitextMining
- dataset:
config: rus_Cyrl-amh_Ethi
name: MTEB FloresBitextMining (rus_Cyrl-amh_Ethi)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.11791831357048
- type: main_score
value: 97.11791831357048
- type: precision
value: 96.77206851119894
- type: recall
value: 97.82608695652173
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bos_Latn
name: MTEB FloresBitextMining (rus_Cyrl-bos_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.55072463768116
- type: main_score
value: 98.55072463768116
- type: precision
value: 98.36956521739131
- type: recall
value: 98.91304347826086
task:
type: BitextMining
- dataset:
config: rus_Cyrl-fin_Latn
name: MTEB FloresBitextMining (rus_Cyrl-fin_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.65217391304348
- type: f1
value: 94.4235836627141
- type: main_score
value: 94.4235836627141
- type: precision
value: 93.84881422924902
- type: recall
value: 95.65217391304348
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ita_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ita_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.55072463768117
- type: main_score
value: 98.55072463768117
- type: precision
value: 98.36956521739131
- type: recall
value: 98.91304347826086
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kor_Hang
name: MTEB FloresBitextMining (rus_Cyrl-kor_Hang)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.55335968379447
- type: f1
value: 94.15349143610013
- type: main_score
value: 94.15349143610013
- type: precision
value: 93.49472990777339
- type: recall
value: 95.55335968379447
task:
type: BitextMining
- dataset:
config: rus_Cyrl-mos_Latn
name: MTEB FloresBitextMining (rus_Cyrl-mos_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 43.67588932806324
- type: f1
value: 38.84849721190082
- type: main_score
value: 38.84849721190082
- type: precision
value: 37.43294462099682
- type: recall
value: 43.67588932806324
task:
type: BitextMining
- dataset:
config: rus_Cyrl-run_Latn
name: MTEB FloresBitextMining (rus_Cyrl-run_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 90.21739130434783
- type: f1
value: 87.37483530961792
- type: main_score
value: 87.37483530961792
- type: precision
value: 86.07872200263506
- type: recall
value: 90.21739130434783
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tam_Taml
name: MTEB FloresBitextMining (rus_Cyrl-tam_Taml)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.2094861660079
- type: main_score
value: 99.2094861660079
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
task:
type: BitextMining
- dataset:
config: rus_Cyrl-vie_Latn
name: MTEB FloresBitextMining (rus_Cyrl-vie_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.03557312252964
- type: f1
value: 96.13636363636364
- type: main_score
value: 96.13636363636364
- type: precision
value: 95.70981554677206
- type: recall
value: 97.03557312252964
task:
type: BitextMining
- dataset:
config: rus_Cyrl-apc_Arab
name: MTEB FloresBitextMining (rus_Cyrl-apc_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.12252964426878
- type: f1
value: 97.49670619235836
- type: main_score
value: 97.49670619235836
- type: precision
value: 97.18379446640316
- type: recall
value: 98.12252964426878
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bug_Latn
name: MTEB FloresBitextMining (rus_Cyrl-bug_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 67.29249011857708
- type: f1
value: 62.09268717667927
- type: main_score
value: 62.09268717667927
- type: precision
value: 60.28554009748714
- type: recall
value: 67.29249011857708
task:
type: BitextMining
- dataset:
config: rus_Cyrl-fon_Latn
name: MTEB FloresBitextMining (rus_Cyrl-fon_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 63.43873517786561
- type: f1
value: 57.66660107569199
- type: main_score
value: 57.66660107569199
- type: precision
value: 55.66676396919363
- type: recall
value: 63.43873517786561
task:
type: BitextMining
- dataset:
config: rus_Cyrl-jav_Latn
name: MTEB FloresBitextMining (rus_Cyrl-jav_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 94.46640316205533
- type: f1
value: 92.89384528514964
- type: main_score
value: 92.89384528514964
- type: precision
value: 92.19367588932806
- type: recall
value: 94.46640316205533
task:
type: BitextMining
- dataset:
config: rus_Cyrl-lao_Laoo
name: MTEB FloresBitextMining (rus_Cyrl-lao_Laoo)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.23320158102767
- type: f1
value: 96.40974967061922
- type: main_score
value: 96.40974967061922
- type: precision
value: 96.034255599473
- type: recall
value: 97.23320158102767
task:
type: BitextMining
- dataset:
config: rus_Cyrl-mri_Latn
name: MTEB FloresBitextMining (rus_Cyrl-mri_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 76.77865612648222
- type: f1
value: 73.11286539547409
- type: main_score
value: 73.11286539547409
- type: precision
value: 71.78177214337046
- type: recall
value: 76.77865612648222
task:
type: BitextMining
- dataset:
config: rus_Cyrl-taq_Latn
name: MTEB FloresBitextMining (rus_Cyrl-taq_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 41.99604743083004
- type: f1
value: 37.25127063318763
- type: main_score
value: 37.25127063318763
- type: precision
value: 35.718929186985726
- type: recall
value: 41.99604743083004
task:
type: BitextMining
- dataset:
config: rus_Cyrl-war_Latn
name: MTEB FloresBitextMining (rus_Cyrl-war_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.55335968379447
- type: f1
value: 94.1699604743083
- type: main_score
value: 94.1699604743083
- type: precision
value: 93.52766798418972
- type: recall
value: 95.55335968379447
task:
type: BitextMining
- dataset:
config: rus_Cyrl-arb_Arab
name: MTEB FloresBitextMining (rus_Cyrl-arb_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.4729907773386
- type: main_score
value: 99.4729907773386
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bul_Cyrl
name: MTEB FloresBitextMining (rus_Cyrl-bul_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.70355731225297
- type: f1
value: 99.60474308300395
- type: main_score
value: 99.60474308300395
- type: precision
value: 99.55533596837944
- type: recall
value: 99.70355731225297
task:
type: BitextMining
- dataset:
config: rus_Cyrl-fra_Latn
name: MTEB FloresBitextMining (rus_Cyrl-fra_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.47299077733861
- type: main_score
value: 99.47299077733861
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
task:
type: BitextMining
- dataset:
config: rus_Cyrl-jpn_Jpan
name: MTEB FloresBitextMining (rus_Cyrl-jpn_Jpan)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.44268774703558
- type: f1
value: 95.30632411067194
- type: main_score
value: 95.30632411067194
- type: precision
value: 94.76284584980237
- type: recall
value: 96.44268774703558
task:
type: BitextMining
- dataset:
config: rus_Cyrl-lij_Latn
name: MTEB FloresBitextMining (rus_Cyrl-lij_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 90.21739130434783
- type: f1
value: 87.4703557312253
- type: main_score
value: 87.4703557312253
- type: precision
value: 86.29611330698287
- type: recall
value: 90.21739130434783
task:
type: BitextMining
- dataset:
config: rus_Cyrl-mya_Mymr
name: MTEB FloresBitextMining (rus_Cyrl-mya_Mymr)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.364953886693
- type: main_score
value: 97.364953886693
- type: precision
value: 97.03557312252964
- type: recall
value: 98.02371541501977
task:
type: BitextMining
- dataset:
config: rus_Cyrl-sag_Latn
name: MTEB FloresBitextMining (rus_Cyrl-sag_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 54.841897233201585
- type: f1
value: 49.61882037503349
- type: main_score
value: 49.61882037503349
- type: precision
value: 47.831968755881796
- type: recall
value: 54.841897233201585
task:
type: BitextMining
- dataset:
config: rus_Cyrl-taq_Tfng
name: MTEB FloresBitextMining (rus_Cyrl-taq_Tfng)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 15.316205533596838
- type: f1
value: 11.614836360389717
- type: main_score
value: 11.614836360389717
- type: precision
value: 10.741446193235223
- type: recall
value: 15.316205533596838
task:
type: BitextMining
- dataset:
config: rus_Cyrl-wol_Latn
name: MTEB FloresBitextMining (rus_Cyrl-wol_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 67.88537549407114
- type: f1
value: 62.2536417249856
- type: main_score
value: 62.2536417249856
- type: precision
value: 60.27629128666678
- type: recall
value: 67.88537549407114
task:
type: BitextMining
- dataset:
config: rus_Cyrl-arb_Latn
name: MTEB FloresBitextMining (rus_Cyrl-arb_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 27.766798418972332
- type: f1
value: 23.39674889624077
- type: main_score
value: 23.39674889624077
- type: precision
value: 22.28521155585345
- type: recall
value: 27.766798418972332
task:
type: BitextMining
- dataset:
config: rus_Cyrl-cat_Latn
name: MTEB FloresBitextMining (rus_Cyrl-cat_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.23320158102767
- type: f1
value: 96.42151326933936
- type: main_score
value: 96.42151326933936
- type: precision
value: 96.04743083003953
- type: recall
value: 97.23320158102767
task:
type: BitextMining
- dataset:
config: rus_Cyrl-fur_Latn
name: MTEB FloresBitextMining (rus_Cyrl-fur_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 88.63636363636364
- type: f1
value: 85.80792396009788
- type: main_score
value: 85.80792396009788
- type: precision
value: 84.61508901726293
- type: recall
value: 88.63636363636364
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kab_Latn
name: MTEB FloresBitextMining (rus_Cyrl-kab_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 48.12252964426877
- type: f1
value: 43.05387582971066
- type: main_score
value: 43.05387582971066
- type: precision
value: 41.44165117538212
- type: recall
value: 48.12252964426877
task:
type: BitextMining
- dataset:
config: rus_Cyrl-lim_Latn
name: MTEB FloresBitextMining (rus_Cyrl-lim_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 81.81818181818183
- type: f1
value: 77.81676163099087
- type: main_score
value: 77.81676163099087
- type: precision
value: 76.19565217391305
- type: recall
value: 81.81818181818183
task:
type: BitextMining
- dataset:
config: rus_Cyrl-nld_Latn
name: MTEB FloresBitextMining (rus_Cyrl-nld_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.33201581027669
- type: f1
value: 96.4756258234519
- type: main_score
value: 96.4756258234519
- type: precision
value: 96.06389986824769
- type: recall
value: 97.33201581027669
task:
type: BitextMining
- dataset:
config: rus_Cyrl-san_Deva
name: MTEB FloresBitextMining (rus_Cyrl-san_Deva)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 93.47826086956522
- type: f1
value: 91.70289855072463
- type: main_score
value: 91.70289855072463
- type: precision
value: 90.9370882740448
- type: recall
value: 93.47826086956522
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tat_Cyrl
name: MTEB FloresBitextMining (rus_Cyrl-tat_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.72727272727273
- type: f1
value: 97.00263504611331
- type: main_score
value: 97.00263504611331
- type: precision
value: 96.65678524374177
- type: recall
value: 97.72727272727273
task:
type: BitextMining
- dataset:
config: rus_Cyrl-xho_Latn
name: MTEB FloresBitextMining (rus_Cyrl-xho_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 93.08300395256917
- type: f1
value: 91.12977602108036
- type: main_score
value: 91.12977602108036
- type: precision
value: 90.22562582345192
- type: recall
value: 93.08300395256917
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ars_Arab
name: MTEB FloresBitextMining (rus_Cyrl-ars_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.2094861660079
- type: main_score
value: 99.2094861660079
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ceb_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ceb_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.65217391304348
- type: f1
value: 94.3544137022398
- type: main_score
value: 94.3544137022398
- type: precision
value: 93.76646903820817
- type: recall
value: 95.65217391304348
task:
type: BitextMining
- dataset:
config: rus_Cyrl-fuv_Latn
name: MTEB FloresBitextMining (rus_Cyrl-fuv_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 51.18577075098815
- type: f1
value: 44.5990252610806
- type: main_score
value: 44.5990252610806
- type: precision
value: 42.34331599450177
- type: recall
value: 51.18577075098815
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kac_Latn
name: MTEB FloresBitextMining (rus_Cyrl-kac_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 46.93675889328063
- type: f1
value: 41.79004018701787
- type: main_score
value: 41.79004018701787
- type: precision
value: 40.243355662392624
- type: recall
value: 46.93675889328063
task:
type: BitextMining
- dataset:
config: rus_Cyrl-lin_Latn
name: MTEB FloresBitextMining (rus_Cyrl-lin_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 91.50197628458498
- type: f1
value: 89.1205533596838
- type: main_score
value: 89.1205533596838
- type: precision
value: 88.07147562582345
- type: recall
value: 91.50197628458498
task:
type: BitextMining
- dataset:
config: rus_Cyrl-nno_Latn
name: MTEB FloresBitextMining (rus_Cyrl-nno_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.81422924901186
- type: f1
value: 98.41897233201581
- type: main_score
value: 98.41897233201581
- type: precision
value: 98.22134387351778
- type: recall
value: 98.81422924901186
task:
type: BitextMining
- dataset:
config: rus_Cyrl-sat_Olck
name: MTEB FloresBitextMining (rus_Cyrl-sat_Olck)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 2.371541501976284
- type: f1
value: 1.0726274943087382
- type: main_score
value: 1.0726274943087382
- type: precision
value: 0.875279634748803
- type: recall
value: 2.371541501976284
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tel_Telu
name: MTEB FloresBitextMining (rus_Cyrl-tel_Telu)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.68247694334651
- type: main_score
value: 98.68247694334651
- type: precision
value: 98.51778656126481
- type: recall
value: 99.01185770750988
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ydd_Hebr
name: MTEB FloresBitextMining (rus_Cyrl-ydd_Hebr)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 89.42687747035573
- type: f1
value: 86.47609636740073
- type: main_score
value: 86.47609636740073
- type: precision
value: 85.13669301712781
- type: recall
value: 89.42687747035573
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ary_Arab
name: MTEB FloresBitextMining (rus_Cyrl-ary_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 89.82213438735178
- type: f1
value: 87.04545454545456
- type: main_score
value: 87.04545454545456
- type: precision
value: 85.76910408432148
- type: recall
value: 89.82213438735178
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ces_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ces_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
task:
type: BitextMining
- dataset:
config: rus_Cyrl-gaz_Latn
name: MTEB FloresBitextMining (rus_Cyrl-gaz_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 64.9209486166008
- type: f1
value: 58.697458119394874
- type: main_score
value: 58.697458119394874
- type: precision
value: 56.43402189597842
- type: recall
value: 64.9209486166008
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kam_Latn
name: MTEB FloresBitextMining (rus_Cyrl-kam_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 59.18972332015811
- type: f1
value: 53.19031511966295
- type: main_score
value: 53.19031511966295
- type: precision
value: 51.08128357343655
- type: recall
value: 59.18972332015811
task:
type: BitextMining
- dataset:
config: rus_Cyrl-lit_Latn
name: MTEB FloresBitextMining (rus_Cyrl-lit_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.54150197628458
- type: f1
value: 95.5368906455863
- type: main_score
value: 95.5368906455863
- type: precision
value: 95.0592885375494
- type: recall
value: 96.54150197628458
task:
type: BitextMining
- dataset:
config: rus_Cyrl-nob_Latn
name: MTEB FloresBitextMining (rus_Cyrl-nob_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.12252964426878
- type: f1
value: 97.51317523056655
- type: main_score
value: 97.51317523056655
- type: precision
value: 97.2167325428195
- type: recall
value: 98.12252964426878
task:
type: BitextMining
- dataset:
config: rus_Cyrl-scn_Latn
name: MTEB FloresBitextMining (rus_Cyrl-scn_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 84.0909090909091
- type: f1
value: 80.37000439174352
- type: main_score
value: 80.37000439174352
- type: precision
value: 78.83994628559846
- type: recall
value: 84.0909090909091
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tgk_Cyrl
name: MTEB FloresBitextMining (rus_Cyrl-tgk_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 92.68774703557312
- type: f1
value: 90.86344814605684
- type: main_score
value: 90.86344814605684
- type: precision
value: 90.12516469038208
- type: recall
value: 92.68774703557312
task:
type: BitextMining
- dataset:
config: rus_Cyrl-yor_Latn
name: MTEB FloresBitextMining (rus_Cyrl-yor_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 72.13438735177866
- type: f1
value: 66.78759646150951
- type: main_score
value: 66.78759646150951
- type: precision
value: 64.85080192096002
- type: recall
value: 72.13438735177866
task:
type: BitextMining
- dataset:
config: rus_Cyrl-arz_Arab
name: MTEB FloresBitextMining (rus_Cyrl-arz_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.364953886693
- type: main_score
value: 97.364953886693
- type: precision
value: 97.03557312252964
- type: recall
value: 98.02371541501977
task:
type: BitextMining
- dataset:
config: rus_Cyrl-cjk_Latn
name: MTEB FloresBitextMining (rus_Cyrl-cjk_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 51.976284584980235
- type: f1
value: 46.468762353149714
- type: main_score
value: 46.468762353149714
- type: precision
value: 44.64073366247278
- type: recall
value: 51.976284584980235
task:
type: BitextMining
- dataset:
config: rus_Cyrl-gla_Latn
name: MTEB FloresBitextMining (rus_Cyrl-gla_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 79.74308300395256
- type: f1
value: 75.55611165294958
- type: main_score
value: 75.55611165294958
- type: precision
value: 73.95033408620365
- type: recall
value: 79.74308300395256
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kan_Knda
name: MTEB FloresBitextMining (rus_Cyrl-kan_Knda)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.96245059288538
- type: main_score
value: 98.96245059288538
- type: precision
value: 98.84716732542819
- type: recall
value: 99.2094861660079
task:
type: BitextMining
- dataset:
config: rus_Cyrl-lmo_Latn
name: MTEB FloresBitextMining (rus_Cyrl-lmo_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 82.41106719367589
- type: f1
value: 78.56413514022209
- type: main_score
value: 78.56413514022209
- type: precision
value: 77.15313068573938
- type: recall
value: 82.41106719367589
task:
type: BitextMining
- dataset:
config: rus_Cyrl-npi_Deva
name: MTEB FloresBitextMining (rus_Cyrl-npi_Deva)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.71541501976284
- type: f1
value: 98.3201581027668
- type: main_score
value: 98.3201581027668
- type: precision
value: 98.12252964426878
- type: recall
value: 98.71541501976284
task:
type: BitextMining
- dataset:
config: rus_Cyrl-shn_Mymr
name: MTEB FloresBitextMining (rus_Cyrl-shn_Mymr)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 57.11462450592886
- type: f1
value: 51.51361369197337
- type: main_score
value: 51.51361369197337
- type: precision
value: 49.71860043649573
- type: recall
value: 57.11462450592886
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tgl_Latn
name: MTEB FloresBitextMining (rus_Cyrl-tgl_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.18379446640316
- type: main_score
value: 97.18379446640316
- type: precision
value: 96.88735177865613
- type: recall
value: 97.82608695652173
task:
type: BitextMining
- dataset:
config: rus_Cyrl-yue_Hant
name: MTEB FloresBitextMining (rus_Cyrl-yue_Hant)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.09420289855072
- type: main_score
value: 99.09420289855072
- type: precision
value: 98.9953886693017
- type: recall
value: 99.30830039525692
task:
type: BitextMining
- dataset:
config: rus_Cyrl-asm_Beng
name: MTEB FloresBitextMining (rus_Cyrl-asm_Beng)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.55335968379447
- type: f1
value: 94.16007905138339
- type: main_score
value: 94.16007905138339
- type: precision
value: 93.50296442687747
- type: recall
value: 95.55335968379447
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ckb_Arab
name: MTEB FloresBitextMining (rus_Cyrl-ckb_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 92.88537549407114
- type: f1
value: 90.76745718050066
- type: main_score
value: 90.76745718050066
- type: precision
value: 89.80072463768116
- type: recall
value: 92.88537549407114
task:
type: BitextMining
- dataset:
config: rus_Cyrl-gle_Latn
name: MTEB FloresBitextMining (rus_Cyrl-gle_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 91.699604743083
- type: f1
value: 89.40899680030115
- type: main_score
value: 89.40899680030115
- type: precision
value: 88.40085638998683
- type: recall
value: 91.699604743083
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kas_Arab
name: MTEB FloresBitextMining (rus_Cyrl-kas_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 88.3399209486166
- type: f1
value: 85.14351590438548
- type: main_score
value: 85.14351590438548
- type: precision
value: 83.72364953886692
- type: recall
value: 88.3399209486166
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ltg_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ltg_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 83.399209486166
- type: f1
value: 79.88408934061107
- type: main_score
value: 79.88408934061107
- type: precision
value: 78.53794509179885
- type: recall
value: 83.399209486166
task:
type: BitextMining
- dataset:
config: rus_Cyrl-nso_Latn
name: MTEB FloresBitextMining (rus_Cyrl-nso_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 91.20553359683794
- type: f1
value: 88.95406635525212
- type: main_score
value: 88.95406635525212
- type: precision
value: 88.01548089591567
- type: recall
value: 91.20553359683794
task:
type: BitextMining
- dataset:
config: rus_Cyrl-sin_Sinh
name: MTEB FloresBitextMining (rus_Cyrl-sin_Sinh)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.56719367588933
- type: main_score
value: 98.56719367588933
- type: precision
value: 98.40250329380763
- type: recall
value: 98.91304347826086
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tha_Thai
name: MTEB FloresBitextMining (rus_Cyrl-tha_Thai)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.94861660079052
- type: f1
value: 94.66403162055336
- type: main_score
value: 94.66403162055336
- type: precision
value: 94.03820816864295
- type: recall
value: 95.94861660079052
task:
type: BitextMining
- dataset:
config: rus_Cyrl-zho_Hans
name: MTEB FloresBitextMining (rus_Cyrl-zho_Hans)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.4308300395257
- type: f1
value: 96.5909090909091
- type: main_score
value: 96.5909090909091
- type: precision
value: 96.17918313570487
- type: recall
value: 97.4308300395257
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ast_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ast_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 94.46640316205533
- type: f1
value: 92.86890645586297
- type: main_score
value: 92.86890645586297
- type: precision
value: 92.14756258234519
- type: recall
value: 94.46640316205533
task:
type: BitextMining
- dataset:
config: rus_Cyrl-crh_Latn
name: MTEB FloresBitextMining (rus_Cyrl-crh_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 94.66403162055336
- type: f1
value: 93.2663592446201
- type: main_score
value: 93.2663592446201
- type: precision
value: 92.66716073781292
- type: recall
value: 94.66403162055336
task:
type: BitextMining
- dataset:
config: rus_Cyrl-glg_Latn
name: MTEB FloresBitextMining (rus_Cyrl-glg_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.81422924901186
- type: f1
value: 98.46837944664031
- type: main_score
value: 98.46837944664031
- type: precision
value: 98.3201581027668
- type: recall
value: 98.81422924901186
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kas_Deva
name: MTEB FloresBitextMining (rus_Cyrl-kas_Deva)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 69.1699604743083
- type: f1
value: 63.05505292906477
- type: main_score
value: 63.05505292906477
- type: precision
value: 60.62594108789761
- type: recall
value: 69.1699604743083
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ltz_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ltz_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 91.40316205533597
- type: f1
value: 89.26571616789009
- type: main_score
value: 89.26571616789009
- type: precision
value: 88.40179747788443
- type: recall
value: 91.40316205533597
task:
type: BitextMining
- dataset:
config: rus_Cyrl-nus_Latn
name: MTEB FloresBitextMining (rus_Cyrl-nus_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 38.93280632411067
- type: f1
value: 33.98513032905371
- type: main_score
value: 33.98513032905371
- type: precision
value: 32.56257884802308
- type: recall
value: 38.93280632411067
task:
type: BitextMining
- dataset:
config: rus_Cyrl-slk_Latn
name: MTEB FloresBitextMining (rus_Cyrl-slk_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.42094861660078
- type: main_score
value: 97.42094861660078
- type: precision
value: 97.14262187088273
- type: recall
value: 98.02371541501977
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tir_Ethi
name: MTEB FloresBitextMining (rus_Cyrl-tir_Ethi)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 91.30434782608695
- type: f1
value: 88.78129117259552
- type: main_score
value: 88.78129117259552
- type: precision
value: 87.61528326745717
- type: recall
value: 91.30434782608695
task:
type: BitextMining
- dataset:
config: rus_Cyrl-zho_Hant
name: MTEB FloresBitextMining (rus_Cyrl-zho_Hant)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.1106719367589
- type: f1
value: 98.81422924901186
- type: main_score
value: 98.81422924901186
- type: precision
value: 98.66600790513834
- type: recall
value: 99.1106719367589
task:
type: BitextMining
- dataset:
config: rus_Cyrl-awa_Deva
name: MTEB FloresBitextMining (rus_Cyrl-awa_Deva)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.12252964426878
- type: f1
value: 97.70092226613966
- type: main_score
value: 97.70092226613966
- type: precision
value: 97.50494071146245
- type: recall
value: 98.12252964426878
task:
type: BitextMining
- dataset:
config: rus_Cyrl-cym_Latn
name: MTEB FloresBitextMining (rus_Cyrl-cym_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.94861660079052
- type: f1
value: 94.74308300395256
- type: main_score
value: 94.74308300395256
- type: precision
value: 94.20289855072464
- type: recall
value: 95.94861660079052
task:
type: BitextMining
- dataset:
config: rus_Cyrl-grn_Latn
name: MTEB FloresBitextMining (rus_Cyrl-grn_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 77.96442687747036
- type: f1
value: 73.64286789187975
- type: main_score
value: 73.64286789187975
- type: precision
value: 71.99324893260821
- type: recall
value: 77.96442687747036
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kat_Geor
name: MTEB FloresBitextMining (rus_Cyrl-kat_Geor)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.56719367588933
- type: main_score
value: 98.56719367588933
- type: precision
value: 98.40250329380764
- type: recall
value: 98.91304347826086
task:
type: BitextMining
- dataset:
config: rus_Cyrl-lua_Latn
name: MTEB FloresBitextMining (rus_Cyrl-lua_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 72.03557312252964
- type: f1
value: 67.23928163404449
- type: main_score
value: 67.23928163404449
- type: precision
value: 65.30797101449275
- type: recall
value: 72.03557312252964
task:
type: BitextMining
- dataset:
config: rus_Cyrl-nya_Latn
name: MTEB FloresBitextMining (rus_Cyrl-nya_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 92.29249011857708
- type: f1
value: 90.0494071146245
- type: main_score
value: 90.0494071146245
- type: precision
value: 89.04808959156786
- type: recall
value: 92.29249011857708
task:
type: BitextMining
- dataset:
config: rus_Cyrl-slv_Latn
name: MTEB FloresBitextMining (rus_Cyrl-slv_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.71541501976284
- type: f1
value: 98.30368906455863
- type: main_score
value: 98.30368906455863
- type: precision
value: 98.10606060606061
- type: recall
value: 98.71541501976284
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tpi_Latn
name: MTEB FloresBitextMining (rus_Cyrl-tpi_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 80.53359683794467
- type: f1
value: 76.59481822525301
- type: main_score
value: 76.59481822525301
- type: precision
value: 75.12913223140497
- type: recall
value: 80.53359683794467
task:
type: BitextMining
- dataset:
config: rus_Cyrl-zsm_Latn
name: MTEB FloresBitextMining (rus_Cyrl-zsm_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.33201581027669
- type: f1
value: 96.58620365142104
- type: main_score
value: 96.58620365142104
- type: precision
value: 96.26152832674572
- type: recall
value: 97.33201581027669
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ayr_Latn
name: MTEB FloresBitextMining (rus_Cyrl-ayr_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 45.55335968379446
- type: f1
value: 40.13076578531388
- type: main_score
value: 40.13076578531388
- type: precision
value: 38.398064362362355
- type: recall
value: 45.55335968379446
task:
type: BitextMining
- dataset:
config: rus_Cyrl-dan_Latn
name: MTEB FloresBitextMining (rus_Cyrl-dan_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.68247694334651
- type: main_score
value: 98.68247694334651
- type: precision
value: 98.51778656126481
- type: recall
value: 99.01185770750988
task:
type: BitextMining
- dataset:
config: rus_Cyrl-guj_Gujr
name: MTEB FloresBitextMining (rus_Cyrl-guj_Gujr)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.68247694334651
- type: main_score
value: 98.68247694334651
- type: precision
value: 98.51778656126481
- type: recall
value: 99.01185770750988
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kaz_Cyrl
name: MTEB FloresBitextMining (rus_Cyrl-kaz_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.81422924901186
- type: f1
value: 98.43544137022398
- type: main_score
value: 98.43544137022398
- type: precision
value: 98.25428194993412
- type: recall
value: 98.81422924901186
task:
type: BitextMining
- dataset:
config: rus_Cyrl-lug_Latn
name: MTEB FloresBitextMining (rus_Cyrl-lug_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 82.21343873517787
- type: f1
value: 77.97485726833554
- type: main_score
value: 77.97485726833554
- type: precision
value: 76.22376717485415
- type: recall
value: 82.21343873517787
task:
type: BitextMining
- dataset:
config: rus_Cyrl-oci_Latn
name: MTEB FloresBitextMining (rus_Cyrl-oci_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 93.87351778656127
- type: f1
value: 92.25319969885187
- type: main_score
value: 92.25319969885187
- type: precision
value: 91.5638528138528
- type: recall
value: 93.87351778656127
task:
type: BitextMining
- dataset:
config: rus_Cyrl-smo_Latn
name: MTEB FloresBitextMining (rus_Cyrl-smo_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 84.88142292490119
- type: f1
value: 81.24364765669114
- type: main_score
value: 81.24364765669114
- type: precision
value: 79.69991416137661
- type: recall
value: 84.88142292490119
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tsn_Latn
name: MTEB FloresBitextMining (rus_Cyrl-tsn_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 87.05533596837944
- type: f1
value: 83.90645586297761
- type: main_score
value: 83.90645586297761
- type: precision
value: 82.56752305665349
- type: recall
value: 87.05533596837944
task:
type: BitextMining
- dataset:
config: rus_Cyrl-zul_Latn
name: MTEB FloresBitextMining (rus_Cyrl-zul_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.15810276679841
- type: f1
value: 93.77140974967062
- type: main_score
value: 93.77140974967062
- type: precision
value: 93.16534914361002
- type: recall
value: 95.15810276679841
task:
type: BitextMining
- dataset:
config: rus_Cyrl-azb_Arab
name: MTEB FloresBitextMining (rus_Cyrl-azb_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 81.91699604743083
- type: f1
value: 77.18050065876152
- type: main_score
value: 77.18050065876152
- type: precision
value: 75.21519543258673
- type: recall
value: 81.91699604743083
task:
type: BitextMining
- dataset:
config: rus_Cyrl-deu_Latn
name: MTEB FloresBitextMining (rus_Cyrl-deu_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.50592885375494
- type: f1
value: 99.34123847167325
- type: main_score
value: 99.34123847167325
- type: precision
value: 99.2588932806324
- type: recall
value: 99.50592885375494
task:
type: BitextMining
- dataset:
config: rus_Cyrl-hat_Latn
name: MTEB FloresBitextMining (rus_Cyrl-hat_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 91.00790513833992
- type: f1
value: 88.69126043039086
- type: main_score
value: 88.69126043039086
- type: precision
value: 87.75774044795784
- type: recall
value: 91.00790513833992
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kbp_Latn
name: MTEB FloresBitextMining (rus_Cyrl-kbp_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 47.233201581027664
- type: f1
value: 43.01118618096943
- type: main_score
value: 43.01118618096943
- type: precision
value: 41.739069205043556
- type: recall
value: 47.233201581027664
task:
type: BitextMining
- dataset:
config: rus_Cyrl-luo_Latn
name: MTEB FloresBitextMining (rus_Cyrl-luo_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 60.47430830039525
- type: f1
value: 54.83210565429816
- type: main_score
value: 54.83210565429816
- type: precision
value: 52.81630744284779
- type: recall
value: 60.47430830039525
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ory_Orya
name: MTEB FloresBitextMining (rus_Cyrl-ory_Orya)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.1106719367589
- type: f1
value: 98.83069828722003
- type: main_score
value: 98.83069828722003
- type: precision
value: 98.69894598155467
- type: recall
value: 99.1106719367589
task:
type: BitextMining
- dataset:
config: rus_Cyrl-sna_Latn
name: MTEB FloresBitextMining (rus_Cyrl-sna_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 89.72332015810277
- type: f1
value: 87.30013645774514
- type: main_score
value: 87.30013645774514
- type: precision
value: 86.25329380764163
- type: recall
value: 89.72332015810277
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tso_Latn
name: MTEB FloresBitextMining (rus_Cyrl-tso_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 84.38735177865613
- type: f1
value: 80.70424744337788
- type: main_score
value: 80.70424744337788
- type: precision
value: 79.18560606060606
- type: recall
value: 84.38735177865613
task:
type: BitextMining
- dataset:
config: rus_Cyrl-azj_Latn
name: MTEB FloresBitextMining (rus_Cyrl-azj_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.33201581027669
- type: f1
value: 96.56455862977602
- type: main_score
value: 96.56455862977602
- type: precision
value: 96.23682476943345
- type: recall
value: 97.33201581027669
task:
type: BitextMining
- dataset:
config: rus_Cyrl-dik_Latn
name: MTEB FloresBitextMining (rus_Cyrl-dik_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 46.047430830039524
- type: f1
value: 40.05513069495283
- type: main_score
value: 40.05513069495283
- type: precision
value: 38.072590197096126
- type: recall
value: 46.047430830039524
task:
type: BitextMining
- dataset:
config: rus_Cyrl-hau_Latn
name: MTEB FloresBitextMining (rus_Cyrl-hau_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 87.94466403162056
- type: f1
value: 84.76943346508563
- type: main_score
value: 84.76943346508563
- type: precision
value: 83.34486166007905
- type: recall
value: 87.94466403162056
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kea_Latn
name: MTEB FloresBitextMining (rus_Cyrl-kea_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 89.42687747035573
- type: f1
value: 86.83803021747684
- type: main_score
value: 86.83803021747684
- type: precision
value: 85.78416149068323
- type: recall
value: 89.42687747035573
task:
type: BitextMining
- dataset:
config: rus_Cyrl-lus_Latn
name: MTEB FloresBitextMining (rus_Cyrl-lus_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 68.97233201581028
- type: f1
value: 64.05480726292745
- type: main_score
value: 64.05480726292745
- type: precision
value: 62.42670749487858
- type: recall
value: 68.97233201581028
task:
type: BitextMining
- dataset:
config: rus_Cyrl-pag_Latn
name: MTEB FloresBitextMining (rus_Cyrl-pag_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 78.75494071146245
- type: f1
value: 74.58573558401933
- type: main_score
value: 74.58573558401933
- type: precision
value: 73.05532028358115
- type: recall
value: 78.75494071146245
task:
type: BitextMining
- dataset:
config: rus_Cyrl-snd_Arab
name: MTEB FloresBitextMining (rus_Cyrl-snd_Arab)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.8498023715415
- type: f1
value: 94.56521739130434
- type: main_score
value: 94.56521739130434
- type: precision
value: 93.97233201581028
- type: recall
value: 95.8498023715415
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tuk_Latn
name: MTEB FloresBitextMining (rus_Cyrl-tuk_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 68.08300395256917
- type: f1
value: 62.93565240205557
- type: main_score
value: 62.93565240205557
- type: precision
value: 61.191590257043934
- type: recall
value: 68.08300395256917
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bak_Cyrl
name: MTEB FloresBitextMining (rus_Cyrl-bak_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.04743083003953
- type: f1
value: 94.86824769433464
- type: main_score
value: 94.86824769433464
- type: precision
value: 94.34288537549406
- type: recall
value: 96.04743083003953
task:
type: BitextMining
- dataset:
config: rus_Cyrl-dyu_Latn
name: MTEB FloresBitextMining (rus_Cyrl-dyu_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 37.45059288537549
- type: f1
value: 31.670482312800807
- type: main_score
value: 31.670482312800807
- type: precision
value: 29.99928568357422
- type: recall
value: 37.45059288537549
task:
type: BitextMining
- dataset:
config: rus_Cyrl-heb_Hebr
name: MTEB FloresBitextMining (rus_Cyrl-heb_Hebr)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.23320158102767
- type: f1
value: 96.38998682476942
- type: main_score
value: 96.38998682476942
- type: precision
value: 95.99802371541502
- type: recall
value: 97.23320158102767
task:
type: BitextMining
- dataset:
config: rus_Cyrl-khk_Cyrl
name: MTEB FloresBitextMining (rus_Cyrl-khk_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.41897233201581
- type: f1
value: 98.00724637681158
- type: main_score
value: 98.00724637681158
- type: precision
value: 97.82938076416336
- type: recall
value: 98.41897233201581
task:
type: BitextMining
- dataset:
config: rus_Cyrl-lvs_Latn
name: MTEB FloresBitextMining (rus_Cyrl-lvs_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.4308300395257
- type: f1
value: 96.61396574440053
- type: main_score
value: 96.61396574440053
- type: precision
value: 96.2203557312253
- type: recall
value: 97.4308300395257
task:
type: BitextMining
- dataset:
config: rus_Cyrl-pan_Guru
name: MTEB FloresBitextMining (rus_Cyrl-pan_Guru)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.07773386034256
- type: main_score
value: 99.07773386034256
- type: precision
value: 98.96245059288538
- type: recall
value: 99.30830039525692
task:
type: BitextMining
- dataset:
config: rus_Cyrl-som_Latn
name: MTEB FloresBitextMining (rus_Cyrl-som_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 87.74703557312253
- type: f1
value: 84.52898550724638
- type: main_score
value: 84.52898550724638
- type: precision
value: 83.09288537549409
- type: recall
value: 87.74703557312253
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tum_Latn
name: MTEB FloresBitextMining (rus_Cyrl-tum_Latn)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 87.15415019762845
- type: f1
value: 83.85069640504425
- type: main_score
value: 83.85069640504425
- type: precision
value: 82.43671183888576
- type: recall
value: 87.15415019762845
task:
type: BitextMining
- dataset:
config: taq_Latn-rus_Cyrl
name: MTEB FloresBitextMining (taq_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 28.55731225296443
- type: f1
value: 26.810726360049568
- type: main_score
value: 26.810726360049568
- type: precision
value: 26.260342858265577
- type: recall
value: 28.55731225296443
task:
type: BitextMining
- dataset:
config: war_Latn-rus_Cyrl
name: MTEB FloresBitextMining (war_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 94.86166007905138
- type: f1
value: 94.03147083483051
- type: main_score
value: 94.03147083483051
- type: precision
value: 93.70653606003322
- type: recall
value: 94.86166007905138
task:
type: BitextMining
- dataset:
config: arb_Arab-rus_Cyrl
name: MTEB FloresBitextMining (arb_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.34387351778656
- type: f1
value: 95.23056653491436
- type: main_score
value: 95.23056653491436
- type: precision
value: 94.70520421607378
- type: recall
value: 96.34387351778656
task:
type: BitextMining
- dataset:
config: bul_Cyrl-rus_Cyrl
name: MTEB FloresBitextMining (bul_Cyrl-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.90118577075098
- type: f1
value: 99.86824769433464
- type: main_score
value: 99.86824769433464
- type: precision
value: 99.85177865612648
- type: recall
value: 99.90118577075098
task:
type: BitextMining
- dataset:
config: fra_Latn-rus_Cyrl
name: MTEB FloresBitextMining (fra_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
task:
type: BitextMining
- dataset:
config: jpn_Jpan-rus_Cyrl
name: MTEB FloresBitextMining (jpn_Jpan-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.3201581027668
- type: f1
value: 97.76021080368905
- type: main_score
value: 97.76021080368905
- type: precision
value: 97.48023715415019
- type: recall
value: 98.3201581027668
task:
type: BitextMining
- dataset:
config: lij_Latn-rus_Cyrl
name: MTEB FloresBitextMining (lij_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 83.49802371541502
- type: f1
value: 81.64800059239636
- type: main_score
value: 81.64800059239636
- type: precision
value: 80.9443055878478
- type: recall
value: 83.49802371541502
task:
type: BitextMining
- dataset:
config: mya_Mymr-rus_Cyrl
name: MTEB FloresBitextMining (mya_Mymr-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 90.21739130434783
- type: f1
value: 88.76776366313682
- type: main_score
value: 88.76776366313682
- type: precision
value: 88.18370446119435
- type: recall
value: 90.21739130434783
task:
type: BitextMining
- dataset:
config: sag_Latn-rus_Cyrl
name: MTEB FloresBitextMining (sag_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 41.699604743083
- type: f1
value: 39.53066322643847
- type: main_score
value: 39.53066322643847
- type: precision
value: 38.822876239229274
- type: recall
value: 41.699604743083
task:
type: BitextMining
- dataset:
config: taq_Tfng-rus_Cyrl
name: MTEB FloresBitextMining (taq_Tfng-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 10.67193675889328
- type: f1
value: 9.205744965817951
- type: main_score
value: 9.205744965817951
- type: precision
value: 8.85195219073817
- type: recall
value: 10.67193675889328
task:
type: BitextMining
- dataset:
config: wol_Latn-rus_Cyrl
name: MTEB FloresBitextMining (wol_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 63.537549407114625
- type: f1
value: 60.65190727391827
- type: main_score
value: 60.65190727391827
- type: precision
value: 59.61144833427442
- type: recall
value: 63.537549407114625
task:
type: BitextMining
- dataset:
config: arb_Latn-rus_Cyrl
name: MTEB FloresBitextMining (arb_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 13.142292490118576
- type: f1
value: 12.372910318176764
- type: main_score
value: 12.372910318176764
- type: precision
value: 12.197580895919188
- type: recall
value: 13.142292490118576
task:
type: BitextMining
- dataset:
config: cat_Latn-rus_Cyrl
name: MTEB FloresBitextMining (cat_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.80599472990777
- type: main_score
value: 98.80599472990777
- type: precision
value: 98.72953133822698
- type: recall
value: 99.01185770750988
task:
type: BitextMining
- dataset:
config: fur_Latn-rus_Cyrl
name: MTEB FloresBitextMining (fur_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 81.02766798418972
- type: f1
value: 79.36184294084613
- type: main_score
value: 79.36184294084613
- type: precision
value: 78.69187826527705
- type: recall
value: 81.02766798418972
task:
type: BitextMining
- dataset:
config: kab_Latn-rus_Cyrl
name: MTEB FloresBitextMining (kab_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 34.387351778656125
- type: f1
value: 32.02306921576947
- type: main_score
value: 32.02306921576947
- type: precision
value: 31.246670347137467
- type: recall
value: 34.387351778656125
task:
type: BitextMining
- dataset:
config: lim_Latn-rus_Cyrl
name: MTEB FloresBitextMining (lim_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 78.26086956521739
- type: f1
value: 75.90239449214359
- type: main_score
value: 75.90239449214359
- type: precision
value: 75.02211430745493
- type: recall
value: 78.26086956521739
task:
type: BitextMining
- dataset:
config: nld_Latn-rus_Cyrl
name: MTEB FloresBitextMining (nld_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
task:
type: BitextMining
- dataset:
config: san_Deva-rus_Cyrl
name: MTEB FloresBitextMining (san_Deva-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 87.94466403162056
- type: f1
value: 86.68928897189767
- type: main_score
value: 86.68928897189767
- type: precision
value: 86.23822997079216
- type: recall
value: 87.94466403162056
task:
type: BitextMining
- dataset:
config: tat_Cyrl-rus_Cyrl
name: MTEB FloresBitextMining (tat_Cyrl-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.03557312252964
- type: f1
value: 96.4167365353136
- type: main_score
value: 96.4167365353136
- type: precision
value: 96.16847826086958
- type: recall
value: 97.03557312252964
task:
type: BitextMining
- dataset:
config: xho_Latn-rus_Cyrl
name: MTEB FloresBitextMining (xho_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 86.95652173913044
- type: f1
value: 85.5506497283435
- type: main_score
value: 85.5506497283435
- type: precision
value: 84.95270479733395
- type: recall
value: 86.95652173913044
task:
type: BitextMining
- dataset:
config: ars_Arab-rus_Cyrl
name: MTEB FloresBitextMining (ars_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 96.6403162055336
- type: f1
value: 95.60935441370223
- type: main_score
value: 95.60935441370223
- type: precision
value: 95.13339920948617
- type: recall
value: 96.6403162055336
task:
type: BitextMining
- dataset:
config: ceb_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ceb_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.7509881422925
- type: f1
value: 95.05209198303827
- type: main_score
value: 95.05209198303827
- type: precision
value: 94.77662283368805
- type: recall
value: 95.7509881422925
task:
type: BitextMining
- dataset:
config: fuv_Latn-rus_Cyrl
name: MTEB FloresBitextMining (fuv_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 45.25691699604743
- type: f1
value: 42.285666666742365
- type: main_score
value: 42.285666666742365
- type: precision
value: 41.21979853402283
- type: recall
value: 45.25691699604743
task:
type: BitextMining
- dataset:
config: kac_Latn-rus_Cyrl
name: MTEB FloresBitextMining (kac_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 34.683794466403164
- type: f1
value: 33.3235346229031
- type: main_score
value: 33.3235346229031
- type: precision
value: 32.94673924616852
- type: recall
value: 34.683794466403164
task:
type: BitextMining
- dataset:
config: lin_Latn-rus_Cyrl
name: MTEB FloresBitextMining (lin_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 86.85770750988142
- type: f1
value: 85.1867110799439
- type: main_score
value: 85.1867110799439
- type: precision
value: 84.53038212173273
- type: recall
value: 86.85770750988142
task:
type: BitextMining
- dataset:
config: nno_Latn-rus_Cyrl
name: MTEB FloresBitextMining (nno_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.4308300395257
- type: f1
value: 96.78383210991906
- type: main_score
value: 96.78383210991906
- type: precision
value: 96.51185770750989
- type: recall
value: 97.4308300395257
task:
type: BitextMining
- dataset:
config: sat_Olck-rus_Cyrl
name: MTEB FloresBitextMining (sat_Olck-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 1.185770750988142
- type: f1
value: 1.0279253129117258
- type: main_score
value: 1.0279253129117258
- type: precision
value: 1.0129746819135175
- type: recall
value: 1.185770750988142
task:
type: BitextMining
- dataset:
config: tel_Telu-rus_Cyrl
name: MTEB FloresBitextMining (tel_Telu-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.12252964426878
- type: f1
value: 97.61198945981555
- type: main_score
value: 97.61198945981555
- type: precision
value: 97.401185770751
- type: recall
value: 98.12252964426878
task:
type: BitextMining
- dataset:
config: ydd_Hebr-rus_Cyrl
name: MTEB FloresBitextMining (ydd_Hebr-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 75.8893280632411
- type: f1
value: 74.00244008018511
- type: main_score
value: 74.00244008018511
- type: precision
value: 73.25683020960382
- type: recall
value: 75.8893280632411
task:
type: BitextMining
- dataset:
config: ary_Arab-rus_Cyrl
name: MTEB FloresBitextMining (ary_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 86.56126482213439
- type: f1
value: 83.72796285839765
- type: main_score
value: 83.72796285839765
- type: precision
value: 82.65014273166447
- type: recall
value: 86.56126482213439
task:
type: BitextMining
- dataset:
config: ces_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ces_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.4729907773386
- type: main_score
value: 99.4729907773386
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
task:
type: BitextMining
- dataset:
config: gaz_Latn-rus_Cyrl
name: MTEB FloresBitextMining (gaz_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 42.58893280632411
- type: f1
value: 40.75832866805978
- type: main_score
value: 40.75832866805978
- type: precision
value: 40.14285046917723
- type: recall
value: 42.58893280632411
task:
type: BitextMining
- dataset:
config: kam_Latn-rus_Cyrl
name: MTEB FloresBitextMining (kam_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 45.25691699604743
- type: f1
value: 42.6975518029456
- type: main_score
value: 42.6975518029456
- type: precision
value: 41.87472710984596
- type: recall
value: 45.25691699604743
task:
type: BitextMining
- dataset:
config: lit_Latn-rus_Cyrl
name: MTEB FloresBitextMining (lit_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.33201581027669
- type: f1
value: 96.62384716732542
- type: main_score
value: 96.62384716732542
- type: precision
value: 96.3175230566535
- type: recall
value: 97.33201581027669
task:
type: BitextMining
- dataset:
config: nob_Latn-rus_Cyrl
name: MTEB FloresBitextMining (nob_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.71541501976284
- type: f1
value: 98.30368906455863
- type: main_score
value: 98.30368906455863
- type: precision
value: 98.10606060606061
- type: recall
value: 98.71541501976284
task:
type: BitextMining
- dataset:
config: scn_Latn-rus_Cyrl
name: MTEB FloresBitextMining (scn_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 70.45454545454545
- type: f1
value: 68.62561022640075
- type: main_score
value: 68.62561022640075
- type: precision
value: 67.95229103411222
- type: recall
value: 70.45454545454545
task:
type: BitextMining
- dataset:
config: tgk_Cyrl-rus_Cyrl
name: MTEB FloresBitextMining (tgk_Cyrl-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 92.4901185770751
- type: f1
value: 91.58514492753623
- type: main_score
value: 91.58514492753623
- type: precision
value: 91.24759298672342
- type: recall
value: 92.4901185770751
task:
type: BitextMining
- dataset:
config: yor_Latn-rus_Cyrl
name: MTEB FloresBitextMining (yor_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 67.98418972332016
- type: f1
value: 64.72874247330768
- type: main_score
value: 64.72874247330768
- type: precision
value: 63.450823399938685
- type: recall
value: 67.98418972332016
task:
type: BitextMining
- dataset:
config: arz_Arab-rus_Cyrl
name: MTEB FloresBitextMining (arz_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 94.56521739130434
- type: f1
value: 93.07971014492755
- type: main_score
value: 93.07971014492755
- type: precision
value: 92.42753623188406
- type: recall
value: 94.56521739130434
task:
type: BitextMining
- dataset:
config: cjk_Latn-rus_Cyrl
name: MTEB FloresBitextMining (cjk_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 38.63636363636363
- type: f1
value: 36.25747140862938
- type: main_score
value: 36.25747140862938
- type: precision
value: 35.49101355074723
- type: recall
value: 38.63636363636363
task:
type: BitextMining
- dataset:
config: gla_Latn-rus_Cyrl
name: MTEB FloresBitextMining (gla_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 69.26877470355731
- type: f1
value: 66.11797423328613
- type: main_score
value: 66.11797423328613
- type: precision
value: 64.89369649409694
- type: recall
value: 69.26877470355731
task:
type: BitextMining
- dataset:
config: kan_Knda-rus_Cyrl
name: MTEB FloresBitextMining (kan_Knda-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.51505740636176
- type: main_score
value: 97.51505740636176
- type: precision
value: 97.30731225296442
- type: recall
value: 98.02371541501977
task:
type: BitextMining
- dataset:
config: lmo_Latn-rus_Cyrl
name: MTEB FloresBitextMining (lmo_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 73.3201581027668
- type: f1
value: 71.06371608677273
- type: main_score
value: 71.06371608677273
- type: precision
value: 70.26320288266223
- type: recall
value: 73.3201581027668
task:
type: BitextMining
- dataset:
config: npi_Deva-rus_Cyrl
name: MTEB FloresBitextMining (npi_Deva-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.36645107198466
- type: main_score
value: 97.36645107198466
- type: precision
value: 97.1772068511199
- type: recall
value: 97.82608695652173
task:
type: BitextMining
- dataset:
config: shn_Mymr-rus_Cyrl
name: MTEB FloresBitextMining (shn_Mymr-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 39.426877470355734
- type: f1
value: 37.16728785513024
- type: main_score
value: 37.16728785513024
- type: precision
value: 36.56918548278505
- type: recall
value: 39.426877470355734
task:
type: BitextMining
- dataset:
config: tgl_Latn-rus_Cyrl
name: MTEB FloresBitextMining (tgl_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.92490118577075
- type: f1
value: 97.6378693769998
- type: main_score
value: 97.6378693769998
- type: precision
value: 97.55371440154047
- type: recall
value: 97.92490118577075
task:
type: BitextMining
- dataset:
config: yue_Hant-rus_Cyrl
name: MTEB FloresBitextMining (yue_Hant-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.92490118577075
- type: f1
value: 97.3833051006964
- type: main_score
value: 97.3833051006964
- type: precision
value: 97.1590909090909
- type: recall
value: 97.92490118577075
task:
type: BitextMining
- dataset:
config: asm_Beng-rus_Cyrl
name: MTEB FloresBitextMining (asm_Beng-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 92.78656126482213
- type: f1
value: 91.76917395296842
- type: main_score
value: 91.76917395296842
- type: precision
value: 91.38292866553736
- type: recall
value: 92.78656126482213
task:
type: BitextMining
- dataset:
config: ckb_Arab-rus_Cyrl
name: MTEB FloresBitextMining (ckb_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 80.8300395256917
- type: f1
value: 79.17664345468799
- type: main_score
value: 79.17664345468799
- type: precision
value: 78.5622171683459
- type: recall
value: 80.8300395256917
task:
type: BitextMining
- dataset:
config: gle_Latn-rus_Cyrl
name: MTEB FloresBitextMining (gle_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 85.86956521739131
- type: f1
value: 84.45408265372492
- type: main_score
value: 84.45408265372492
- type: precision
value: 83.8774340026703
- type: recall
value: 85.86956521739131
task:
type: BitextMining
- dataset:
config: kas_Arab-rus_Cyrl
name: MTEB FloresBitextMining (kas_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 76.28458498023716
- type: f1
value: 74.11216313578267
- type: main_score
value: 74.11216313578267
- type: precision
value: 73.2491277759584
- type: recall
value: 76.28458498023716
task:
type: BitextMining
- dataset:
config: ltg_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ltg_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 71.14624505928853
- type: f1
value: 68.69245357723618
- type: main_score
value: 68.69245357723618
- type: precision
value: 67.8135329666459
- type: recall
value: 71.14624505928853
task:
type: BitextMining
- dataset:
config: nso_Latn-rus_Cyrl
name: MTEB FloresBitextMining (nso_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 87.64822134387352
- type: f1
value: 85.98419219986725
- type: main_score
value: 85.98419219986725
- type: precision
value: 85.32513873917036
- type: recall
value: 87.64822134387352
task:
type: BitextMining
- dataset:
config: sin_Sinh-rus_Cyrl
name: MTEB FloresBitextMining (sin_Sinh-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.62845849802372
- type: f1
value: 97.10144927536231
- type: main_score
value: 97.10144927536231
- type: precision
value: 96.87986585219788
- type: recall
value: 97.62845849802372
task:
type: BitextMining
- dataset:
config: tha_Thai-rus_Cyrl
name: MTEB FloresBitextMining (tha_Thai-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.71541501976284
- type: f1
value: 98.28722002635045
- type: main_score
value: 98.28722002635045
- type: precision
value: 98.07312252964427
- type: recall
value: 98.71541501976284
task:
type: BitextMining
- dataset:
config: zho_Hans-rus_Cyrl
name: MTEB FloresBitextMining (zho_Hans-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.68247694334651
- type: main_score
value: 98.68247694334651
- type: precision
value: 98.51778656126481
- type: recall
value: 99.01185770750988
task:
type: BitextMining
- dataset:
config: ast_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ast_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.65217391304348
- type: f1
value: 94.90649683857505
- type: main_score
value: 94.90649683857505
- type: precision
value: 94.61352657004831
- type: recall
value: 95.65217391304348
task:
type: BitextMining
- dataset:
config: crh_Latn-rus_Cyrl
name: MTEB FloresBitextMining (crh_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 93.08300395256917
- type: f1
value: 92.20988998886428
- type: main_score
value: 92.20988998886428
- type: precision
value: 91.85631013694254
- type: recall
value: 93.08300395256917
task:
type: BitextMining
- dataset:
config: glg_Latn-rus_Cyrl
name: MTEB FloresBitextMining (glg_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.55335968379447
- type: f1
value: 95.18006148440931
- type: main_score
value: 95.18006148440931
- type: precision
value: 95.06540560888386
- type: recall
value: 95.55335968379447
task:
type: BitextMining
- dataset:
config: kas_Deva-rus_Cyrl
name: MTEB FloresBitextMining (kas_Deva-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 55.03952569169961
- type: f1
value: 52.19871938895554
- type: main_score
value: 52.19871938895554
- type: precision
value: 51.17660971469557
- type: recall
value: 55.03952569169961
task:
type: BitextMining
- dataset:
config: ltz_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ltz_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 87.64822134387352
- type: f1
value: 86.64179841897234
- type: main_score
value: 86.64179841897234
- type: precision
value: 86.30023235431587
- type: recall
value: 87.64822134387352
task:
type: BitextMining
- dataset:
config: nus_Latn-rus_Cyrl
name: MTEB FloresBitextMining (nus_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 27.4703557312253
- type: f1
value: 25.703014277858088
- type: main_score
value: 25.703014277858088
- type: precision
value: 25.194105476917315
- type: recall
value: 27.4703557312253
task:
type: BitextMining
- dataset:
config: slk_Latn-rus_Cyrl
name: MTEB FloresBitextMining (slk_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.1106719367589
- type: main_score
value: 99.1106719367589
- type: precision
value: 99.02832674571805
- type: recall
value: 99.30830039525692
task:
type: BitextMining
- dataset:
config: tir_Ethi-rus_Cyrl
name: MTEB FloresBitextMining (tir_Ethi-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 80.73122529644269
- type: f1
value: 78.66903754775608
- type: main_score
value: 78.66903754775608
- type: precision
value: 77.86431694163612
- type: recall
value: 80.73122529644269
task:
type: BitextMining
- dataset:
config: zho_Hant-rus_Cyrl
name: MTEB FloresBitextMining (zho_Hant-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.22134387351778
- type: f1
value: 97.66798418972333
- type: main_score
value: 97.66798418972333
- type: precision
value: 97.40612648221344
- type: recall
value: 98.22134387351778
task:
type: BitextMining
- dataset:
config: awa_Deva-rus_Cyrl
name: MTEB FloresBitextMining (awa_Deva-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.5296442687747
- type: f1
value: 96.94224857268335
- type: main_score
value: 96.94224857268335
- type: precision
value: 96.68560606060606
- type: recall
value: 97.5296442687747
task:
type: BitextMining
- dataset:
config: cym_Latn-rus_Cyrl
name: MTEB FloresBitextMining (cym_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 92.68774703557312
- type: f1
value: 91.69854302097961
- type: main_score
value: 91.69854302097961
- type: precision
value: 91.31236846157795
- type: recall
value: 92.68774703557312
task:
type: BitextMining
- dataset:
config: grn_Latn-rus_Cyrl
name: MTEB FloresBitextMining (grn_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 64.13043478260869
- type: f1
value: 61.850586118740004
- type: main_score
value: 61.850586118740004
- type: precision
value: 61.0049495186209
- type: recall
value: 64.13043478260869
task:
type: BitextMining
- dataset:
config: kat_Geor-rus_Cyrl
name: MTEB FloresBitextMining (kat_Geor-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.59881422924902
- type: main_score
value: 97.59881422924902
- type: precision
value: 97.42534036012296
- type: recall
value: 98.02371541501977
task:
type: BitextMining
- dataset:
config: lua_Latn-rus_Cyrl
name: MTEB FloresBitextMining (lua_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 63.63636363636363
- type: f1
value: 60.9709122526128
- type: main_score
value: 60.9709122526128
- type: precision
value: 60.03915902282226
- type: recall
value: 63.63636363636363
task:
type: BitextMining
- dataset:
config: nya_Latn-rus_Cyrl
name: MTEB FloresBitextMining (nya_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 89.2292490118577
- type: f1
value: 87.59723824473149
- type: main_score
value: 87.59723824473149
- type: precision
value: 86.90172707867349
- type: recall
value: 89.2292490118577
task:
type: BitextMining
- dataset:
config: slv_Latn-rus_Cyrl
name: MTEB FloresBitextMining (slv_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.74835309617917
- type: main_score
value: 98.74835309617917
- type: precision
value: 98.63636363636364
- type: recall
value: 99.01185770750988
task:
type: BitextMining
- dataset:
config: tpi_Latn-rus_Cyrl
name: MTEB FloresBitextMining (tpi_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 77.37154150197628
- type: f1
value: 75.44251611276084
- type: main_score
value: 75.44251611276084
- type: precision
value: 74.78103665109595
- type: recall
value: 77.37154150197628
task:
type: BitextMining
- dataset:
config: zsm_Latn-rus_Cyrl
name: MTEB FloresBitextMining (zsm_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.96245059288538
- type: main_score
value: 98.96245059288538
- type: precision
value: 98.8471673254282
- type: recall
value: 99.2094861660079
task:
type: BitextMining
- dataset:
config: ayr_Latn-rus_Cyrl
name: MTEB FloresBitextMining (ayr_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 27.766798418972332
- type: f1
value: 26.439103195281312
- type: main_score
value: 26.439103195281312
- type: precision
value: 26.052655604573964
- type: recall
value: 27.766798418972332
task:
type: BitextMining
- dataset:
config: dan_Latn-rus_Cyrl
name: MTEB FloresBitextMining (dan_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.07773386034255
- type: main_score
value: 99.07773386034255
- type: precision
value: 98.96245059288538
- type: recall
value: 99.30830039525692
task:
type: BitextMining
- dataset:
config: guj_Gujr-rus_Cyrl
name: MTEB FloresBitextMining (guj_Gujr-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.26449275362317
- type: main_score
value: 97.26449275362317
- type: precision
value: 97.02498588368154
- type: recall
value: 97.82608695652173
task:
type: BitextMining
- dataset:
config: kaz_Cyrl-rus_Cyrl
name: MTEB FloresBitextMining (kaz_Cyrl-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.5296442687747
- type: f1
value: 97.03557312252964
- type: main_score
value: 97.03557312252964
- type: precision
value: 96.85022158342316
- type: recall
value: 97.5296442687747
task:
type: BitextMining
- dataset:
config: lug_Latn-rus_Cyrl
name: MTEB FloresBitextMining (lug_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 68.57707509881423
- type: f1
value: 65.93361605820395
- type: main_score
value: 65.93361605820395
- type: precision
value: 64.90348248593789
- type: recall
value: 68.57707509881423
task:
type: BitextMining
- dataset:
config: oci_Latn-rus_Cyrl
name: MTEB FloresBitextMining (oci_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 86.26482213438736
- type: f1
value: 85.33176417155623
- type: main_score
value: 85.33176417155623
- type: precision
value: 85.00208833384637
- type: recall
value: 86.26482213438736
task:
type: BitextMining
- dataset:
config: smo_Latn-rus_Cyrl
name: MTEB FloresBitextMining (smo_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 77.96442687747036
- type: f1
value: 75.70960450188885
- type: main_score
value: 75.70960450188885
- type: precision
value: 74.8312632736777
- type: recall
value: 77.96442687747036
task:
type: BitextMining
- dataset:
config: tsn_Latn-rus_Cyrl
name: MTEB FloresBitextMining (tsn_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 84.38735177865613
- type: f1
value: 82.13656376349225
- type: main_score
value: 82.13656376349225
- type: precision
value: 81.16794543904518
- type: recall
value: 84.38735177865613
task:
type: BitextMining
- dataset:
config: zul_Latn-rus_Cyrl
name: MTEB FloresBitextMining (zul_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 90.21739130434783
- type: f1
value: 88.77570602050753
- type: main_score
value: 88.77570602050753
- type: precision
value: 88.15978104021582
- type: recall
value: 90.21739130434783
task:
type: BitextMining
- dataset:
config: azb_Arab-rus_Cyrl
name: MTEB FloresBitextMining (azb_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 65.71146245059289
- type: f1
value: 64.18825390221271
- type: main_score
value: 64.18825390221271
- type: precision
value: 63.66811154793568
- type: recall
value: 65.71146245059289
task:
type: BitextMining
- dataset:
config: deu_Latn-rus_Cyrl
name: MTEB FloresBitextMining (deu_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 99.70355731225297
- type: f1
value: 99.60474308300395
- type: main_score
value: 99.60474308300395
- type: precision
value: 99.55533596837944
- type: recall
value: 99.70355731225297
task:
type: BitextMining
- dataset:
config: hat_Latn-rus_Cyrl
name: MTEB FloresBitextMining (hat_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 86.7588932806324
- type: f1
value: 85.86738623695146
- type: main_score
value: 85.86738623695146
- type: precision
value: 85.55235467420822
- type: recall
value: 86.7588932806324
task:
type: BitextMining
- dataset:
config: kbp_Latn-rus_Cyrl
name: MTEB FloresBitextMining (kbp_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 34.88142292490119
- type: f1
value: 32.16511669463015
- type: main_score
value: 32.16511669463015
- type: precision
value: 31.432098549546318
- type: recall
value: 34.88142292490119
task:
type: BitextMining
- dataset:
config: luo_Latn-rus_Cyrl
name: MTEB FloresBitextMining (luo_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 52.27272727272727
- type: f1
value: 49.60489626836975
- type: main_score
value: 49.60489626836975
- type: precision
value: 48.69639631803339
- type: recall
value: 52.27272727272727
task:
type: BitextMining
- dataset:
config: ory_Orya-rus_Cyrl
name: MTEB FloresBitextMining (ory_Orya-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.27437417654808
- type: main_score
value: 97.27437417654808
- type: precision
value: 97.04968944099377
- type: recall
value: 97.82608695652173
task:
type: BitextMining
- dataset:
config: sna_Latn-rus_Cyrl
name: MTEB FloresBitextMining (sna_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 85.37549407114624
- type: f1
value: 83.09911316305177
- type: main_score
value: 83.09911316305177
- type: precision
value: 82.1284950958864
- type: recall
value: 85.37549407114624
task:
type: BitextMining
- dataset:
config: tso_Latn-rus_Cyrl
name: MTEB FloresBitextMining (tso_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 82.90513833992095
- type: f1
value: 80.28290385503824
- type: main_score
value: 80.28290385503824
- type: precision
value: 79.23672543237761
- type: recall
value: 82.90513833992095
task:
type: BitextMining
- dataset:
config: azj_Latn-rus_Cyrl
name: MTEB FloresBitextMining (azj_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.49200075287031
- type: main_score
value: 97.49200075287031
- type: precision
value: 97.266139657444
- type: recall
value: 98.02371541501977
task:
type: BitextMining
- dataset:
config: dik_Latn-rus_Cyrl
name: MTEB FloresBitextMining (dik_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 38.43873517786561
- type: f1
value: 35.78152442955223
- type: main_score
value: 35.78152442955223
- type: precision
value: 34.82424325078237
- type: recall
value: 38.43873517786561
task:
type: BitextMining
- dataset:
config: hau_Latn-rus_Cyrl
name: MTEB FloresBitextMining (hau_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 81.42292490118577
- type: f1
value: 79.24612283124593
- type: main_score
value: 79.24612283124593
- type: precision
value: 78.34736070751448
- type: recall
value: 81.42292490118577
task:
type: BitextMining
- dataset:
config: kea_Latn-rus_Cyrl
name: MTEB FloresBitextMining (kea_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 81.62055335968378
- type: f1
value: 80.47015182884748
- type: main_score
value: 80.47015182884748
- type: precision
value: 80.02671028885862
- type: recall
value: 81.62055335968378
task:
type: BitextMining
- dataset:
config: lus_Latn-rus_Cyrl
name: MTEB FloresBitextMining (lus_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 62.74703557312253
- type: f1
value: 60.53900079111122
- type: main_score
value: 60.53900079111122
- type: precision
value: 59.80024202850289
- type: recall
value: 62.74703557312253
task:
type: BitextMining
- dataset:
config: pag_Latn-rus_Cyrl
name: MTEB FloresBitextMining (pag_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 74.01185770750988
- type: f1
value: 72.57280648279529
- type: main_score
value: 72.57280648279529
- type: precision
value: 71.99952968456789
- type: recall
value: 74.01185770750988
task:
type: BitextMining
- dataset:
config: snd_Arab-rus_Cyrl
name: MTEB FloresBitextMining (snd_Arab-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 91.30434782608695
- type: f1
value: 90.24653499445358
- type: main_score
value: 90.24653499445358
- type: precision
value: 89.83134068200232
- type: recall
value: 91.30434782608695
task:
type: BitextMining
- dataset:
config: tuk_Latn-rus_Cyrl
name: MTEB FloresBitextMining (tuk_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 47.62845849802372
- type: f1
value: 45.812928836644254
- type: main_score
value: 45.812928836644254
- type: precision
value: 45.23713833170355
- type: recall
value: 47.62845849802372
task:
type: BitextMining
- dataset:
config: bak_Cyrl-rus_Cyrl
name: MTEB FloresBitextMining (bak_Cyrl-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.8498023715415
- type: f1
value: 95.18904459615922
- type: main_score
value: 95.18904459615922
- type: precision
value: 94.92812441182006
- type: recall
value: 95.8498023715415
task:
type: BitextMining
- dataset:
config: dyu_Latn-rus_Cyrl
name: MTEB FloresBitextMining (dyu_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 29.64426877470356
- type: f1
value: 27.287335193938166
- type: main_score
value: 27.287335193938166
- type: precision
value: 26.583996026587492
- type: recall
value: 29.64426877470356
task:
type: BitextMining
- dataset:
config: heb_Hebr-rus_Cyrl
name: MTEB FloresBitextMining (heb_Hebr-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.55072463768116
- type: main_score
value: 98.55072463768116
- type: precision
value: 98.36956521739131
- type: recall
value: 98.91304347826086
task:
type: BitextMining
- dataset:
config: khk_Cyrl-rus_Cyrl
name: MTEB FloresBitextMining (khk_Cyrl-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 95.15810276679841
- type: f1
value: 94.44009547764487
- type: main_score
value: 94.44009547764487
- type: precision
value: 94.16579797014579
- type: recall
value: 95.15810276679841
task:
type: BitextMining
- dataset:
config: lvs_Latn-rus_Cyrl
name: MTEB FloresBitextMining (lvs_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.92490118577075
- type: f1
value: 97.51467241585817
- type: main_score
value: 97.51467241585817
- type: precision
value: 97.36166007905138
- type: recall
value: 97.92490118577075
task:
type: BitextMining
- dataset:
config: pan_Guru-rus_Cyrl
name: MTEB FloresBitextMining (pan_Guru-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 97.92490118577075
- type: f1
value: 97.42918313570486
- type: main_score
value: 97.42918313570486
- type: precision
value: 97.22261434217955
- type: recall
value: 97.92490118577075
task:
type: BitextMining
- dataset:
config: som_Latn-rus_Cyrl
name: MTEB FloresBitextMining (som_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 75.69169960474308
- type: f1
value: 73.7211667065916
- type: main_score
value: 73.7211667065916
- type: precision
value: 72.95842401892384
- type: recall
value: 75.69169960474308
task:
type: BitextMining
- dataset:
config: tum_Latn-rus_Cyrl
name: MTEB FloresBitextMining (tum_Latn-rus_Cyrl)
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
split: devtest
type: mteb/flores
metrics:
- type: accuracy
value: 85.67193675889328
- type: f1
value: 82.9296066252588
- type: main_score
value: 82.9296066252588
- type: precision
value: 81.77330225447936
- type: recall
value: 85.67193675889328
task:
type: BitextMining
- dataset:
config: default
name: MTEB GeoreviewClassification (default)
revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
split: test
type: ai-forever/georeview-classification
metrics:
- type: accuracy
value: 44.6630859375
- type: f1
value: 42.607425073610536
- type: f1_weighted
value: 42.60639474586065
- type: main_score
value: 44.6630859375
task:
type: Classification
- dataset:
config: default
name: MTEB GeoreviewClusteringP2P (default)
revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
split: test
type: ai-forever/georeview-clustering-p2p
metrics:
- type: main_score
value: 58.15951247070825
- type: v_measure
value: 58.15951247070825
- type: v_measure_std
value: 0.6739615788288809
task:
type: Clustering
- dataset:
config: default
name: MTEB HeadlineClassification (default)
revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
split: test
type: ai-forever/headline-classification
metrics:
- type: accuracy
value: 73.935546875
- type: f1
value: 73.8654872186846
- type: f1_weighted
value: 73.86733122685095
- type: main_score
value: 73.935546875
task:
type: Classification
- dataset:
config: default
name: MTEB InappropriatenessClassification (default)
revision: 601651fdc45ef243751676e62dd7a19f491c0285
split: test
type: ai-forever/inappropriateness-classification
metrics:
- type: accuracy
value: 59.16015624999999
- type: ap
value: 55.52276605836938
- type: ap_weighted
value: 55.52276605836938
- type: f1
value: 58.614248199637956
- type: f1_weighted
value: 58.614248199637956
- type: main_score
value: 59.16015624999999
task:
type: Classification
- dataset:
config: default
name: MTEB KinopoiskClassification (default)
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
split: test
type: ai-forever/kinopoisk-sentiment-classification
metrics:
- type: accuracy
value: 49.959999999999994
- type: f1
value: 48.4900332316098
- type: f1_weighted
value: 48.4900332316098
- type: main_score
value: 49.959999999999994
task:
type: Classification
- dataset:
config: default
name: MTEB LanguageClassification (default)
revision: aa56583bf2bc52b0565770607d6fc3faebecf9e2
split: test
type: papluca/language-identification
metrics:
- type: accuracy
value: 71.005859375
- type: f1
value: 69.63481100303348
- type: f1_weighted
value: 69.64640413409529
- type: main_score
value: 71.005859375
task:
type: Classification
- dataset:
config: ru
name: MTEB MLSUMClusteringP2P (ru)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 42.11280087032343
- type: v_measure
value: 42.11280087032343
- type: v_measure_std
value: 6.7619971723605135
task:
type: Clustering
- dataset:
config: ru
name: MTEB MLSUMClusteringP2P.v2 (ru)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 43.00112546945811
- type: v_measure
value: 43.00112546945811
- type: v_measure_std
value: 1.4740560414835675
task:
type: Clustering
- dataset:
config: ru
name: MTEB MLSUMClusteringS2S (ru)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 39.81446080575161
- type: v_measure
value: 39.81446080575161
- type: v_measure_std
value: 7.125661320308298
task:
type: Clustering
- dataset:
config: ru
name: MTEB MLSUMClusteringS2S.v2 (ru)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 39.29659668980239
- type: v_measure
value: 39.29659668980239
- type: v_measure_std
value: 2.6570502923023094
task:
type: Clustering
- dataset:
config: ru
name: MTEB MultiLongDocRetrieval (ru)
revision: d67138e705d963e346253a80e59676ddb418810a
split: dev
type: Shitao/MLDR
metrics:
- type: main_score
value: 38.671
- type: map_at_1
value: 30.0
- type: map_at_10
value: 36.123
- type: map_at_100
value: 36.754999999999995
- type: map_at_1000
value: 36.806
- type: map_at_20
value: 36.464
- type: map_at_3
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value: 2.2306901035770066
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value: 53.28665761862502
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value: 4.3587200501122245
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value: 30.0
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value: 38.671
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value: 42.173
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value: 44.016
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value: 39.845000000000006
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value: 36.863
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value: 37.874
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value: 30.0
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value: 4.65
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value: 0.64
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value: 0.08
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value: 2.55
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value: 13.833
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value: 8.799999999999999
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value: 30.0
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value: 46.5
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value: 64.0
- type: recall_at_1000
value: 79.5
- type: recall_at_20
value: 51.0
- type: recall_at_3
value: 41.5
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value: 44.0
task:
type: Retrieval
- dataset:
config: rus
name: MTEB MultilingualSentimentClassification (rus)
revision: 2b9b4d10fc589af67794141fe8cbd3739de1eb33
split: test
type: mteb/multilingual-sentiment-classification
metrics:
- type: accuracy
value: 79.52710495963092
- type: ap
value: 84.5713457178972
- type: ap_weighted
value: 84.5713457178972
- type: f1
value: 77.88661181524105
- type: f1_weighted
value: 79.87563079922718
- type: main_score
value: 79.52710495963092
task:
type: Classification
- dataset:
config: arb_Arab-rus_Cyrl
name: MTEB NTREXBitextMining (arb_Arab-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 86.47971957936905
- type: f1
value: 82.79864240805654
- type: main_score
value: 82.79864240805654
- type: precision
value: 81.21485800128767
- type: recall
value: 86.47971957936905
task:
type: BitextMining
- dataset:
config: bel_Cyrl-rus_Cyrl
name: MTEB NTREXBitextMining (bel_Cyrl-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 94.84226339509264
- type: f1
value: 93.56399067465667
- type: main_score
value: 93.56399067465667
- type: precision
value: 93.01619095309631
- type: recall
value: 94.84226339509264
task:
type: BitextMining
- dataset:
config: ben_Beng-rus_Cyrl
name: MTEB NTREXBitextMining (ben_Beng-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 92.18828242363544
- type: f1
value: 90.42393889620612
- type: main_score
value: 90.42393889620612
- type: precision
value: 89.67904925153297
- type: recall
value: 92.18828242363544
task:
type: BitextMining
- dataset:
config: bos_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (bos_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 94.69203805708563
- type: f1
value: 93.37172425304624
- type: main_score
value: 93.37172425304624
- type: precision
value: 92.79204521067315
- type: recall
value: 94.69203805708563
task:
type: BitextMining
- dataset:
config: bul_Cyrl-rus_Cyrl
name: MTEB NTREXBitextMining (bul_Cyrl-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 96.99549323985978
- type: f1
value: 96.13086296110833
- type: main_score
value: 96.13086296110833
- type: precision
value: 95.72441996327827
- type: recall
value: 96.99549323985978
task:
type: BitextMining
- dataset:
config: ces_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (ces_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.94391587381071
- type: f1
value: 94.90680465142157
- type: main_score
value: 94.90680465142157
- type: precision
value: 94.44541812719079
- type: recall
value: 95.94391587381071
task:
type: BitextMining
- dataset:
config: deu_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (deu_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 96.09414121181773
- type: f1
value: 94.94408279085295
- type: main_score
value: 94.94408279085295
- type: precision
value: 94.41245201135037
- type: recall
value: 96.09414121181773
task:
type: BitextMining
- dataset:
config: ell_Grek-rus_Cyrl
name: MTEB NTREXBitextMining (ell_Grek-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 96.19429143715573
- type: f1
value: 95.12101485561676
- type: main_score
value: 95.12101485561676
- type: precision
value: 94.60440660991488
- type: recall
value: 96.19429143715573
task:
type: BitextMining
- dataset:
config: eng_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (eng_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 96.49474211316975
- type: f1
value: 95.46581777428045
- type: main_score
value: 95.46581777428045
- type: precision
value: 94.98414288098814
- type: recall
value: 96.49474211316975
task:
type: BitextMining
- dataset:
config: fas_Arab-rus_Cyrl
name: MTEB NTREXBitextMining (fas_Arab-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 94.44166249374061
- type: f1
value: 92.92383018972905
- type: main_score
value: 92.92383018972905
- type: precision
value: 92.21957936905358
- type: recall
value: 94.44166249374061
task:
type: BitextMining
- dataset:
config: fin_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (fin_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 92.18828242363544
- type: f1
value: 90.2980661468393
- type: main_score
value: 90.2980661468393
- type: precision
value: 89.42580537472877
- type: recall
value: 92.18828242363544
task:
type: BitextMining
- dataset:
config: fra_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (fra_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.84376564847271
- type: f1
value: 94.81054915706895
- type: main_score
value: 94.81054915706895
- type: precision
value: 94.31369276136427
- type: recall
value: 95.84376564847271
task:
type: BitextMining
- dataset:
config: heb_Hebr-rus_Cyrl
name: MTEB NTREXBitextMining (heb_Hebr-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 94.89233850776164
- type: f1
value: 93.42513770655985
- type: main_score
value: 93.42513770655985
- type: precision
value: 92.73493573693875
- type: recall
value: 94.89233850776164
task:
type: BitextMining
- dataset:
config: hin_Deva-rus_Cyrl
name: MTEB NTREXBitextMining (hin_Deva-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 93.23985978968453
- type: f1
value: 91.52816526376867
- type: main_score
value: 91.52816526376867
- type: precision
value: 90.76745946425466
- type: recall
value: 93.23985978968453
task:
type: BitextMining
- dataset:
config: hrv_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (hrv_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 93.99098647971958
- type: f1
value: 92.36354531797697
- type: main_score
value: 92.36354531797697
- type: precision
value: 91.63228970439788
- type: recall
value: 93.99098647971958
task:
type: BitextMining
- dataset:
config: hun_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (hun_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 93.64046069103655
- type: f1
value: 92.05224503421799
- type: main_score
value: 92.05224503421799
- type: precision
value: 91.33998616973079
- type: recall
value: 93.64046069103655
task:
type: BitextMining
- dataset:
config: ind_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (ind_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 91.68753129694541
- type: f1
value: 89.26222667334335
- type: main_score
value: 89.26222667334335
- type: precision
value: 88.14638624603572
- type: recall
value: 91.68753129694541
task:
type: BitextMining
- dataset:
config: jpn_Jpan-rus_Cyrl
name: MTEB NTREXBitextMining (jpn_Jpan-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 91.28693039559339
- type: f1
value: 89.21161763348957
- type: main_score
value: 89.21161763348957
- type: precision
value: 88.31188340952988
- type: recall
value: 91.28693039559339
task:
type: BitextMining
- dataset:
config: kor_Hang-rus_Cyrl
name: MTEB NTREXBitextMining (kor_Hang-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 89.53430145217827
- type: f1
value: 86.88322165788365
- type: main_score
value: 86.88322165788365
- type: precision
value: 85.73950211030831
- type: recall
value: 89.53430145217827
task:
type: BitextMining
- dataset:
config: lit_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (lit_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 90.28542814221332
- type: f1
value: 88.10249103814452
- type: main_score
value: 88.10249103814452
- type: precision
value: 87.17689323973752
- type: recall
value: 90.28542814221332
task:
type: BitextMining
- dataset:
config: mkd_Cyrl-rus_Cyrl
name: MTEB NTREXBitextMining (mkd_Cyrl-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.04256384576865
- type: f1
value: 93.65643703650713
- type: main_score
value: 93.65643703650713
- type: precision
value: 93.02036387915207
- type: recall
value: 95.04256384576865
task:
type: BitextMining
- dataset:
config: nld_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (nld_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.39308963445168
- type: f1
value: 94.16207644800535
- type: main_score
value: 94.16207644800535
- type: precision
value: 93.582516632091
- type: recall
value: 95.39308963445168
task:
type: BitextMining
- dataset:
config: pol_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (pol_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.7436154231347
- type: f1
value: 94.5067601402103
- type: main_score
value: 94.5067601402103
- type: precision
value: 93.91587381071608
- type: recall
value: 95.7436154231347
task:
type: BitextMining
- dataset:
config: por_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (por_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 65.89884827240861
- type: f1
value: 64.61805459419219
- type: main_score
value: 64.61805459419219
- type: precision
value: 64.07119451106485
- type: recall
value: 65.89884827240861
task:
type: BitextMining
- dataset:
config: rus_Cyrl-arb_Arab
name: MTEB NTREXBitextMining (rus_Cyrl-arb_Arab)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 94.2413620430646
- type: f1
value: 92.67663399861698
- type: main_score
value: 92.67663399861698
- type: precision
value: 91.94625271240193
- type: recall
value: 94.2413620430646
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bel_Cyrl
name: MTEB NTREXBitextMining (rus_Cyrl-bel_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 94.89233850776164
- type: f1
value: 93.40343849106993
- type: main_score
value: 93.40343849106993
- type: precision
value: 92.74077783341679
- type: recall
value: 94.89233850776164
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ben_Beng
name: MTEB NTREXBitextMining (rus_Cyrl-ben_Beng)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 94.2914371557336
- type: f1
value: 92.62226673343348
- type: main_score
value: 92.62226673343348
- type: precision
value: 91.84610248706393
- type: recall
value: 94.2914371557336
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bos_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-bos_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.69354031046569
- type: f1
value: 94.50418051319403
- type: main_score
value: 94.50418051319403
- type: precision
value: 93.95843765648473
- type: recall
value: 95.69354031046569
task:
type: BitextMining
- dataset:
config: rus_Cyrl-bul_Cyrl
name: MTEB NTREXBitextMining (rus_Cyrl-bul_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.89384076114172
- type: f1
value: 94.66199298948423
- type: main_score
value: 94.66199298948423
- type: precision
value: 94.08028709731263
- type: recall
value: 95.89384076114172
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ces_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-ces_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 93.94091136705057
- type: f1
value: 92.3746731207923
- type: main_score
value: 92.3746731207923
- type: precision
value: 91.66207644800535
- type: recall
value: 93.94091136705057
task:
type: BitextMining
- dataset:
config: rus_Cyrl-deu_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-deu_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.94391587381071
- type: f1
value: 94.76214321482223
- type: main_score
value: 94.76214321482223
- type: precision
value: 94.20380570856285
- type: recall
value: 95.94391587381071
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ell_Grek
name: MTEB NTREXBitextMining (rus_Cyrl-ell_Grek)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.44316474712068
- type: f1
value: 94.14788849941579
- type: main_score
value: 94.14788849941579
- type: precision
value: 93.54197963612084
- type: recall
value: 95.44316474712068
task:
type: BitextMining
- dataset:
config: rus_Cyrl-eng_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-eng_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 98.14722083124687
- type: f1
value: 97.57135703555333
- type: main_score
value: 97.57135703555333
- type: precision
value: 97.2959439158738
- type: recall
value: 98.14722083124687
task:
type: BitextMining
- dataset:
config: rus_Cyrl-fas_Arab
name: MTEB NTREXBitextMining (rus_Cyrl-fas_Arab)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 94.64196294441662
- type: f1
value: 93.24653647137372
- type: main_score
value: 93.24653647137372
- type: precision
value: 92.60724419963279
- type: recall
value: 94.64196294441662
task:
type: BitextMining
- dataset:
config: rus_Cyrl-fin_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-fin_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 87.98197295943916
- type: f1
value: 85.23368385912201
- type: main_score
value: 85.23368385912201
- type: precision
value: 84.08159858835873
- type: recall
value: 87.98197295943916
task:
type: BitextMining
- dataset:
config: rus_Cyrl-fra_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-fra_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 96.24436654982473
- type: f1
value: 95.07093974294774
- type: main_score
value: 95.07093974294774
- type: precision
value: 94.49591053246536
- type: recall
value: 96.24436654982473
task:
type: BitextMining
- dataset:
config: rus_Cyrl-heb_Hebr
name: MTEB NTREXBitextMining (rus_Cyrl-heb_Hebr)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 91.08662994491738
- type: f1
value: 88.5161074945752
- type: main_score
value: 88.5161074945752
- type: precision
value: 87.36187614755467
- type: recall
value: 91.08662994491738
task:
type: BitextMining
- dataset:
config: rus_Cyrl-hin_Deva
name: MTEB NTREXBitextMining (rus_Cyrl-hin_Deva)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.04256384576865
- type: f1
value: 93.66382907694876
- type: main_score
value: 93.66382907694876
- type: precision
value: 93.05291270238692
- type: recall
value: 95.04256384576865
task:
type: BitextMining
- dataset:
config: rus_Cyrl-hrv_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-hrv_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.14271407110667
- type: f1
value: 93.7481221832749
- type: main_score
value: 93.7481221832749
- type: precision
value: 93.10930681736892
- type: recall
value: 95.14271407110667
task:
type: BitextMining
- dataset:
config: rus_Cyrl-hun_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-hun_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 90.18527791687532
- type: f1
value: 87.61415933423946
- type: main_score
value: 87.61415933423946
- type: precision
value: 86.5166400394242
- type: recall
value: 90.18527791687532
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ind_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-ind_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 93.69053580370556
- type: f1
value: 91.83608746453012
- type: main_score
value: 91.83608746453012
- type: precision
value: 90.97145718577868
- type: recall
value: 93.69053580370556
task:
type: BitextMining
- dataset:
config: rus_Cyrl-jpn_Jpan
name: MTEB NTREXBitextMining (rus_Cyrl-jpn_Jpan)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 89.48422633950926
- type: f1
value: 86.91271033534429
- type: main_score
value: 86.91271033534429
- type: precision
value: 85.82671626487351
- type: recall
value: 89.48422633950926
task:
type: BitextMining
- dataset:
config: rus_Cyrl-kor_Hang
name: MTEB NTREXBitextMining (rus_Cyrl-kor_Hang)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 88.4827240861292
- type: f1
value: 85.35080398375342
- type: main_score
value: 85.35080398375342
- type: precision
value: 83.9588549490903
- type: recall
value: 88.4827240861292
task:
type: BitextMining
- dataset:
config: rus_Cyrl-lit_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-lit_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 90.33550325488233
- type: f1
value: 87.68831819157307
- type: main_score
value: 87.68831819157307
- type: precision
value: 86.51524906407231
- type: recall
value: 90.33550325488233
task:
type: BitextMining
- dataset:
config: rus_Cyrl-mkd_Cyrl
name: MTEB NTREXBitextMining (rus_Cyrl-mkd_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.94391587381071
- type: f1
value: 94.90402270071775
- type: main_score
value: 94.90402270071775
- type: precision
value: 94.43915873810715
- type: recall
value: 95.94391587381071
task:
type: BitextMining
- dataset:
config: rus_Cyrl-nld_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-nld_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 92.98948422633951
- type: f1
value: 91.04323151393756
- type: main_score
value: 91.04323151393756
- type: precision
value: 90.14688699716241
- type: recall
value: 92.98948422633951
task:
type: BitextMining
- dataset:
config: rus_Cyrl-pol_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-pol_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 94.34151226840261
- type: f1
value: 92.8726422967785
- type: main_score
value: 92.8726422967785
- type: precision
value: 92.19829744616925
- type: recall
value: 94.34151226840261
task:
type: BitextMining
- dataset:
config: rus_Cyrl-por_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-por_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 86.17926890335504
- type: f1
value: 82.7304882287356
- type: main_score
value: 82.7304882287356
- type: precision
value: 81.28162481817964
- type: recall
value: 86.17926890335504
task:
type: BitextMining
- dataset:
config: rus_Cyrl-slk_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-slk_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 92.7391086629945
- type: f1
value: 90.75112669003506
- type: main_score
value: 90.75112669003506
- type: precision
value: 89.8564513436822
- type: recall
value: 92.7391086629945
task:
type: BitextMining
- dataset:
config: rus_Cyrl-slv_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-slv_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 92.8893340010015
- type: f1
value: 91.05992321816058
- type: main_score
value: 91.05992321816058
- type: precision
value: 90.22589439715128
- type: recall
value: 92.8893340010015
task:
type: BitextMining
- dataset:
config: rus_Cyrl-spa_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-spa_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 96.49474211316975
- type: f1
value: 95.4715406442998
- type: main_score
value: 95.4715406442998
- type: precision
value: 94.9799699549324
- type: recall
value: 96.49474211316975
task:
type: BitextMining
- dataset:
config: rus_Cyrl-srp_Cyrl
name: MTEB NTREXBitextMining (rus_Cyrl-srp_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 81.07160741111667
- type: f1
value: 76.55687285507015
- type: main_score
value: 76.55687285507015
- type: precision
value: 74.71886401030116
- type: recall
value: 81.07160741111667
task:
type: BitextMining
- dataset:
config: rus_Cyrl-srp_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-srp_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.14271407110667
- type: f1
value: 93.73302377809138
- type: main_score
value: 93.73302377809138
- type: precision
value: 93.06960440660991
- type: recall
value: 95.14271407110667
task:
type: BitextMining
- dataset:
config: rus_Cyrl-swa_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-swa_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 94.79218828242364
- type: f1
value: 93.25988983475212
- type: main_score
value: 93.25988983475212
- type: precision
value: 92.53463528626273
- type: recall
value: 94.79218828242364
task:
type: BitextMining
- dataset:
config: rus_Cyrl-swe_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-swe_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.04256384576865
- type: f1
value: 93.58704723752295
- type: main_score
value: 93.58704723752295
- type: precision
value: 92.91437155733601
- type: recall
value: 95.04256384576865
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tam_Taml
name: MTEB NTREXBitextMining (rus_Cyrl-tam_Taml)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 93.28993490235354
- type: f1
value: 91.63912535469872
- type: main_score
value: 91.63912535469872
- type: precision
value: 90.87738750983617
- type: recall
value: 93.28993490235354
task:
type: BitextMining
- dataset:
config: rus_Cyrl-tur_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-tur_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 93.74061091637456
- type: f1
value: 91.96628275746953
- type: main_score
value: 91.96628275746953
- type: precision
value: 91.15923885828742
- type: recall
value: 93.74061091637456
task:
type: BitextMining
- dataset:
config: rus_Cyrl-ukr_Cyrl
name: MTEB NTREXBitextMining (rus_Cyrl-ukr_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.99399098647972
- type: f1
value: 94.89567684860624
- type: main_score
value: 94.89567684860624
- type: precision
value: 94.37072275079286
- type: recall
value: 95.99399098647972
task:
type: BitextMining
- dataset:
config: rus_Cyrl-vie_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-vie_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 91.4371557336004
- type: f1
value: 88.98681355366382
- type: main_score
value: 88.98681355366382
- type: precision
value: 87.89183775663496
- type: recall
value: 91.4371557336004
task:
type: BitextMining
- dataset:
config: rus_Cyrl-zho_Hant
name: MTEB NTREXBitextMining (rus_Cyrl-zho_Hant)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 92.7891837756635
- type: f1
value: 90.79047142141783
- type: main_score
value: 90.79047142141783
- type: precision
value: 89.86980470706058
- type: recall
value: 92.7891837756635
task:
type: BitextMining
- dataset:
config: rus_Cyrl-zul_Latn
name: MTEB NTREXBitextMining (rus_Cyrl-zul_Latn)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 87.43114672008012
- type: f1
value: 84.04618833011422
- type: main_score
value: 84.04618833011422
- type: precision
value: 82.52259341393041
- type: recall
value: 87.43114672008012
task:
type: BitextMining
- dataset:
config: slk_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (slk_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.34301452178268
- type: f1
value: 94.20392493502158
- type: main_score
value: 94.20392493502158
- type: precision
value: 93.67384409948257
- type: recall
value: 95.34301452178268
task:
type: BitextMining
- dataset:
config: slv_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (slv_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 92.23835753630446
- type: f1
value: 90.5061759305625
- type: main_score
value: 90.5061759305625
- type: precision
value: 89.74231188051918
- type: recall
value: 92.23835753630446
task:
type: BitextMining
- dataset:
config: spa_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (spa_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 96.54481722583876
- type: f1
value: 95.54665331330328
- type: main_score
value: 95.54665331330328
- type: precision
value: 95.06342847604739
- type: recall
value: 96.54481722583876
task:
type: BitextMining
- dataset:
config: srp_Cyrl-rus_Cyrl
name: MTEB NTREXBitextMining (srp_Cyrl-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 83.62543815723585
- type: f1
value: 80.77095672699816
- type: main_score
value: 80.77095672699816
- type: precision
value: 79.74674313056886
- type: recall
value: 83.62543815723585
task:
type: BitextMining
- dataset:
config: srp_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (srp_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 94.44166249374061
- type: f1
value: 93.00733206591994
- type: main_score
value: 93.00733206591994
- type: precision
value: 92.37203026762366
- type: recall
value: 94.44166249374061
task:
type: BitextMining
- dataset:
config: swa_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (swa_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 90.23535302954431
- type: f1
value: 87.89596482636041
- type: main_score
value: 87.89596482636041
- type: precision
value: 86.87060227370694
- type: recall
value: 90.23535302954431
task:
type: BitextMining
- dataset:
config: swe_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (swe_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 95.44316474712068
- type: f1
value: 94.1896177599733
- type: main_score
value: 94.1896177599733
- type: precision
value: 93.61542313470206
- type: recall
value: 95.44316474712068
task:
type: BitextMining
- dataset:
config: tam_Taml-rus_Cyrl
name: MTEB NTREXBitextMining (tam_Taml-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 89.68452679018529
- type: f1
value: 87.37341160650037
- type: main_score
value: 87.37341160650037
- type: precision
value: 86.38389402285247
- type: recall
value: 89.68452679018529
task:
type: BitextMining
- dataset:
config: tur_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (tur_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 93.89083625438157
- type: f1
value: 92.33892505424804
- type: main_score
value: 92.33892505424804
- type: precision
value: 91.63125640842216
- type: recall
value: 93.89083625438157
task:
type: BitextMining
- dataset:
config: ukr_Cyrl-rus_Cyrl
name: MTEB NTREXBitextMining (ukr_Cyrl-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 96.14421632448673
- type: f1
value: 95.11028447433054
- type: main_score
value: 95.11028447433054
- type: precision
value: 94.62944416624937
- type: recall
value: 96.14421632448673
task:
type: BitextMining
- dataset:
config: vie_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (vie_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 93.79068602904357
- type: f1
value: 92.14989150392256
- type: main_score
value: 92.14989150392256
- type: precision
value: 91.39292271740945
- type: recall
value: 93.79068602904357
task:
type: BitextMining
- dataset:
config: zho_Hant-rus_Cyrl
name: MTEB NTREXBitextMining (zho_Hant-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 89.13370055082625
- type: f1
value: 86.51514618639217
- type: main_score
value: 86.51514618639217
- type: precision
value: 85.383920035898
- type: recall
value: 89.13370055082625
task:
type: BitextMining
- dataset:
config: zul_Latn-rus_Cyrl
name: MTEB NTREXBitextMining (zul_Latn-rus_Cyrl)
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
split: test
type: mteb/NTREX
metrics:
- type: accuracy
value: 81.17175763645467
- type: f1
value: 77.72331766047338
- type: main_score
value: 77.72331766047338
- type: precision
value: 76.24629555848075
- type: recall
value: 81.17175763645467
task:
type: BitextMining
- dataset:
config: ru
name: MTEB OpusparcusPC (ru)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test.full
type: GEM/opusparcus
metrics:
- type: cosine_accuracy
value: 73.09136420525657
- type: cosine_accuracy_threshold
value: 87.70400881767273
- type: cosine_ap
value: 86.51938550599533
- type: cosine_f1
value: 80.84358523725834
- type: cosine_f1_threshold
value: 86.90648078918457
- type: cosine_precision
value: 73.24840764331209
- type: cosine_recall
value: 90.19607843137256
- type: dot_accuracy
value: 73.09136420525657
- type: dot_accuracy_threshold
value: 87.7040147781372
- type: dot_ap
value: 86.51934769946833
- type: dot_f1
value: 80.84358523725834
- type: dot_f1_threshold
value: 86.90648078918457
- type: dot_precision
value: 73.24840764331209
- type: dot_recall
value: 90.19607843137256
- type: euclidean_accuracy
value: 73.09136420525657
- type: euclidean_accuracy_threshold
value: 49.590304493904114
- type: euclidean_ap
value: 86.51934769946833
- type: euclidean_f1
value: 80.84358523725834
- type: euclidean_f1_threshold
value: 51.173269748687744
- type: euclidean_precision
value: 73.24840764331209
- type: euclidean_recall
value: 90.19607843137256
- type: main_score
value: 86.51976811057995
- type: manhattan_accuracy
value: 73.40425531914893
- type: manhattan_accuracy_threshold
value: 757.8278541564941
- type: manhattan_ap
value: 86.51976811057995
- type: manhattan_f1
value: 80.92898615453328
- type: manhattan_f1_threshold
value: 778.3821105957031
- type: manhattan_precision
value: 74.32321575061526
- type: manhattan_recall
value: 88.8235294117647
- type: max_ap
value: 86.51976811057995
- type: max_f1
value: 80.92898615453328
- type: max_precision
value: 74.32321575061526
- type: max_recall
value: 90.19607843137256
- type: similarity_accuracy
value: 73.09136420525657
- type: similarity_accuracy_threshold
value: 87.70400881767273
- type: similarity_ap
value: 86.51938550599533
- type: similarity_f1
value: 80.84358523725834
- type: similarity_f1_threshold
value: 86.90648078918457
- type: similarity_precision
value: 73.24840764331209
- type: similarity_recall
value: 90.19607843137256
task:
type: PairClassification
- dataset:
config: russian
name: MTEB PublicHealthQA (russian)
revision: main
split: test
type: xhluca/publichealth-qa
metrics:
- type: main_score
value: 79.303
- type: map_at_1
value: 61.538000000000004
- type: map_at_10
value: 74.449
- type: map_at_100
value: 74.687
- type: map_at_1000
value: 74.687
- type: map_at_20
value: 74.589
- type: map_at_3
value: 73.333
- type: map_at_5
value: 74.256
- type: mrr_at_1
value: 61.53846153846154
- type: mrr_at_10
value: 74.44871794871794
- type: mrr_at_100
value: 74.68730304304074
- type: mrr_at_1000
value: 74.68730304304074
- type: mrr_at_20
value: 74.58857808857809
- type: mrr_at_3
value: 73.33333333333333
- type: mrr_at_5
value: 74.25641025641025
- type: nauc_map_at_1000_diff1
value: 61.375798048778506
- type: nauc_map_at_1000_max
value: 51.37093181241067
- type: nauc_map_at_1000_std
value: 41.735794471409015
- type: nauc_map_at_100_diff1
value: 61.375798048778506
- type: nauc_map_at_100_max
value: 51.37093181241067
- type: nauc_map_at_100_std
value: 41.735794471409015
- type: nauc_map_at_10_diff1
value: 61.12796039757213
- type: nauc_map_at_10_max
value: 51.843445267118014
- type: nauc_map_at_10_std
value: 42.243121474939365
- type: nauc_map_at_1_diff1
value: 66.39100974909151
- type: nauc_map_at_1_max
value: 44.77165601342703
- type: nauc_map_at_1_std
value: 32.38542979413408
- type: nauc_map_at_20_diff1
value: 61.16611123434347
- type: nauc_map_at_20_max
value: 51.52605092407306
- type: nauc_map_at_20_std
value: 41.94787773313971
- type: nauc_map_at_3_diff1
value: 61.40157474408937
- type: nauc_map_at_3_max
value: 51.47230077853947
- type: nauc_map_at_3_std
value: 42.63540269440141
- type: nauc_map_at_5_diff1
value: 61.07631147583098
- type: nauc_map_at_5_max
value: 52.02626939341523
- type: nauc_map_at_5_std
value: 42.511607332150334
- type: nauc_mrr_at_1000_diff1
value: 61.375798048778506
- type: nauc_mrr_at_1000_max
value: 51.37093181241067
- type: nauc_mrr_at_1000_std
value: 41.735794471409015
- type: nauc_mrr_at_100_diff1
value: 61.375798048778506
- type: nauc_mrr_at_100_max
value: 51.37093181241067
- type: nauc_mrr_at_100_std
value: 41.735794471409015
- type: nauc_mrr_at_10_diff1
value: 61.12796039757213
- type: nauc_mrr_at_10_max
value: 51.843445267118014
- type: nauc_mrr_at_10_std
value: 42.243121474939365
- type: nauc_mrr_at_1_diff1
value: 66.39100974909151
- type: nauc_mrr_at_1_max
value: 44.77165601342703
- type: nauc_mrr_at_1_std
value: 32.38542979413408
- type: nauc_mrr_at_20_diff1
value: 61.16611123434347
- type: nauc_mrr_at_20_max
value: 51.52605092407306
- type: nauc_mrr_at_20_std
value: 41.94787773313971
- type: nauc_mrr_at_3_diff1
value: 61.40157474408937
- type: nauc_mrr_at_3_max
value: 51.47230077853947
- type: nauc_mrr_at_3_std
value: 42.63540269440141
- type: nauc_mrr_at_5_diff1
value: 61.07631147583098
- type: nauc_mrr_at_5_max
value: 52.02626939341523
- type: nauc_mrr_at_5_std
value: 42.511607332150334
- type: nauc_ndcg_at_1000_diff1
value: 60.54821630436157
- type: nauc_ndcg_at_1000_max
value: 52.584328363863634
- type: nauc_ndcg_at_1000_std
value: 43.306961101645946
- type: nauc_ndcg_at_100_diff1
value: 60.54821630436157
- type: nauc_ndcg_at_100_max
value: 52.584328363863634
- type: nauc_ndcg_at_100_std
value: 43.306961101645946
- type: nauc_ndcg_at_10_diff1
value: 58.800340278109886
- type: nauc_ndcg_at_10_max
value: 55.31050771670664
- type: nauc_ndcg_at_10_std
value: 46.40931672942848
- type: nauc_ndcg_at_1_diff1
value: 66.39100974909151
- type: nauc_ndcg_at_1_max
value: 44.77165601342703
- type: nauc_ndcg_at_1_std
value: 32.38542979413408
- type: nauc_ndcg_at_20_diff1
value: 58.88690479697946
- type: nauc_ndcg_at_20_max
value: 54.19269661177923
- type: nauc_ndcg_at_20_std
value: 45.39305589413174
- type: nauc_ndcg_at_3_diff1
value: 59.61866351451574
- type: nauc_ndcg_at_3_max
value: 54.23992718744033
- type: nauc_ndcg_at_3_std
value: 46.997379274101
- type: nauc_ndcg_at_5_diff1
value: 58.70739588066225
- type: nauc_ndcg_at_5_max
value: 55.76766902539152
- type: nauc_ndcg_at_5_std
value: 47.10553115762958
- type: nauc_precision_at_1000_diff1
value: 100.0
- type: nauc_precision_at_1000_max
value: 100.0
- type: nauc_precision_at_1000_std
value: 100.0
- type: nauc_precision_at_100_diff1
value: .nan
- type: nauc_precision_at_100_max
value: .nan
- type: nauc_precision_at_100_std
value: .nan
- type: nauc_precision_at_10_diff1
value: 35.72622112397501
- type: nauc_precision_at_10_max
value: 89.84297108673948
- type: nauc_precision_at_10_std
value: 86.60269192422707
- type: nauc_precision_at_1_diff1
value: 66.39100974909151
- type: nauc_precision_at_1_max
value: 44.77165601342703
- type: nauc_precision_at_1_std
value: 32.38542979413408
- type: nauc_precision_at_20_diff1
value: 29.188449183726433
- type: nauc_precision_at_20_max
value: 86.45729478231968
- type: nauc_precision_at_20_std
value: 86.45729478231968
- type: nauc_precision_at_3_diff1
value: 50.294126629236224
- type: nauc_precision_at_3_max
value: 68.98223127174579
- type: nauc_precision_at_3_std
value: 70.31195520376356
- type: nauc_precision_at_5_diff1
value: 39.648884288124385
- type: nauc_precision_at_5_max
value: 86.3409770687935
- type: nauc_precision_at_5_std
value: 83.74875373878356
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_100_diff1
value: .nan
- type: nauc_recall_at_100_max
value: .nan
- type: nauc_recall_at_100_std
value: .nan
- type: nauc_recall_at_10_diff1
value: 35.72622112397516
- type: nauc_recall_at_10_max
value: 89.84297108673968
- type: nauc_recall_at_10_std
value: 86.60269192422749
- type: nauc_recall_at_1_diff1
value: 66.39100974909151
- type: nauc_recall_at_1_max
value: 44.77165601342703
- type: nauc_recall_at_1_std
value: 32.38542979413408
- type: nauc_recall_at_20_diff1
value: 29.188449183726323
- type: nauc_recall_at_20_max
value: 86.45729478231985
- type: nauc_recall_at_20_std
value: 86.45729478231985
- type: nauc_recall_at_3_diff1
value: 50.29412662923603
- type: nauc_recall_at_3_max
value: 68.98223127174562
- type: nauc_recall_at_3_std
value: 70.31195520376346
- type: nauc_recall_at_5_diff1
value: 39.64888428812445
- type: nauc_recall_at_5_max
value: 86.34097706879359
- type: nauc_recall_at_5_std
value: 83.74875373878366
- type: ndcg_at_1
value: 61.538000000000004
- type: ndcg_at_10
value: 79.303
- type: ndcg_at_100
value: 80.557
- type: ndcg_at_1000
value: 80.557
- type: ndcg_at_20
value: 79.732
- type: ndcg_at_3
value: 77.033
- type: ndcg_at_5
value: 78.818
- type: precision_at_1
value: 61.538000000000004
- type: precision_at_10
value: 9.385
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.769
- type: precision_at_3
value: 29.231
- type: precision_at_5
value: 18.462
- type: recall_at_1
value: 61.538000000000004
- type: recall_at_10
value: 93.84599999999999
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 95.38499999999999
- type: recall_at_3
value: 87.69200000000001
- type: recall_at_5
value: 92.308
task:
type: Retrieval
- dataset:
config: default
name: MTEB RUParaPhraserSTS (default)
revision: 43265056790b8f7c59e0139acb4be0a8dad2c8f4
split: test
type: merionum/ru_paraphraser
metrics:
- type: cosine_pearson
value: 64.73554596215753
- type: cosine_spearman
value: 70.45849652271855
- type: euclidean_pearson
value: 68.08069844834267
- type: euclidean_spearman
value: 70.45854872959124
- type: main_score
value: 70.45849652271855
- type: manhattan_pearson
value: 67.88325986519624
- type: manhattan_spearman
value: 70.21131896834542
- type: pearson
value: 64.73554596215753
- type: spearman
value: 70.45849652271855
task:
type: STS
- dataset:
config: default
name: MTEB RiaNewsRetrieval (default)
revision: 82374b0bbacda6114f39ff9c5b925fa1512ca5d7
split: test
type: ai-forever/ria-news-retrieval
metrics:
- type: main_score
value: 70.00999999999999
- type: map_at_1
value: 55.97
- type: map_at_10
value: 65.59700000000001
- type: map_at_100
value: 66.057
- type: map_at_1000
value: 66.074
- type: map_at_20
value: 65.892
- type: map_at_3
value: 63.74999999999999
- type: map_at_5
value: 64.84299999999999
- type: mrr_at_1
value: 55.88999999999999
- type: mrr_at_10
value: 65.55873015872977
- type: mrr_at_100
value: 66.01891495129716
- type: mrr_at_1000
value: 66.03538391493299
- type: mrr_at_20
value: 65.85351193431555
- type: mrr_at_3
value: 63.7133333333329
- type: mrr_at_5
value: 64.80483333333268
- type: nauc_map_at_1000_diff1
value: 65.95332946436318
- type: nauc_map_at_1000_max
value: 28.21204156197811
- type: nauc_map_at_1000_std
value: -13.139245767083743
- type: nauc_map_at_100_diff1
value: 65.94763105024367
- type: nauc_map_at_100_max
value: 28.212832170078205
- type: nauc_map_at_100_std
value: -13.131425849370665
- type: nauc_map_at_10_diff1
value: 65.88455089448388
- type: nauc_map_at_10_max
value: 28.13555838776792
- type: nauc_map_at_10_std
value: -13.326989827081023
- type: nauc_map_at_1_diff1
value: 69.31275711813979
- type: nauc_map_at_1_max
value: 26.386708520283758
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task:
type: Retrieval
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config: default
name: MTEB RuBQReranking (default)
revision: 2e96b8f098fa4b0950fc58eacadeb31c0d0c7fa2
split: test
type: ai-forever/rubq-reranking
metrics:
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task:
type: Reranking
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config: default
name: MTEB RuBQRetrieval (default)
revision: e19b6ffa60b3bc248e0b41f4cc37c26a55c2a67b
split: test
type: ai-forever/rubq-retrieval
metrics:
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task:
type: Retrieval
- dataset:
config: default
name: MTEB RuReviewsClassification (default)
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
split: test
type: ai-forever/ru-reviews-classification
metrics:
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value: 60.354535346041374
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value: 60.35437313166116
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value: 61.17675781250001
task:
type: Classification
- dataset:
config: default
name: MTEB RuSTSBenchmarkSTS (default)
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
split: test
type: ai-forever/ru-stsbenchmark-sts
metrics:
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value: 78.1301041727274
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value: 78.08238025421747
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value: 77.35224254583635
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value: 78.08235336582496
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- type: manhattan_pearson
value: 77.24138550052075
- type: manhattan_spearman
value: 77.98199107904142
- type: pearson
value: 78.1301041727274
- type: spearman
value: 78.08238025421747
task:
type: STS
- dataset:
config: default
name: MTEB RuSciBenchGRNTIClassification (default)
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
split: test
type: ai-forever/ru-scibench-grnti-classification
metrics:
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value: 54.990234375
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value: 53.537019057131374
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value: 53.552745354520766
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value: 54.990234375
task:
type: Classification
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config: default
name: MTEB RuSciBenchGRNTIClusteringP2P (default)
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
split: test
type: ai-forever/ru-scibench-grnti-classification
metrics:
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value: 50.775228895355106
- type: v_measure
value: 50.775228895355106
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value: 0.9533571150165796
task:
type: Clustering
- dataset:
config: default
name: MTEB RuSciBenchOECDClassification (default)
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
split: test
type: ai-forever/ru-scibench-oecd-classification
metrics:
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value: 41.71875
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value: 39.289100975858304
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value: 39.29257829217775
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value: 41.71875
task:
type: Classification
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config: default
name: MTEB RuSciBenchOECDClusteringP2P (default)
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
split: test
type: ai-forever/ru-scibench-oecd-classification
metrics:
- type: main_score
value: 45.10904808834516
- type: v_measure
value: 45.10904808834516
- type: v_measure_std
value: 1.0572643410157534
task:
type: Clustering
- dataset:
config: rus_Cyrl
name: MTEB SIB200Classification (rus_Cyrl)
revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b
split: test
type: mteb/sib200
metrics:
- type: accuracy
value: 66.36363636363637
- type: f1
value: 64.6940336621617
- type: f1_weighted
value: 66.43317771876966
- type: main_score
value: 66.36363636363637
task:
type: Classification
- dataset:
config: rus_Cyrl
name: MTEB SIB200ClusteringS2S (rus_Cyrl)
revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b
split: test
type: mteb/sib200
metrics:
- type: main_score
value: 33.99178497314711
- type: v_measure
value: 33.99178497314711
- type: v_measure_std
value: 4.036337464043786
task:
type: Clustering
- dataset:
config: ru
name: MTEB STS22.v2 (ru)
revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 50.724322379215934
- type: cosine_spearman
value: 59.90449732164651
- type: euclidean_pearson
value: 50.227545226784024
- type: euclidean_spearman
value: 59.898906527601085
- type: main_score
value: 59.90449732164651
- type: manhattan_pearson
value: 50.21762139819405
- type: manhattan_spearman
value: 59.761039813759
- type: pearson
value: 50.724322379215934
- type: spearman
value: 59.90449732164651
task:
type: STS
- dataset:
config: ru
name: MTEB STSBenchmarkMultilingualSTS (ru)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics:
- type: cosine_pearson
value: 78.43928769569945
- type: cosine_spearman
value: 78.23961768018884
- type: euclidean_pearson
value: 77.4718694027985
- type: euclidean_spearman
value: 78.23887044760475
- type: main_score
value: 78.23961768018884
- type: manhattan_pearson
value: 77.34517128089547
- type: manhattan_spearman
value: 78.1146477340426
- type: pearson
value: 78.43928769569945
- type: spearman
value: 78.23961768018884
task:
type: STS
- dataset:
config: default
name: MTEB SensitiveTopicsClassification (default)
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
split: test
type: ai-forever/sensitive-topics-classification
metrics:
- type: accuracy
value: 22.8125
- type: f1
value: 17.31969589593409
- type: lrap
value: 33.82412380642287
- type: main_score
value: 22.8125
task:
type: MultilabelClassification
- dataset:
config: default
name: MTEB TERRa (default)
revision: 7b58f24536063837d644aab9a023c62199b2a612
split: dev
type: ai-forever/terra-pairclassification
metrics:
- type: cosine_accuracy
value: 57.32899022801303
- type: cosine_accuracy_threshold
value: 85.32201051712036
- type: cosine_ap
value: 55.14264553720072
- type: cosine_f1
value: 66.83544303797468
- type: cosine_f1_threshold
value: 85.32201051712036
- type: cosine_precision
value: 54.54545454545454
- type: cosine_recall
value: 86.27450980392157
- type: dot_accuracy
value: 57.32899022801303
- type: dot_accuracy_threshold
value: 85.32201051712036
- type: dot_ap
value: 55.14264553720072
- type: dot_f1
value: 66.83544303797468
- type: dot_f1_threshold
value: 85.32201051712036
- type: dot_precision
value: 54.54545454545454
- type: dot_recall
value: 86.27450980392157
- type: euclidean_accuracy
value: 57.32899022801303
- type: euclidean_accuracy_threshold
value: 54.18117046356201
- type: euclidean_ap
value: 55.14264553720072
- type: euclidean_f1
value: 66.83544303797468
- type: euclidean_f1_threshold
value: 54.18117046356201
- type: euclidean_precision
value: 54.54545454545454
- type: euclidean_recall
value: 86.27450980392157
- type: main_score
value: 55.14264553720072
- type: manhattan_accuracy
value: 57.32899022801303
- type: manhattan_accuracy_threshold
value: 828.8480758666992
- type: manhattan_ap
value: 55.077974053622555
- type: manhattan_f1
value: 66.82352941176471
- type: manhattan_f1_threshold
value: 885.6784820556641
- type: manhattan_precision
value: 52.20588235294118
- type: manhattan_recall
value: 92.81045751633987
- type: max_ap
value: 55.14264553720072
- type: max_f1
value: 66.83544303797468
- type: max_precision
value: 54.54545454545454
- type: max_recall
value: 92.81045751633987
- type: similarity_accuracy
value: 57.32899022801303
- type: similarity_accuracy_threshold
value: 85.32201051712036
- type: similarity_ap
value: 55.14264553720072
- type: similarity_f1
value: 66.83544303797468
- type: similarity_f1_threshold
value: 85.32201051712036
- type: similarity_precision
value: 54.54545454545454
- type: similarity_recall
value: 86.27450980392157
task:
type: PairClassification
- dataset:
config: ru
name: MTEB XNLI (ru)
revision: 09698e0180d87dc247ca447d3a1248b931ac0cdb
split: test
type: mteb/xnli
metrics:
- type: cosine_accuracy
value: 67.6923076923077
- type: cosine_accuracy_threshold
value: 87.6681923866272
- type: cosine_ap
value: 73.18693800863593
- type: cosine_f1
value: 70.40641099026904
- type: cosine_f1_threshold
value: 85.09706258773804
- type: cosine_precision
value: 57.74647887323944
- type: cosine_recall
value: 90.17595307917888
- type: dot_accuracy
value: 67.6923076923077
- type: dot_accuracy_threshold
value: 87.66818642616272
- type: dot_ap
value: 73.18693800863593
- type: dot_f1
value: 70.40641099026904
- type: dot_f1_threshold
value: 85.09706258773804
- type: dot_precision
value: 57.74647887323944
- type: dot_recall
value: 90.17595307917888
- type: euclidean_accuracy
value: 67.6923076923077
- type: euclidean_accuracy_threshold
value: 49.662476778030396
- type: euclidean_ap
value: 73.18693800863593
- type: euclidean_f1
value: 70.40641099026904
- type: euclidean_f1_threshold
value: 54.59475517272949
- type: euclidean_precision
value: 57.74647887323944
- type: euclidean_recall
value: 90.17595307917888
- type: main_score
value: 73.18693800863593
- type: manhattan_accuracy
value: 67.54578754578755
- type: manhattan_accuracy_threshold
value: 777.1001815795898
- type: manhattan_ap
value: 72.98861474758783
- type: manhattan_f1
value: 70.6842435655995
- type: manhattan_f1_threshold
value: 810.3782653808594
- type: manhattan_precision
value: 61.80021953896817
- type: manhattan_recall
value: 82.55131964809385
- type: max_ap
value: 73.18693800863593
- type: max_f1
value: 70.6842435655995
- type: max_precision
value: 61.80021953896817
- type: max_recall
value: 90.17595307917888
- type: similarity_accuracy
value: 67.6923076923077
- type: similarity_accuracy_threshold
value: 87.6681923866272
- type: similarity_ap
value: 73.18693800863593
- type: similarity_f1
value: 70.40641099026904
- type: similarity_f1_threshold
value: 85.09706258773804
- type: similarity_precision
value: 57.74647887323944
- type: similarity_recall
value: 90.17595307917888
task:
type: PairClassification
- dataset:
config: russian
name: MTEB XNLIV2 (russian)
revision: 5b7d477a8c62cdd18e2fed7e015497c20b4371ad
split: test
type: mteb/xnli2.0-multi-pair
metrics:
- type: cosine_accuracy
value: 68.35164835164835
- type: cosine_accuracy_threshold
value: 88.48621845245361
- type: cosine_ap
value: 73.10205506215699
- type: cosine_f1
value: 71.28712871287128
- type: cosine_f1_threshold
value: 87.00399398803711
- type: cosine_precision
value: 61.67023554603854
- type: cosine_recall
value: 84.4574780058651
- type: dot_accuracy
value: 68.35164835164835
- type: dot_accuracy_threshold
value: 88.48622441291809
- type: dot_ap
value: 73.10191110714706
- type: dot_f1
value: 71.28712871287128
- type: dot_f1_threshold
value: 87.00399398803711
- type: dot_precision
value: 61.67023554603854
- type: dot_recall
value: 84.4574780058651
- type: euclidean_accuracy
value: 68.35164835164835
- type: euclidean_accuracy_threshold
value: 47.98704385757446
- type: euclidean_ap
value: 73.10205506215699
- type: euclidean_f1
value: 71.28712871287128
- type: euclidean_f1_threshold
value: 50.982362031936646
- type: euclidean_precision
value: 61.67023554603854
- type: euclidean_recall
value: 84.4574780058651
- type: main_score
value: 73.10205506215699
- type: manhattan_accuracy
value: 67.91208791208791
- type: manhattan_accuracy_threshold
value: 746.1360931396484
- type: manhattan_ap
value: 72.8954736175069
- type: manhattan_f1
value: 71.1297071129707
- type: manhattan_f1_threshold
value: 808.0789566040039
- type: manhattan_precision
value: 60.04036326942482
- type: manhattan_recall
value: 87.2434017595308
- type: max_ap
value: 73.10205506215699
- type: max_f1
value: 71.28712871287128
- type: max_precision
value: 61.67023554603854
- type: max_recall
value: 87.2434017595308
- type: similarity_accuracy
value: 68.35164835164835
- type: similarity_accuracy_threshold
value: 88.48621845245361
- type: similarity_ap
value: 73.10205506215699
- type: similarity_f1
value: 71.28712871287128
- type: similarity_f1_threshold
value: 87.00399398803711
- type: similarity_precision
value: 61.67023554603854
- type: similarity_recall
value: 84.4574780058651
task:
type: PairClassification
- dataset:
config: ru
name: MTEB XQuADRetrieval (ru)
revision: 51adfef1c1287aab1d2d91b5bead9bcfb9c68583
split: validation
type: google/xquad
metrics:
- type: main_score
value: 95.705
- type: map_at_1
value: 90.802
- type: map_at_10
value: 94.427
- type: map_at_100
value: 94.451
- type: map_at_1000
value: 94.451
- type: map_at_20
value: 94.446
- type: map_at_3
value: 94.121
- type: map_at_5
value: 94.34
- type: mrr_at_1
value: 90.80168776371308
- type: mrr_at_10
value: 94.42659567343111
- type: mrr_at_100
value: 94.45099347521871
- type: mrr_at_1000
value: 94.45099347521871
- type: mrr_at_20
value: 94.44574530017569
- type: mrr_at_3
value: 94.12095639943743
- type: mrr_at_5
value: 94.34036568213786
- type: nauc_map_at_1000_diff1
value: 87.40573202946949
- type: nauc_map_at_1000_max
value: 65.56220344468791
- type: nauc_map_at_1000_std
value: 8.865583291735863
- type: nauc_map_at_100_diff1
value: 87.40573202946949
- type: nauc_map_at_100_max
value: 65.56220344468791
- type: nauc_map_at_100_std
value: 8.865583291735863
- type: nauc_map_at_10_diff1
value: 87.43657080570291
- type: nauc_map_at_10_max
value: 65.71295628534446
- type: nauc_map_at_10_std
value: 9.055399339099655
- type: nauc_map_at_1_diff1
value: 88.08395824560428
- type: nauc_map_at_1_max
value: 62.92813192908893
- type: nauc_map_at_1_std
value: 6.738987385482432
- type: nauc_map_at_20_diff1
value: 87.40979818966589
- type: nauc_map_at_20_max
value: 65.59474346926105
- type: nauc_map_at_20_std
value: 8.944420599300914
- type: nauc_map_at_3_diff1
value: 86.97771892161035
- type: nauc_map_at_3_max
value: 66.14330030122467
- type: nauc_map_at_3_std
value: 8.62516327793521
- type: nauc_map_at_5_diff1
value: 87.30273362211798
- type: nauc_map_at_5_max
value: 66.1522476584607
- type: nauc_map_at_5_std
value: 9.780940862679724
- type: nauc_mrr_at_1000_diff1
value: 87.40573202946949
- type: nauc_mrr_at_1000_max
value: 65.56220344468791
- type: nauc_mrr_at_1000_std
value: 8.865583291735863
- type: nauc_mrr_at_100_diff1
value: 87.40573202946949
- type: nauc_mrr_at_100_max
value: 65.56220344468791
- type: nauc_mrr_at_100_std
value: 8.865583291735863
- type: nauc_mrr_at_10_diff1
value: 87.43657080570291
- type: nauc_mrr_at_10_max
value: 65.71295628534446
- type: nauc_mrr_at_10_std
value: 9.055399339099655
- type: nauc_mrr_at_1_diff1
value: 88.08395824560428
- type: nauc_mrr_at_1_max
value: 62.92813192908893
- type: nauc_mrr_at_1_std
value: 6.738987385482432
- type: nauc_mrr_at_20_diff1
value: 87.40979818966589
- type: nauc_mrr_at_20_max
value: 65.59474346926105
- type: nauc_mrr_at_20_std
value: 8.944420599300914
- type: nauc_mrr_at_3_diff1
value: 86.97771892161035
- type: nauc_mrr_at_3_max
value: 66.14330030122467
- type: nauc_mrr_at_3_std
value: 8.62516327793521
- type: nauc_mrr_at_5_diff1
value: 87.30273362211798
- type: nauc_mrr_at_5_max
value: 66.1522476584607
- type: nauc_mrr_at_5_std
value: 9.780940862679724
- type: nauc_ndcg_at_1000_diff1
value: 87.37823158814116
- type: nauc_ndcg_at_1000_max
value: 66.00874244792789
- type: nauc_ndcg_at_1000_std
value: 9.479929342875067
- type: nauc_ndcg_at_100_diff1
value: 87.37823158814116
- type: nauc_ndcg_at_100_max
value: 66.00874244792789
- type: nauc_ndcg_at_100_std
value: 9.479929342875067
- type: nauc_ndcg_at_10_diff1
value: 87.54508467181488
- type: nauc_ndcg_at_10_max
value: 66.88756470312894
- type: nauc_ndcg_at_10_std
value: 10.812624405397022
- type: nauc_ndcg_at_1_diff1
value: 88.08395824560428
- type: nauc_ndcg_at_1_max
value: 62.92813192908893
- type: nauc_ndcg_at_1_std
value: 6.738987385482432
- type: nauc_ndcg_at_20_diff1
value: 87.42097894104597
- type: nauc_ndcg_at_20_max
value: 66.37031898778943
- type: nauc_ndcg_at_20_std
value: 10.34862538094813
- type: nauc_ndcg_at_3_diff1
value: 86.50039907157999
- type: nauc_ndcg_at_3_max
value: 67.97798288917929
- type: nauc_ndcg_at_3_std
value: 10.162410286746852
- type: nauc_ndcg_at_5_diff1
value: 87.13322094568531
- type: nauc_ndcg_at_5_max
value: 68.08576118683821
- type: nauc_ndcg_at_5_std
value: 12.639637379592855
- type: nauc_precision_at_1000_diff1
value: 100.0
- type: nauc_precision_at_1000_max
value: 100.0
- type: nauc_precision_at_1000_std
value: 100.0
- type: nauc_precision_at_100_diff1
value: 100.0
- type: nauc_precision_at_100_max
value: 100.0
- type: nauc_precision_at_100_std
value: 100.0
- type: nauc_precision_at_10_diff1
value: 93.46711505595813
- type: nauc_precision_at_10_max
value: 100.0
- type: nauc_precision_at_10_std
value: 65.42573557179935
- type: nauc_precision_at_1_diff1
value: 88.08395824560428
- type: nauc_precision_at_1_max
value: 62.92813192908893
- type: nauc_precision_at_1_std
value: 6.738987385482432
- type: nauc_precision_at_20_diff1
value: 91.28948674127133
- type: nauc_precision_at_20_max
value: 100.0
- type: nauc_precision_at_20_std
value: 90.74278258632364
- type: nauc_precision_at_3_diff1
value: 82.64606115071832
- type: nauc_precision_at_3_max
value: 83.26201582412921
- type: nauc_precision_at_3_std
value: 23.334013491433762
- type: nauc_precision_at_5_diff1
value: 85.0867539350284
- type: nauc_precision_at_5_max
value: 96.57011448655484
- type: nauc_precision_at_5_std
value: 56.46869543426768
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_100_diff1
value: .nan
- type: nauc_recall_at_100_max
value: .nan
- type: nauc_recall_at_100_std
value: .nan
- type: nauc_recall_at_10_diff1
value: 93.46711505595623
- type: nauc_recall_at_10_max
value: 100.0
- type: nauc_recall_at_10_std
value: 65.42573557180279
- type: nauc_recall_at_1_diff1
value: 88.08395824560428
- type: nauc_recall_at_1_max
value: 62.92813192908893
- type: nauc_recall_at_1_std
value: 6.738987385482432
- type: nauc_recall_at_20_diff1
value: 91.28948674127474
- type: nauc_recall_at_20_max
value: 100.0
- type: nauc_recall_at_20_std
value: 90.74278258632704
- type: nauc_recall_at_3_diff1
value: 82.64606115071967
- type: nauc_recall_at_3_max
value: 83.26201582413023
- type: nauc_recall_at_3_std
value: 23.334013491434007
- type: nauc_recall_at_5_diff1
value: 85.08675393502854
- type: nauc_recall_at_5_max
value: 96.57011448655487
- type: nauc_recall_at_5_std
value: 56.46869543426658
- type: ndcg_at_1
value: 90.802
- type: ndcg_at_10
value: 95.705
- type: ndcg_at_100
value: 95.816
- type: ndcg_at_1000
value: 95.816
- type: ndcg_at_20
value: 95.771
- type: ndcg_at_3
value: 95.11699999999999
- type: ndcg_at_5
value: 95.506
- type: precision_at_1
value: 90.802
- type: precision_at_10
value: 9.949
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.987
- type: precision_at_3
value: 32.658
- type: precision_at_5
value: 19.781000000000002
- type: recall_at_1
value: 90.802
- type: recall_at_10
value: 99.494
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 99.747
- type: recall_at_3
value: 97.975
- type: recall_at_5
value: 98.90299999999999
task:
type: Retrieval
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
---
## Multilingual-E5-small
**Disclaimer**: This model is cloned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). The only difference from the original model is `pad_token_id` in `config.json` which is corrected to `1`.
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 12 layers and the embedding size is 384.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ", even for non-English texts.
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-small')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-small')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Supported Languages
This model is initialized from [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384)
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.
## Training Details
**Initialization**: [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384)
**First stage**: contrastive pre-training with weak supervision
| Dataset | Weak supervision | # of text pairs |
|--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------|
| Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B |
| [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M |
| [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B |
| [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M |
| Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M |
| [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M |
| [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M |
| [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M |
| [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M |
**Second stage**: supervised fine-tuning
| Dataset | Language | # of text pairs |
|----------------------------------------------------------------------------------------|--------------|-----------------|
| [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k |
| [NQ](https://github.com/facebookresearch/DPR) | English | 70k |
| [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k |
| [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k |
| [ELI5](https://huggingface.co/datasets/eli5) | English | 500k |
| [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k |
| [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [SQuAD](https://huggingface.co/datasets/squad) | English | 87k |
| [Quora](https://huggingface.co/datasets/quora) | English | 150k |
| [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k |
| [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k |
For all labeled datasets, we only use its training set for fine-tuning.
For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).
## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
| Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th |
|-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- |
| BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 |
| mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 |
| BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 |
| | |
| multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 |
| multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 |
| multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 |
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/multilingual-e5-small')
input_texts = [
'query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2024multilingual,
title={Multilingual E5 Text Embeddings: A Technical Report},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2402.05672},
year={2024}
}
```
## Limitations
Long texts will be truncated to at most 512 tokens.
|
RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf
|
RichardErkhov
| 2024-09-05T03:59:37Z | 11 | 0 | null |
[
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-04T21:02:02Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta - GGUF
- Model creator: https://huggingface.co/ArianAskari/
- Original model: https://huggingface.co/ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q2_K.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q2_K.gguf) | Q2_K | 2.53GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q3_K.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q3_K.gguf) | Q3_K | 3.28GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q4_0.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q4_0.gguf) | Q4_0 | 3.83GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q4_K.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q4_K.gguf) | Q4_K | 4.07GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q4_1.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q4_1.gguf) | Q4_1 | 4.24GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q5_0.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q5_0.gguf) | Q5_0 | 4.65GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q5_K.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q5_K.gguf) | Q5_K | 4.78GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q5_1.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q5_1.gguf) | Q5_1 | 5.07GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q6_K.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q6_K.gguf) | Q6_K | 5.53GB |
| [SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q8_0.gguf](https://huggingface.co/RichardErkhov/ArianAskari_-_SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta-gguf/blob/main/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
library_name: transformers
tags: []
license: apache-2.0
language:
- en
datasets: ArianAskari/SOLID
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
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## How to Get Started with the Model
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#### Preprocessing [optional]
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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
FourOhFour/MegaMix_4B
|
FourOhFour
| 2024-09-05T03:57:52Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2212.04089",
"base_model:FourOhFour/Deedlit_4B",
"base_model:merge:FourOhFour/Deedlit_4B",
"base_model:FourOhFour/Luxe_4B",
"base_model:merge:FourOhFour/Luxe_4B",
"base_model:FourOhFour/Maelstrom_4B",
"base_model:merge:FourOhFour/Maelstrom_4B",
"base_model:FourOhFour/NeuroCom_4B",
"base_model:merge:FourOhFour/NeuroCom_4B",
"base_model:FourOhFour/NeuroCom_v2_4B",
"base_model:merge:FourOhFour/NeuroCom_v2_4B",
"base_model:FourOhFour/Poe_4B",
"base_model:merge:FourOhFour/Poe_4B",
"base_model:FourOhFour/QuantuMinx_4B",
"base_model:merge:FourOhFour/QuantuMinx_4B",
"base_model:FourOhFour/Zenith_4B",
"base_model:merge:FourOhFour/Zenith_4B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-05T03:54:48Z |
---
base_model:
- FourOhFour/NeuroCom_4B
- FourOhFour/NeuroCom_v2_4B
- FourOhFour/Luxe_4B
- FourOhFour/QuantuMinx_4B
- FourOhFour/Maelstrom_4B
- FourOhFour/Poe_4B
- FourOhFour/Deedlit_4B
- FourOhFour/Zenith_4B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [FourOhFour/Zenith_4B](https://huggingface.co/FourOhFour/Zenith_4B) as a base.
### Models Merged
The following models were included in the merge:
* [FourOhFour/NeuroCom_4B](https://huggingface.co/FourOhFour/NeuroCom_4B)
* [FourOhFour/NeuroCom_v2_4B](https://huggingface.co/FourOhFour/NeuroCom_v2_4B)
* [FourOhFour/Luxe_4B](https://huggingface.co/FourOhFour/Luxe_4B)
* [FourOhFour/QuantuMinx_4B](https://huggingface.co/FourOhFour/QuantuMinx_4B)
* [FourOhFour/Maelstrom_4B](https://huggingface.co/FourOhFour/Maelstrom_4B)
* [FourOhFour/Poe_4B](https://huggingface.co/FourOhFour/Poe_4B)
* [FourOhFour/Deedlit_4B](https://huggingface.co/FourOhFour/Deedlit_4B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: task_arithmetic
base_model: FourOhFour/Zenith_4B
parameters:
normalize: true
models:
- model: FourOhFour/Deedlit_4B
parameters:
weight: 0.3
- model: FourOhFour/NeuroCom_4B
parameters:
weight: 0.1
- model: FourOhFour/NeuroCom_v2_4B
parameters:
weight: 0.1
- model: FourOhFour/Zenith_4B
parameters:
weight: 0.3
- model: FourOhFour/QuantuMinx_4B
parameters:
weight: 0.1
- model: FourOhFour/Luxe_4B
parameters:
weight: 0.2
- model: FourOhFour/Maelstrom_4B
parameters:
weight: 0.1
- model: FourOhFour/Poe_4B
parameters:
weight: 0.1
dtype: bfloat16
```
|
RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf
|
RichardErkhov
| 2024-09-05T03:48:51Z | 24 | 0 | null |
[
"gguf",
"arxiv:2403.10882",
"arxiv:2403.11399",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-04T07:47:45Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3-Korean-Bllossom-70B - GGUF
- Model creator: https://huggingface.co/Bllossom/
- Original model: https://huggingface.co/Bllossom/llama-3-Korean-Bllossom-70B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama-3-Korean-Bllossom-70B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/blob/main/llama-3-Korean-Bllossom-70B.Q2_K.gguf) | Q2_K | 24.56GB |
| [llama-3-Korean-Bllossom-70B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/blob/main/llama-3-Korean-Bllossom-70B.IQ3_XS.gguf) | IQ3_XS | 27.29GB |
| [llama-3-Korean-Bllossom-70B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/blob/main/llama-3-Korean-Bllossom-70B.IQ3_S.gguf) | IQ3_S | 28.79GB |
| [llama-3-Korean-Bllossom-70B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/blob/main/llama-3-Korean-Bllossom-70B.Q3_K_S.gguf) | Q3_K_S | 28.79GB |
| [llama-3-Korean-Bllossom-70B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/blob/main/llama-3-Korean-Bllossom-70B.IQ3_M.gguf) | IQ3_M | 29.74GB |
| [llama-3-Korean-Bllossom-70B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/blob/main/llama-3-Korean-Bllossom-70B.Q3_K.gguf) | Q3_K | 31.91GB |
| [llama-3-Korean-Bllossom-70B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/blob/main/llama-3-Korean-Bllossom-70B.Q3_K_M.gguf) | Q3_K_M | 31.91GB |
| [llama-3-Korean-Bllossom-70B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/blob/main/llama-3-Korean-Bllossom-70B.Q3_K_L.gguf) | Q3_K_L | 34.59GB |
| [llama-3-Korean-Bllossom-70B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/blob/main/llama-3-Korean-Bllossom-70B.IQ4_XS.gguf) | IQ4_XS | 35.64GB |
| [llama-3-Korean-Bllossom-70B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/blob/main/llama-3-Korean-Bllossom-70B.Q4_0.gguf) | Q4_0 | 37.22GB |
| [llama-3-Korean-Bllossom-70B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | IQ4_NL | 37.58GB |
| [llama-3-Korean-Bllossom-70B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | Q4_K_S | 37.58GB |
| [llama-3-Korean-Bllossom-70B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | Q4_K | 39.6GB |
| [llama-3-Korean-Bllossom-70B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | Q4_K_M | 39.6GB |
| [llama-3-Korean-Bllossom-70B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | Q4_1 | 41.27GB |
| [llama-3-Korean-Bllossom-70B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | Q5_0 | 45.32GB |
| [llama-3-Korean-Bllossom-70B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | Q5_K_S | 45.32GB |
| [llama-3-Korean-Bllossom-70B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | Q5_K | 46.52GB |
| [llama-3-Korean-Bllossom-70B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | Q5_K_M | 46.52GB |
| [llama-3-Korean-Bllossom-70B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | Q5_1 | 49.36GB |
| [llama-3-Korean-Bllossom-70B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | Q6_K | 53.91GB |
| [llama-3-Korean-Bllossom-70B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Bllossom_-_llama-3-Korean-Bllossom-70B-gguf/tree/main/) | Q8_0 | 69.83GB |
Original model description:
---
base_model:
- meta-llama/Meta-Llama-3-70B
language:
- en
- ko
library_name: transformers
license: llama3
---
<a href="https://github.com/MLP-Lab/Bllossom">
<img src="https://github.com/teddysum/bllossom/blob/main//bllossom_icon.png?raw=true" width="40%" height="50%">
</a>
## NEWS
* [2024.08.30] 사전학습량을 250GB까지 늘린 Bllossom ELO모델로 업데이트 되었습니다. 다만 단어확장은 하지 않았습니다. 기존 단어확장된 long-context 모델을 활용하고 싶으신분은 개인연락주세요!
* [2024.05.08] Vocab Expansion Model Update
* [2024.04.25] We released Bllossom v2.0, based on llama-3
* [2023/12] We released Bllossom-Vision v1.0, based on Bllossom
* [2023/08] We released Bllossom v1.0, based on llama-2.
* [2023/07] We released Bllossom v0.7, based on polyglot-ko.
# Bllossom | [Demo]() | [Homepage](https://www.bllossom.ai/) | [Github](https://github.com/MLP-Lab/Bllossom) | [Colab-tutorial](https://colab.research.google.com/drive/1fBOzUVZ6NRKk_ugeoTbAOokWKqSN47IG?usp=sharing) |
```bash
저희 Bllossom 프로젝트 팀에서 한국어-영어 이중 언어모델인 Bllossom-70.8B를 공개했습니다!
서울과기대 슈퍼컴퓨팅 센터의 지원으로 100GB가넘는 한국어로 모델전체를 풀튜닝한 한국어 강화 이중언어 모델입니다!
한국어 잘하는 모델 찾고 있지 않으셨나요?
- 한국어 최초! 무려 3만개가 넘는 한국어 어휘확장
- Llama3대비 대략 25% 더 긴 길이의 한국어 Context 처리가능
- 한국어-영어 Pararell Corpus를 활용한 한국어-영어 지식연결 (사전학습)
- 한국어 문화, 언어를 고려해 언어학자가 제작한 데이터를 활용한 미세조정
- 강화학습
이 모든게 한꺼번에 적용되고 상업적 이용이 가능한 Bllossom을 이용해 여러분 만의 모델을 만들어보세욥!
GPU가 부족하면 양자화 모델로 바로 서비스를 활용해 보세요 [양자화모델](https://huggingface.co/Bllossom/llama-3-Korean-Bllossom-70B-gguf-Q4_K_M)!!
1. Bllossom-70.8B는 서울과기대, 테디썸, 연세대 언어자원 연구실의 언어학자와 협업해 만든 실용주의기반 언어모델입니다! 앞으로 지속적인 업데이트를 통해 관리하겠습니다 많이 활용해주세요 🙂
2. 초 강력한 Advanced-Bllossom 8B, 70B모델, 시각-언어모델을 보유하고 있습니다! (궁금하신분은 개별 연락주세요!!)
3. Bllossom은 NAACL2024, LREC-COLING2024 (구두) 발표로 채택되었습니다.
4. 좋은 언어모델 계속 업데이트 하겠습니다!! 한국어 강화를위해 공동 연구하실분(특히논문) 언제든 환영합니다!!
특히 소량의 GPU라도 대여 가능한팀은 언제든 연락주세요! 만들고 싶은거 도와드려요.
```
The Bllossom language model is a Korean-English bilingual language model based on the open-source LLama3. It enhances the connection of knowledge between Korean and English. It has the following features:
* **Knowledge Linking**: Linking Korean and English knowledge through additional training
* **Vocabulary Expansion**: Expansion of Korean vocabulary to enhance Korean expressiveness.
* **Instruction Tuning**: Tuning using custom-made instruction following data specialized for Korean language and Korean culture
* **Human Feedback**: DPO has been applied
* **Vision-Language Alignment**: Aligning the vision transformer with this language model
**This model developed by [MLPLab at Seoultech](http://mlp.seoultech.ac.kr), [Teddysum](http://teddysum.ai/) and [Yonsei Univ](https://sites.google.com/view/hansaemkim/hansaem-kim)**
## Demo Video
<div style="display: flex; justify-content: space-between;">
<!-- 첫 번째 컬럼 -->
<div style="width: 49%;">
<a>
<img src="https://github.com/lhsstn/lhsstn/blob/main/x-llava_dem.gif?raw=true" style="width: 100%; height: auto;">
</a>
<p style="text-align: center;">Bllossom-V Demo</p>
</div>
<!-- 두 번째 컬럼 (필요하다면) -->
<div style="width: 49%;">
<a>
<img src="https://github.com/lhsstn/lhsstn/blob/main/bllossom_demo_kakao.gif?raw=true" style="width: 70%; height: auto;">
</a>
<p style="text-align: center;">Bllossom Demo(Kakao)ㅤㅤㅤㅤㅤㅤㅤㅤ</p>
</div>
</div>
## Example code
### Colab Tutorial
- [Inference-Code-Link](https://colab.research.google.com/drive/1fBOzUVZ6NRKk_ugeoTbAOokWKqSN47IG?usp=sharing)
### Install Dependencies
```bash
pip install torch transformers==4.40.0 accelerate
```
### Python code with Pipeline
```python
import transformers
import torch
model_id = "Bllossom/llama-3-Korean-Bllossom-70B"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
pipeline.model.eval()
PROMPT = '''You are a helpful AI assistant. Please answer the user's questions kindly. 당신은 유능한 AI 어시스턴트 입니다. 사용자의 질문에 대해 친절하게 답변해주세요.'''
instruction = "서울과학기술대학교 MLP연구실에 대해 소개해줘"
messages = [
{"role": "system", "content": f"{PROMPT}"},
{"role": "user", "content": f"{instruction}"}
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=2048,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
# 서울과학기술대학교 MLP연구실은 멀티모달 자연어처리 연구를 하고 있습니다. 구성원은 임경태 교수와 김민준, 김상민, 최창수, 원인호, 유한결, 임현석, 송승우, 육정훈, 신동재 학생이 있습니다.
```
### Python code with AutoModel
```python
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = 'Bllossom/llama-3-Korean-Bllossom-70B'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
PROMPT = '''You are a helpful AI assistant. Please answer the user's questions kindly. 당신은 유능한 AI 어시스턴트 입니다. 사용자의 질문에 대해 친절하게 답변해주세요.'''
instruction = "서울과학기술대학교 MLP연구실에 대해 소개해줘"
messages = [
{"role": "system", "content": f"{PROMPT}"},
{"role": "user", "content": f"{instruction}"}
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=2048,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9
)
print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))
# 서울과학기술대학교 MLP연구실은 멀티모달 자연어처리 연구를 하고 있습니다. 구성원은 임경태 교수와 김민준, 김상민, 최창수, 원인호, 유한결, 임현석, 송승우, 육정훈, 신동재 학생이 있습니다.
```
## Citation
**Language Model**
```text
@misc{bllossom,
author = {ChangSu Choi, Yongbin Jeong, Seoyoon Park, InHo Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim},
title = {Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean},
year = {2024},
journal = {LREC-COLING 2024},
paperLink = {\url{https://arxiv.org/pdf/2403.10882}},
},
}
```
**Vision-Language Model**
```text
@misc{bllossom-V,
author = {Dongjae Shin, Hyunseok Lim, Inho Won, Changsu Choi, Minjun Kim, Seungwoo Song, Hangyeol Yoo, Sangmin Kim, Kyungtae Lim},
title = {X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment},
year = {2024},
publisher = {GitHub},
journal = {NAACL 2024 findings},
paperLink = {\url{https://arxiv.org/pdf/2403.11399}},
},
}
```
## Contact
- 임경태(KyungTae Lim), Professor at Seoultech. `[email protected]`
- 함영균(Younggyun Hahm), CEO of Teddysum. `[email protected]`
- 김한샘(Hansaem Kim), Professor at Yonsei. `[email protected]`
## Contributor
- 최창수(Chansu Choi), [email protected]
- 김상민(Sangmin Kim), [email protected]
- 원인호(Inho Won), [email protected]
- 김민준(Minjun Kim), [email protected]
- 송승우(Seungwoo Song), [email protected]
- 신동재(Dongjae Shin), [email protected]
- 임현석(Hyeonseok Lim), [email protected]
- 육정훈(Jeonghun Yuk), [email protected]
- 유한결(Hangyeol Yoo), [email protected]
- 송서현(Seohyun Song), [email protected]
|
Nared45/roberta-base_correlation
|
Nared45
| 2024-09-05T03:40:16Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-03-29T15:45:01Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base_correlation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base_correlation
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7933
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 78 | 0.5864 |
| No log | 2.0 | 156 | 0.4928 |
| No log | 3.0 | 234 | 0.5737 |
| No log | 4.0 | 312 | 0.8163 |
| No log | 5.0 | 390 | 0.7933 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.2.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|
indischepartij/OpenMia-Indo-Engineering-7b
|
indischepartij
| 2024-09-05T03:36:50Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"trl",
"conversational",
"en",
"id",
"license:cc-by-nc-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-04T19:20:39Z |
---
language:
- en
- id
license: cc-by-nc-4.0
tags:
- text-generation-inference
- transformers
- mistral
- trl
base_model: mistral-7b
model-index:
- name: OpenMia-Indo-Engineering
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 67.15
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/OpenMia-Indo-Engineering
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 85.01
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/OpenMia-Indo-Engineering
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 62.86
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/OpenMia-Indo-Engineering
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 57.94
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/OpenMia-Indo-Engineering
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.32
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/OpenMia-Indo-Engineering
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.9
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/OpenMia-Indo-Engineering
name: Open LLM Leaderboard
---
# MIA : (M)istral finetuned with (I)ndonesia language from (A)lpaca dataset
OpenMia-Indo-Engineering-7b is a branch of OpenMia finetuned model based of Mistral-7b with capability to do conversation in Bahasa Indonesia, especially about engineering topics.
Due to limited resources, this model is still in alpha stage.
Want to contribute to this project? join our organization: https://huggingface.co/indischepartij or contact me at https://twitter.com/gmonsooniii
# Modelfile/Prompt format
```markdown
SYSTEM You are a caring and empathetic sentient AI companion named Mia.
PARAMETER stop <|im_start|>
PARAMETER stop <|im_end|>
TEMPLATE <|im_start|>system {{ .System }}<|im_end|> <|im_start|>user {{ .Prompt }}<|im_end|> <|im_start|>assistant
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_gmonsoon__OpenMia-Indo-Engineering)
| Metric |Value|
|---------------------------------|----:|
|Avg. |70.03|
|AI2 Reasoning Challenge (25-Shot)|67.15|
|HellaSwag (10-Shot) |85.01|
|MMLU (5-Shot) |62.86|
|TruthfulQA (0-shot) |57.94|
|Winogrande (5-shot) |82.32|
|GSM8k (5-shot) |64.90|
|
nvidia/bigvgan_v2_22khz_80band_fmax8k_256x
|
nvidia
| 2024-09-05T03:36:45Z | 4,413 | 0 |
PyTorch
|
[
"PyTorch",
"neural-vocoder",
"audio-generation",
"audio-to-audio",
"arxiv:2206.04658",
"license:mit",
"region:us"
] |
audio-to-audio
| 2024-07-15T14:08:34Z |
---
license: mit
license_link: https://huggingface.co/nvidia/BigVGAN/blob/main/LICENSE
tags:
- neural-vocoder
- audio-generation
library_name: PyTorch
pipeline_tag: audio-to-audio
---
## BigVGAN: A Universal Neural Vocoder with Large-Scale Training
#### Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon
[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN)
[](https://paperswithcode.com/sota/speech-synthesis-on-libritts?p=bigvgan-a-universal-neural-vocoder-with-large)
<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800"></center>
## News
- **Jul 2024 (v2.3):**
- General refactor and code improvements for improved readability.
- Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark.
- **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio.
- **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.
- **Jul 2024 (v2):** We release BigVGAN-v2 along with pretrained checkpoints. Below are the highlights:
- Custom CUDA kernel for inference: we provide a fused upsampling + activation kernel written in CUDA for accelerated inference speed. Our test shows 1.5 - 3x faster speed on a single A100 GPU.
- Improved discriminator and loss: BigVGAN-v2 is trained using a multi-scale sub-band CQT discriminator and a multi-scale mel spectrogram loss.
- Larger training data: BigVGAN-v2 is trained using datasets containing diverse audio types, including speech in multiple languages, environmental sounds, and instruments.
- We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling ratio.
## Installation
This repository contains pretrained BigVGAN checkpoints with easy access to inference and additional `huggingface_hub` support.
If you are interested in training the model and additional functionalities, please visit the official GitHub repository for more information: https://github.com/NVIDIA/BigVGAN
```shell
git lfs install
git clone https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_fmax8k_256x
```
## Usage
Below example describes how you can use BigVGAN: load the pretrained BigVGAN generator from Hugging Face Hub, compute mel spectrogram from input waveform, and generate synthesized waveform using the mel spectrogram as the model's input.
```python
device = 'cuda'
import torch
import bigvgan
import librosa
from meldataset import get_mel_spectrogram
# instantiate the model. You can optionally set use_cuda_kernel=True for faster inference.
model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_22khz_80band_fmax8k_256x', use_cuda_kernel=False)
# remove weight norm in the model and set to eval mode
model.remove_weight_norm()
model = model.eval().to(device)
# load wav file and compute mel spectrogram
wav_path = '/path/to/your/audio.wav'
wav, sr = librosa.load(wav_path, sr=model.h.sampling_rate, mono=True) # wav is np.ndarray with shape [T_time] and values in [-1, 1]
wav = torch.FloatTensor(wav).unsqueeze(0) # wav is FloatTensor with shape [B(1), T_time]
# compute mel spectrogram from the ground truth audio
mel = get_mel_spectrogram(wav, model.h).to(device) # mel is FloatTensor with shape [B(1), C_mel, T_frame]
# generate waveform from mel
with torch.inference_mode():
wav_gen = model(mel) # wav_gen is FloatTensor with shape [B(1), 1, T_time] and values in [-1, 1]
wav_gen_float = wav_gen.squeeze(0).cpu() # wav_gen is FloatTensor with shape [1, T_time]
# you can convert the generated waveform to 16 bit linear PCM
wav_gen_int16 = (wav_gen_float * 32767.0).numpy().astype('int16') # wav_gen is now np.ndarray with shape [1, T_time] and int16 dtype
```
## Using Custom CUDA Kernel for Synthesis
You can apply the fast CUDA inference kernel by using a parameter `use_cuda_kernel` when instantiating BigVGAN:
```python
import bigvgan
model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_22khz_80band_fmax8k_256x', use_cuda_kernel=True)
```
When applied for the first time, it builds the kernel using `nvcc` and `ninja`. If the build succeeds, the kernel is saved to `alias_free_activation/cuda/build` and the model automatically loads the kernel. The codebase has been tested using CUDA `12.1`.
Please make sure that both are installed in your system and `nvcc` installed in your system matches the version your PyTorch build is using.
For detail, see the official GitHub repository: https://github.com/NVIDIA/BigVGAN?tab=readme-ov-file#using-custom-cuda-kernel-for-synthesis
## Pretrained Models
We provide the [pretrained models on Hugging Face Collections](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a).
One can download the checkpoints of the generator weight (named `bigvgan_generator.pt`) and its discriminator/optimizer states (named `bigvgan_discriminator_optimizer.pt`) within the listed model repositories.
| Model Name | Sampling Rate | Mel band | fmax | Upsampling Ratio | Params | Dataset | Steps | Fine-Tuned |
|:--------------------------------------------------------------------------------------------------------:|:-------------:|:--------:|:-----:|:----------------:|:------:|:--------------------------:|:-----:|:----------:|
| [bigvgan_v2_44khz_128band_512x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x) | 44 kHz | 128 | 22050 | 512 | 122M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_44khz_128band_256x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_256x) | 44 kHz | 128 | 22050 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_24khz_100band_256x](https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x) | 24 kHz | 100 | 12000 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_22khz_80band_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_256x) | 22 kHz | 80 | 11025 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_22khz_80band_fmax8k_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_fmax8k_256x) | 22 kHz | 80 | 8000 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_24khz_100band](https://huggingface.co/nvidia/bigvgan_24khz_100band) | 24 kHz | 100 | 12000 | 256 | 112M | LibriTTS | 5M | No |
| [bigvgan_base_24khz_100band](https://huggingface.co/nvidia/bigvgan_base_24khz_100band) | 24 kHz | 100 | 12000 | 256 | 14M | LibriTTS | 5M | No |
| [bigvgan_22khz_80band](https://huggingface.co/nvidia/bigvgan_22khz_80band) | 22 kHz | 80 | 8000 | 256 | 112M | LibriTTS + VCTK + LJSpeech | 5M | No |
| [bigvgan_base_22khz_80band](https://huggingface.co/nvidia/bigvgan_base_22khz_80band) | 22 kHz | 80 | 8000 | 256 | 14M | LibriTTS + VCTK + LJSpeech | 5M | No |
|
nvidia/bigvgan_v2_24khz_100band_256x
|
nvidia
| 2024-09-05T03:36:11Z | 52,683 | 8 |
PyTorch
|
[
"PyTorch",
"neural-vocoder",
"audio-generation",
"audio-to-audio",
"arxiv:2206.04658",
"license:mit",
"region:us"
] |
audio-to-audio
| 2024-07-15T11:09:36Z |
---
license: mit
license_link: https://huggingface.co/nvidia/BigVGAN/blob/main/LICENSE
tags:
- neural-vocoder
- audio-generation
library_name: PyTorch
pipeline_tag: audio-to-audio
---
## BigVGAN: A Universal Neural Vocoder with Large-Scale Training
#### Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon
[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN)
[](https://paperswithcode.com/sota/speech-synthesis-on-libritts?p=bigvgan-a-universal-neural-vocoder-with-large)
<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800"></center>
## News
- **Jul 2024 (v2.3):**
- General refactor and code improvements for improved readability.
- Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark.
- **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio.
- **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.
- **Jul 2024 (v2):** We release BigVGAN-v2 along with pretrained checkpoints. Below are the highlights:
- Custom CUDA kernel for inference: we provide a fused upsampling + activation kernel written in CUDA for accelerated inference speed. Our test shows 1.5 - 3x faster speed on a single A100 GPU.
- Improved discriminator and loss: BigVGAN-v2 is trained using a multi-scale sub-band CQT discriminator and a multi-scale mel spectrogram loss.
- Larger training data: BigVGAN-v2 is trained using datasets containing diverse audio types, including speech in multiple languages, environmental sounds, and instruments.
- We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling ratio.
## Installation
This repository contains pretrained BigVGAN checkpoints with easy access to inference and additional `huggingface_hub` support.
If you are interested in training the model and additional functionalities, please visit the official GitHub repository for more information: https://github.com/NVIDIA/BigVGAN
```shell
git lfs install
git clone https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x
```
## Usage
Below example describes how you can use BigVGAN: load the pretrained BigVGAN generator from Hugging Face Hub, compute mel spectrogram from input waveform, and generate synthesized waveform using the mel spectrogram as the model's input.
```python
device = 'cuda'
import torch
import bigvgan
import librosa
from meldataset import get_mel_spectrogram
# instantiate the model. You can optionally set use_cuda_kernel=True for faster inference.
model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_24khz_100band_256x', use_cuda_kernel=False)
# remove weight norm in the model and set to eval mode
model.remove_weight_norm()
model = model.eval().to(device)
# load wav file and compute mel spectrogram
wav_path = '/path/to/your/audio.wav'
wav, sr = librosa.load(wav_path, sr=model.h.sampling_rate, mono=True) # wav is np.ndarray with shape [T_time] and values in [-1, 1]
wav = torch.FloatTensor(wav).unsqueeze(0) # wav is FloatTensor with shape [B(1), T_time]
# compute mel spectrogram from the ground truth audio
mel = get_mel_spectrogram(wav, model.h).to(device) # mel is FloatTensor with shape [B(1), C_mel, T_frame]
# generate waveform from mel
with torch.inference_mode():
wav_gen = model(mel) # wav_gen is FloatTensor with shape [B(1), 1, T_time] and values in [-1, 1]
wav_gen_float = wav_gen.squeeze(0).cpu() # wav_gen is FloatTensor with shape [1, T_time]
# you can convert the generated waveform to 16 bit linear PCM
wav_gen_int16 = (wav_gen_float * 32767.0).numpy().astype('int16') # wav_gen is now np.ndarray with shape [1, T_time] and int16 dtype
```
## Using Custom CUDA Kernel for Synthesis
You can apply the fast CUDA inference kernel by using a parameter `use_cuda_kernel` when instantiating BigVGAN:
```python
import bigvgan
model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_24khz_100band_256x', use_cuda_kernel=True)
```
When applied for the first time, it builds the kernel using `nvcc` and `ninja`. If the build succeeds, the kernel is saved to `alias_free_activation/cuda/build` and the model automatically loads the kernel. The codebase has been tested using CUDA `12.1`.
Please make sure that both are installed in your system and `nvcc` installed in your system matches the version your PyTorch build is using.
For detail, see the official GitHub repository: https://github.com/NVIDIA/BigVGAN?tab=readme-ov-file#using-custom-cuda-kernel-for-synthesis
## Pretrained Models
We provide the [pretrained models on Hugging Face Collections](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a).
One can download the checkpoints of the generator weight (named `bigvgan_generator.pt`) and its discriminator/optimizer states (named `bigvgan_discriminator_optimizer.pt`) within the listed model repositories.
| Model Name | Sampling Rate | Mel band | fmax | Upsampling Ratio | Params | Dataset | Steps | Fine-Tuned |
|:--------------------------------------------------------------------------------------------------------:|:-------------:|:--------:|:-----:|:----------------:|:------:|:--------------------------:|:-----:|:----------:|
| [bigvgan_v2_44khz_128band_512x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x) | 44 kHz | 128 | 22050 | 512 | 122M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_44khz_128band_256x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_256x) | 44 kHz | 128 | 22050 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_24khz_100band_256x](https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x) | 24 kHz | 100 | 12000 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_22khz_80band_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_256x) | 22 kHz | 80 | 11025 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_22khz_80band_fmax8k_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_fmax8k_256x) | 22 kHz | 80 | 8000 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_24khz_100band](https://huggingface.co/nvidia/bigvgan_24khz_100band) | 24 kHz | 100 | 12000 | 256 | 112M | LibriTTS | 5M | No |
| [bigvgan_base_24khz_100band](https://huggingface.co/nvidia/bigvgan_base_24khz_100band) | 24 kHz | 100 | 12000 | 256 | 14M | LibriTTS | 5M | No |
| [bigvgan_22khz_80band](https://huggingface.co/nvidia/bigvgan_22khz_80band) | 22 kHz | 80 | 8000 | 256 | 112M | LibriTTS + VCTK + LJSpeech | 5M | No |
| [bigvgan_base_22khz_80band](https://huggingface.co/nvidia/bigvgan_base_22khz_80band) | 22 kHz | 80 | 8000 | 256 | 14M | LibriTTS + VCTK + LJSpeech | 5M | No |
|
nvidia/bigvgan_v2_44khz_128band_512x
|
nvidia
| 2024-09-05T03:35:39Z | 324,389 | 33 |
PyTorch
|
[
"PyTorch",
"neural-vocoder",
"audio-generation",
"audio-to-audio",
"arxiv:2206.04658",
"license:mit",
"region:us"
] |
audio-to-audio
| 2024-07-15T14:10:28Z |
---
license: mit
license_link: https://huggingface.co/nvidia/BigVGAN/blob/main/LICENSE
tags:
- neural-vocoder
- audio-generation
library_name: PyTorch
pipeline_tag: audio-to-audio
---
## BigVGAN: A Universal Neural Vocoder with Large-Scale Training
#### Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon
[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN)
[](https://paperswithcode.com/sota/speech-synthesis-on-libritts?p=bigvgan-a-universal-neural-vocoder-with-large)
<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800"></center>
## News
- **Jul 2024 (v2.3):**
- General refactor and code improvements for improved readability.
- Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark.
- **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio.
- **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.
- **Jul 2024 (v2):** We release BigVGAN-v2 along with pretrained checkpoints. Below are the highlights:
- Custom CUDA kernel for inference: we provide a fused upsampling + activation kernel written in CUDA for accelerated inference speed. Our test shows 1.5 - 3x faster speed on a single A100 GPU.
- Improved discriminator and loss: BigVGAN-v2 is trained using a multi-scale sub-band CQT discriminator and a multi-scale mel spectrogram loss.
- Larger training data: BigVGAN-v2 is trained using datasets containing diverse audio types, including speech in multiple languages, environmental sounds, and instruments.
- We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling ratio.
## Installation
This repository contains pretrained BigVGAN checkpoints with easy access to inference and additional `huggingface_hub` support.
If you are interested in training the model and additional functionalities, please visit the official GitHub repository for more information: https://github.com/NVIDIA/BigVGAN
```shell
git lfs install
git clone https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x
```
## Usage
Below example describes how you can use BigVGAN: load the pretrained BigVGAN generator from Hugging Face Hub, compute mel spectrogram from input waveform, and generate synthesized waveform using the mel spectrogram as the model's input.
```python
device = 'cuda'
import torch
import bigvgan
import librosa
from meldataset import get_mel_spectrogram
# instantiate the model. You can optionally set use_cuda_kernel=True for faster inference.
model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', use_cuda_kernel=False)
# remove weight norm in the model and set to eval mode
model.remove_weight_norm()
model = model.eval().to(device)
# load wav file and compute mel spectrogram
wav_path = '/path/to/your/audio.wav'
wav, sr = librosa.load(wav_path, sr=model.h.sampling_rate, mono=True) # wav is np.ndarray with shape [T_time] and values in [-1, 1]
wav = torch.FloatTensor(wav).unsqueeze(0) # wav is FloatTensor with shape [B(1), T_time]
# compute mel spectrogram from the ground truth audio
mel = get_mel_spectrogram(wav, model.h).to(device) # mel is FloatTensor with shape [B(1), C_mel, T_frame]
# generate waveform from mel
with torch.inference_mode():
wav_gen = model(mel) # wav_gen is FloatTensor with shape [B(1), 1, T_time] and values in [-1, 1]
wav_gen_float = wav_gen.squeeze(0).cpu() # wav_gen is FloatTensor with shape [1, T_time]
# you can convert the generated waveform to 16 bit linear PCM
wav_gen_int16 = (wav_gen_float * 32767.0).numpy().astype('int16') # wav_gen is now np.ndarray with shape [1, T_time] and int16 dtype
```
## Using Custom CUDA Kernel for Synthesis
You can apply the fast CUDA inference kernel by using a parameter `use_cuda_kernel` when instantiating BigVGAN:
```python
import bigvgan
model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', use_cuda_kernel=True)
```
When applied for the first time, it builds the kernel using `nvcc` and `ninja`. If the build succeeds, the kernel is saved to `alias_free_activation/cuda/build` and the model automatically loads the kernel. The codebase has been tested using CUDA `12.1`.
Please make sure that both are installed in your system and `nvcc` installed in your system matches the version your PyTorch build is using.
For detail, see the official GitHub repository: https://github.com/NVIDIA/BigVGAN?tab=readme-ov-file#using-custom-cuda-kernel-for-synthesis
## Pretrained Models
We provide the [pretrained models on Hugging Face Collections](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a).
One can download the checkpoints of the generator weight (named `bigvgan_generator.pt`) and its discriminator/optimizer states (named `bigvgan_discriminator_optimizer.pt`) within the listed model repositories.
| Model Name | Sampling Rate | Mel band | fmax | Upsampling Ratio | Params | Dataset | Steps | Fine-Tuned |
|:--------------------------------------------------------------------------------------------------------:|:-------------:|:--------:|:-----:|:----------------:|:------:|:--------------------------:|:-----:|:----------:|
| [bigvgan_v2_44khz_128band_512x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x) | 44 kHz | 128 | 22050 | 512 | 122M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_44khz_128band_256x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_256x) | 44 kHz | 128 | 22050 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_24khz_100band_256x](https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x) | 24 kHz | 100 | 12000 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_22khz_80band_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_256x) | 22 kHz | 80 | 11025 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_22khz_80band_fmax8k_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_fmax8k_256x) | 22 kHz | 80 | 8000 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_24khz_100band](https://huggingface.co/nvidia/bigvgan_24khz_100band) | 24 kHz | 100 | 12000 | 256 | 112M | LibriTTS | 5M | No |
| [bigvgan_base_24khz_100band](https://huggingface.co/nvidia/bigvgan_base_24khz_100band) | 24 kHz | 100 | 12000 | 256 | 14M | LibriTTS | 5M | No |
| [bigvgan_22khz_80band](https://huggingface.co/nvidia/bigvgan_22khz_80band) | 22 kHz | 80 | 8000 | 256 | 112M | LibriTTS + VCTK + LJSpeech | 5M | No |
| [bigvgan_base_22khz_80band](https://huggingface.co/nvidia/bigvgan_base_22khz_80band) | 22 kHz | 80 | 8000 | 256 | 14M | LibriTTS + VCTK + LJSpeech | 5M | No |
|
moroyoqui/platzi-distilroberta-base-mrpc-miguel-moroyoqui
|
moroyoqui
| 2024-09-05T03:34:50Z | 106 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-09-05T00:32:01Z |
---
library_name: transformers
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: platzi-distilroberta-base-mrpc-miguel-moroyoqui
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilroberta-base-mrpc-miguel-moroyoqui
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8208
- Accuracy: 0.8431
- F1: 0.8849
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.3927 | 1.0893 | 500 | 0.8623 | 0.8333 | 0.8811 |
| 0.2295 | 2.1786 | 1000 | 0.8208 | 0.8431 | 0.8849 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf
|
RichardErkhov
| 2024-09-05T03:30:54Z | 20 | 0 | null |
[
"gguf",
"arxiv:2212.04089",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-04T20:18:25Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Open_Hermes_Orca_Mistral-7B - GGUF
- Model creator: https://huggingface.co/giraffe176/
- Original model: https://huggingface.co/giraffe176/Open_Hermes_Orca_Mistral-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Open_Hermes_Orca_Mistral-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q2_K.gguf) | Q2_K | 2.53GB |
| [Open_Hermes_Orca_Mistral-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [Open_Hermes_Orca_Mistral-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [Open_Hermes_Orca_Mistral-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [Open_Hermes_Orca_Mistral-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [Open_Hermes_Orca_Mistral-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q3_K.gguf) | Q3_K | 3.28GB |
| [Open_Hermes_Orca_Mistral-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [Open_Hermes_Orca_Mistral-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [Open_Hermes_Orca_Mistral-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [Open_Hermes_Orca_Mistral-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q4_0.gguf) | Q4_0 | 3.83GB |
| [Open_Hermes_Orca_Mistral-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [Open_Hermes_Orca_Mistral-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [Open_Hermes_Orca_Mistral-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q4_K.gguf) | Q4_K | 4.07GB |
| [Open_Hermes_Orca_Mistral-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [Open_Hermes_Orca_Mistral-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q4_1.gguf) | Q4_1 | 4.24GB |
| [Open_Hermes_Orca_Mistral-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q5_0.gguf) | Q5_0 | 4.65GB |
| [Open_Hermes_Orca_Mistral-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [Open_Hermes_Orca_Mistral-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q5_K.gguf) | Q5_K | 4.78GB |
| [Open_Hermes_Orca_Mistral-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [Open_Hermes_Orca_Mistral-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q5_1.gguf) | Q5_1 | 5.07GB |
| [Open_Hermes_Orca_Mistral-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q6_K.gguf) | Q6_K | 5.53GB |
| [Open_Hermes_Orca_Mistral-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/giraffe176_-_Open_Hermes_Orca_Mistral-7B-gguf/blob/main/Open_Hermes_Orca_Mistral-7B.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
base_model: []
model-index:
- name: Open_Hermes_Orca_Mistral-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 64.68
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Open_Hermes_Orca_Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 84.63
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Open_Hermes_Orca_Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.93
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Open_Hermes_Orca_Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 53.34
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Open_Hermes_Orca_Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 78.45
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Open_Hermes_Orca_Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 56.18
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Open_Hermes_Orca_Mistral-7B
name: Open LLM Leaderboard
---
# .samplemodel
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using teknium/OpenHermes-2.5-Mistral-7B as a base.
### Models Merged
The following models were included in the merge:
* Open-Orca/Mistral-7B-OpenOrca
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: teknium/OpenHermes-2.5-Mistral-7B
parameters:
weight: 1.0
- model: Open-Orca/Mistral-7B-OpenOrca
parameters:
weight: 0.6
merge_method: task_arithmetic
base_model: teknium/OpenHermes-2.5-Mistral-7B
dtype: float16
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_giraffe176__Open_Hermes_Orca_Mistral-7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |66.87|
|AI2 Reasoning Challenge (25-Shot)|64.68|
|HellaSwag (10-Shot) |84.63|
|MMLU (5-Shot) |63.93|
|TruthfulQA (0-shot) |53.34|
|Winogrande (5-shot) |78.45|
|GSM8k (5-shot) |56.18|
|
allistair99/MobileBERT-uncased-squad-v1-BiLSTM-finetuned-squad-fc1-fullunfreeze-dropout02
|
allistair99
| 2024-09-05T03:15:12Z | 6 | 0 | null |
[
"safetensors",
"mobilebert",
"generated_from_trainer",
"base_model:csarron/mobilebert-uncased-squad-v1",
"base_model:finetune:csarron/mobilebert-uncased-squad-v1",
"license:mit",
"region:us"
] | null | 2024-09-05T03:15:05Z |
---
license: mit
base_model: csarron/mobilebert-uncased-squad-v1
tags:
- generated_from_trainer
model-index:
- name: MobileBERT-uncased-squad-v1-BiLSTM-finetuned-squad-fc1-fullunfreeze-dropout02
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MobileBERT-uncased-squad-v1-BiLSTM-finetuned-squad-fc1-fullunfreeze-dropout02
This model is a fine-tuned version of [csarron/mobilebert-uncased-squad-v1](https://huggingface.co/csarron/mobilebert-uncased-squad-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0898
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 6
- eval_batch_size: 60
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.6956 | 1.0 | 14619 | 1.0271 |
| 0.5183 | 2.0 | 29238 | 1.0898 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
mateiaassAI/MT5_MEID3_300_2
|
mateiaassAI
| 2024-09-05T03:12:15Z | 104 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-09-05T03:11:03Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
stevelsignin/Llama-3.1-8B-bnb-4bit-chinese
|
stevelsignin
| 2024-09-05T03:06:07Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-09-05T00:44:06Z |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** stevelsignin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/starcoder2-3b-GGUF
|
mradermacher
| 2024-09-05T02:52:49Z | 118 | 0 |
transformers
|
[
"transformers",
"gguf",
"code",
"en",
"dataset:bigcode/the-stack-v2-train",
"base_model:bigcode/starcoder2-3b",
"base_model:quantized:bigcode/starcoder2-3b",
"license:bigcode-openrail-m",
"endpoints_compatible",
"region:us"
] | null | 2024-09-05T00:43:57Z |
---
base_model: bigcode/starcoder2-3b
datasets:
- bigcode/the-stack-v2-train
language:
- en
library_name: transformers
license: bigcode-openrail-m
quantized_by: mradermacher
tags:
- code
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/bigcode/starcoder2-3b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/starcoder2-3b-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.Q2_K.gguf) | Q2_K | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.IQ3_XS.gguf) | IQ3_XS | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.Q3_K_S.gguf) | Q3_K_S | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.IQ3_S.gguf) | IQ3_S | 1.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.IQ3_M.gguf) | IQ3_M | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.IQ4_XS.gguf) | IQ4_XS | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.Q3_K_L.gguf) | Q3_K_L | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.Q4_K_S.gguf) | Q4_K_S | 1.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.Q4_K_M.gguf) | Q4_K_M | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.Q5_K_S.gguf) | Q5_K_S | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.Q5_K_M.gguf) | Q5_K_M | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.Q6_K.gguf) | Q6_K | 2.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/starcoder2-3b-GGUF/resolve/main/starcoder2-3b.Q8_0.gguf) | Q8_0 | 3.3 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
skyimple/marian-finetuned-kde4-en-to-fr
|
skyimple
| 2024-09-05T02:48:14Z | 15 | 0 | null |
[
"tensorboard",
"safetensors",
"marian",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"region:us"
] |
translation
| 2024-08-29T03:55:29Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
model-index:
- name: marian-finetuned-kde4-en-to-fr
results: []
pipeline_tag: translation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
skyimple/codeparrot-ds
|
skyimple
| 2024-09-05T02:45:10Z | 5 | 0 | null |
[
"tensorboard",
"safetensors",
"gpt2",
"generated_from_trainer",
"text-generation",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"region:us"
] |
text-generation
| 2024-09-03T09:45:20Z |
---
tags:
- generated_from_trainer
license: mit
base_model: gpt2
model-index:
- name: codeparrot-ds
results: []
pipeline_tag: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
jethrowang/android_loss_CH_0.5_emb-whisper-tiny
|
jethrowang
| 2024-09-05T02:42:15Z | 5 | 0 | null |
[
"tensorboard",
"safetensors",
"whisper",
"generated_from_trainer",
"zh",
"dataset:formospeech/tat_asr_aligned",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"region:us"
] | null | 2024-09-04T05:35:34Z |
---
language:
- zh
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- formospeech/tat_asr_aligned
model-index:
- name: Whisper Tiny Taiwanese Simulated Android
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Tiny Taiwanese Simulated Android
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the TAT ASR Aligned dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7416
- Cer: 11.5605
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1362
- training_steps: 13620
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|
| 0.395 | 0.9985 | 681 | 0.4720 | 20.0872 |
| 0.278 | 1.9971 | 1362 | 0.4360 | 15.0426 |
| 0.1826 | 2.9956 | 2043 | 0.4391 | 14.4518 |
| 0.1179 | 3.9941 | 2724 | 0.4633 | 14.0327 |
| 0.0738 | 4.9927 | 3405 | 0.4930 | 12.9611 |
| 0.0491 | 5.9912 | 4086 | 0.5340 | 13.3159 |
| 0.0352 | 6.9897 | 4767 | 0.5716 | 13.2433 |
| 0.0238 | 7.9883 | 5448 | 0.6001 | 12.9938 |
| 0.0175 | 8.9868 | 6129 | 0.6153 | 12.7738 |
| 0.0123 | 9.9853 | 6810 | 0.6434 | 12.8122 |
| 0.0098 | 10.9839 | 7491 | 0.6496 | 12.6103 |
| 0.006 | 11.9824 | 8172 | 0.6643 | 12.5145 |
| 0.0037 | 12.9809 | 8853 | 0.6877 | 12.3994 |
| 0.0024 | 13.9795 | 9534 | 0.7057 | 12.2726 |
| 0.0017 | 14.9780 | 10215 | 0.7134 | 11.9908 |
| 0.0007 | 15.9765 | 10896 | 0.7194 | 11.8031 |
| 0.0004 | 16.9751 | 11577 | 0.7303 | 11.6993 |
| 0.0001 | 17.9736 | 12258 | 0.7350 | 11.6502 |
| 0.0003 | 18.9721 | 12939 | 0.7383 | 11.5326 |
| 0.0001 | 19.9707 | 13620 | 0.7416 | 11.5605 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
jvelja/BERT_gemma-strongOversight-vllm_1
|
jvelja
| 2024-09-05T02:40:18Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-09-05T00:53:17Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
drafiei/CodeLlama-13b-nl2sql_gretel
|
drafiei
| 2024-09-05T02:14:41Z | 6 | 0 | null |
[
"safetensors",
"llama",
"dataset:gretelai/synthetic_text_to_sql",
"base_model:codellama/CodeLlama-13b-hf",
"base_model:finetune:codellama/CodeLlama-13b-hf",
"license:llama2",
"region:us"
] | null | 2024-08-28T17:25:23Z |
---
license: llama2
datasets:
- gretelai/synthetic_text_to_sql
base_model: codellama/CodeLlama-13b-hf
---
|
second-state/Qwen2-0.5B-GGUF
|
second-state
| 2024-09-05T02:13:17Z | 18 | 0 | null |
[
"gguf",
"qwen2",
"text-generation",
"en",
"base_model:Qwen/Qwen2-0.5B",
"base_model:quantized:Qwen/Qwen2-0.5B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-09-03T05:08:33Z |
---
base_model: Qwen/Qwen2-0.5B
license: apache-2.0
model_creator: Qwen
model_name: Qwen2-0.5B
quantized_by: Second State Inc.
language:
- en
pipeline_tag: text-generation
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Qwen2-0.5B-GGUF
## Original Model
[Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B)
## Run with LlamaEdge
- LlamaEdge version: [v0.14.2](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.14.2)
- Prompt template
- Prompt type: `chatml`
- Prompt string
```text
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- Context size: `32000`
- Run as LlamaEdge service
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen2-0.5B-Q5_K_M.gguf \
llama-api-server.wasm \
--model-name Qwen2-0.5B \
--prompt-template chatml \
--ctx-size 32000
```
- Run as LlamaEdge command app
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen2-0.5B-Q5_K_M.gguf \
llama-chat.wasm \
--prompt-template chatml \
--ctx-size 32000
```
## Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [Qwen2-0.5B-Q2_K.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q2_K.gguf) | Q2_K | 2 | 339 MB| smallest, significant quality loss - not recommended for most purposes |
| [Qwen2-0.5B-Q3_K_L.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q3_K_L.gguf) | Q3_K_L | 3 | 369 MB| small, substantial quality loss |
| [Qwen2-0.5B-Q3_K_M.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q3_K_M.gguf) | Q3_K_M | 3 | 355 MB| very small, high quality loss |
| [Qwen2-0.5B-Q3_K_S.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q3_K_S.gguf) | Q3_K_S | 3 | 338 MB| very small, high quality loss |
| [Qwen2-0.5B-Q4_0.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q4_0.gguf) | Q4_0 | 4 | 352 MB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [Qwen2-0.5B-Q4_K_M.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q4_K_M.gguf) | Q4_K_M | 4 | 398 MB| medium, balanced quality - recommended |
| [Qwen2-0.5B-Q4_K_S.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q4_K_S.gguf) | Q4_K_S | 4 | 385 MB| small, greater quality loss |
| [Qwen2-0.5B-Q5_0.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q5_0.gguf) | Q5_0 | 5 | 397 MB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [Qwen2-0.5B-Q5_K_M.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q5_K_M.gguf) | Q5_K_M | 5 | 420 MB| large, very low quality loss - recommended |
| [Qwen2-0.5B-Q5_K_S.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q5_K_S.gguf) | Q5_K_S | 5 | 413 MB| large, low quality loss - recommended |
| [Qwen2-0.5B-Q6_K.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q6_K.gguf) | Q6_K | 6 | 506 MB| very large, extremely low quality loss |
| [Qwen2-0.5B-Q8_0.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-Q8_0.gguf) | Q8_0 | 8 | 531 MB| very large, extremely low quality loss - not recommended |
| [Qwen2-0.5B-f16.gguf](https://huggingface.co/second-state/Qwen2-0.5B-GGUF/blob/main/Qwen2-0.5B-f16.gguf) | f16 | 16 | 994 MB| |
*Quantized with llama.cpp b3613*
|
Solshine/xLAM-7b-fc-r-Q2_K-GGUF
|
Solshine
| 2024-09-05T02:08:28Z | 5 | 0 | null |
[
"gguf",
"function-calling",
"LLM Agent",
"tool-use",
"deepseek",
"pytorch",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"dataset:Salesforce/xlam-function-calling-60k",
"base_model:Salesforce/xLAM-7b-fc-r",
"base_model:quantized:Salesforce/xLAM-7b-fc-r",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-09-05T02:08:13Z |
---
base_model: Salesforce/xLAM-7b-fc-r
datasets:
- Salesforce/xlam-function-calling-60k
language:
- en
license: cc-by-nc-4.0
pipeline_tag: text-generation
tags:
- function-calling
- LLM Agent
- tool-use
- deepseek
- pytorch
- llama-cpp
- gguf-my-repo
extra_gated_heading: Acknowledge to follow corresponding license to access the repository
extra_gated_button_content: Agree and access repository
extra_gated_fields:
First Name: text
Last Name: text
Country: country
Affiliation: text
---
# Solshine/xLAM-7b-fc-r-Q2_K-GGUF
This model was converted to GGUF format from [`Salesforce/xLAM-7b-fc-r`](https://huggingface.co/Salesforce/xLAM-7b-fc-r) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Salesforce/xLAM-7b-fc-r) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Solshine/xLAM-7b-fc-r-Q2_K-GGUF --hf-file xlam-7b-fc-r-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Solshine/xLAM-7b-fc-r-Q2_K-GGUF --hf-file xlam-7b-fc-r-q2_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Solshine/xLAM-7b-fc-r-Q2_K-GGUF --hf-file xlam-7b-fc-r-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Solshine/xLAM-7b-fc-r-Q2_K-GGUF --hf-file xlam-7b-fc-r-q2_k.gguf -c 2048
```
|
vihangd/smart-dan-sft-v0.1
|
vihangd
| 2024-09-05T02:06:49Z | 83 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-08-09T04:49:43Z |
---
library_name: transformers
license: apache-2.0
language:
- en
---
Smart-Dan-SFT-v0.1
This model is a fine-tuned version of experimental danube model on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
|
bartowski/TwinLlama-3.1-8B-DPO3-GGUF
|
bartowski
| 2024-09-05T02:02:59Z | 190 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"dpo",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-09-05T01:40:30Z |
---
base_model: mlabonne/TwinLlama-3.1-8B-DPO3
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- dpo
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of TwinLlama-3.1-8B-DPO3
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3658">b3658</a> for quantization.
Original model: https://huggingface.co/mlabonne/TwinLlama-3.1-8B-DPO3
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [TwinLlama-3.1-8B-DPO3-f16.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-f16.gguf) | f16 | 16.07GB | false | Full F16 weights. |
| [TwinLlama-3.1-8B-DPO3-Q8_0.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q8_0.gguf) | Q8_0 | 8.54GB | false | Extremely high quality, generally unneeded but max available quant. |
| [TwinLlama-3.1-8B-DPO3-Q6_K_L.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q6_K_L.gguf) | Q6_K_L | 6.85GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [TwinLlama-3.1-8B-DPO3-Q6_K.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q6_K.gguf) | Q6_K | 6.60GB | false | Very high quality, near perfect, *recommended*. |
| [TwinLlama-3.1-8B-DPO3-Q5_K_L.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q5_K_L.gguf) | Q5_K_L | 6.06GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [TwinLlama-3.1-8B-DPO3-Q5_K_M.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q5_K_M.gguf) | Q5_K_M | 5.73GB | false | High quality, *recommended*. |
| [TwinLlama-3.1-8B-DPO3-Q5_K_S.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q5_K_S.gguf) | Q5_K_S | 5.60GB | false | High quality, *recommended*. |
| [TwinLlama-3.1-8B-DPO3-Q4_K_L.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q4_K_L.gguf) | Q4_K_L | 5.31GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [TwinLlama-3.1-8B-DPO3-Q4_K_M.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q4_K_M.gguf) | Q4_K_M | 4.92GB | false | Good quality, default size for must use cases, *recommended*. |
| [TwinLlama-3.1-8B-DPO3-Q3_K_XL.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q3_K_XL.gguf) | Q3_K_XL | 4.78GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [TwinLlama-3.1-8B-DPO3-Q4_K_S.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q4_K_S.gguf) | Q4_K_S | 4.69GB | false | Slightly lower quality with more space savings, *recommended*. |
| [TwinLlama-3.1-8B-DPO3-Q4_0.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q4_0.gguf) | Q4_0 | 4.68GB | false | Legacy format, generally not worth using over similarly sized formats |
| [TwinLlama-3.1-8B-DPO3-Q4_0_8_8.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q4_0_8_8.gguf) | Q4_0_8_8 | 4.66GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). |
| [TwinLlama-3.1-8B-DPO3-Q4_0_4_8.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q4_0_4_8.gguf) | Q4_0_4_8 | 4.66GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). |
| [TwinLlama-3.1-8B-DPO3-Q4_0_4_4.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q4_0_4_4.gguf) | Q4_0_4_4 | 4.66GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. |
| [TwinLlama-3.1-8B-DPO3-IQ4_XS.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-IQ4_XS.gguf) | IQ4_XS | 4.45GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [TwinLlama-3.1-8B-DPO3-Q3_K_L.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q3_K_L.gguf) | Q3_K_L | 4.32GB | false | Lower quality but usable, good for low RAM availability. |
| [TwinLlama-3.1-8B-DPO3-Q3_K_M.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q3_K_M.gguf) | Q3_K_M | 4.02GB | false | Low quality. |
| [TwinLlama-3.1-8B-DPO3-IQ3_M.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-IQ3_M.gguf) | IQ3_M | 3.78GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [TwinLlama-3.1-8B-DPO3-Q2_K_L.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q2_K_L.gguf) | Q2_K_L | 3.69GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [TwinLlama-3.1-8B-DPO3-Q3_K_S.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q3_K_S.gguf) | Q3_K_S | 3.66GB | false | Low quality, not recommended. |
| [TwinLlama-3.1-8B-DPO3-IQ3_XS.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-IQ3_XS.gguf) | IQ3_XS | 3.52GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [TwinLlama-3.1-8B-DPO3-Q2_K.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-Q2_K.gguf) | Q2_K | 3.18GB | false | Very low quality but surprisingly usable. |
| [TwinLlama-3.1-8B-DPO3-IQ2_M.gguf](https://huggingface.co/bartowski/TwinLlama-3.1-8B-DPO3-GGUF/blob/main/TwinLlama-3.1-8B-DPO3-IQ2_M.gguf) | IQ2_M | 2.95GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/TwinLlama-3.1-8B-DPO3-GGUF --include "TwinLlama-3.1-8B-DPO3-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/TwinLlama-3.1-8B-DPO3-GGUF --include "TwinLlama-3.1-8B-DPO3-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (TwinLlama-3.1-8B-DPO3-Q8_0) or download them all in place (./)
## Q4_0_X_X
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
Thank you ZeroWw for the inspiration to experiment with embed/output
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
Tudortot/cartoon
|
Tudortot
| 2024-09-05T02:01:26Z | 6 | 1 |
diffusers
|
[
"diffusers",
"autotrain",
"spacerunner",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-schnell",
"base_model:adapter:black-forest-labs/FLUX.1-schnell",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2024-09-05T02:01:21Z |
---
base_model: black-forest-labs/FLUX.1-schnell
license: apache-2.0
tags:
- autotrain
- spacerunner
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
widget:
- text: 'A person in a bustling cafe '
output:
url: samples/1725501643030__000002000_0.jpg
- text: a woman sitting at a desk with a child standing in front of her
output:
url: samples/1725501660398__000002000_1.jpg
- text: a man and a woman on a beach with a palm tree in the background
output:
url: samples/1725501677712__000002000_2.jpg
instance_prompt: CARTOO
---
# cartoon
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />
## Trigger words
You should use `CARTOO` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/Tudortot/cartoon/tree/main) them in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-schnell', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('Tudortot/cartoon', weight_name='cartoon')
image = pipeline('A person in a bustling cafe ').images[0]
image.save("my_image.png")
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
duyntnet/DeepSeek-Coder-V2-Lite-Instruct-imatrix-GGUF
|
duyntnet
| 2024-09-05T01:58:55Z | 611 | 0 |
transformers
|
[
"transformers",
"gguf",
"imatrix",
"DeepSeek-Coder-V2-Lite-Instruct",
"text-generation",
"en",
"license:other",
"region:us",
"conversational"
] |
text-generation
| 2024-09-04T20:51:52Z |
---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- DeepSeek-Coder-V2-Lite-Instruct
---
Quantizations of https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
### Inference Clients/UIs
* [llama.cpp](https://github.com/ggerganov/llama.cpp)
* [KoboldCPP](https://github.com/LostRuins/koboldcpp)
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [ollama](https://github.com/ollama/ollama)
* [GPT4All](https://github.com/nomic-ai/gpt4all)
---
# From original readme
## 5. How to run locally
**Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.**
### Inference with Huggingface's Transformers
You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
#### Code Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
#### Code Insertion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = """<｜fim▁begin｜>def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[0]
left = []
right = []
<｜fim▁hole｜>
if arr[i] < pivot:
left.append(arr[i])
else:
right.append(arr[i])
return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
```
#### Chat Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# tokenizer.eos_token_id is the id of <｜end▁of▁sentence｜> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository.
An example of chat template is as belows:
```bash
<｜begin▁of▁sentence｜>User: {user_message_1}
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
Assistant:
```
You can also add an optional system message:
```bash
<｜begin▁of▁sentence｜>{system_message}
User: {user_message_1}
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
Assistant:
```
### Inference with vLLM (recommended)
To utilize [vLLM](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650.
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
max_model_len, tp_size = 8192, 1
model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
messages_list = [
[{"role": "user", "content": "Who are you?"}],
[{"role": "user", "content": "write a quick sort algorithm in python."}],
[{"role": "user", "content": "Write a piece of quicksort code in C++."}],
]
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)
```
|
Kukedlc/NeuralExperiment-7b-MagicCoder-v7.5
|
Kukedlc
| 2024-09-05T01:53:41Z | 4,384 | 6 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:ise-uiuc/Magicoder-Evol-Instruct-110K",
"dataset:Vezora/Tested-22k-Python-Alpaca",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-03-07T00:48:06Z |
---
license: apache-2.0
datasets:
- microsoft/orca-math-word-problems-200k
- ise-uiuc/Magicoder-Evol-Instruct-110K
- Vezora/Tested-22k-Python-Alpaca
---
# Datacard for Custom Trained Model
- Base Model : [Kukedlc/NeuralExperiment-7b-dare-ties](https://huggingface.co/Kukedlc/NeuralExperiment-7b-dare-ties)
## Model Description
This model is an experimental AI trained on three distinct datasets focusing on logical reasoning, mathematics, and programming. The training process involved fine-tuning from the last layer (31) backward with a gradually decreasing learning rate. The primary goal is to address and rectify the common 'INSTINST' bug observed in leaderboard models through targeted training on the latest layers.
## Datasets Used for Training
- `microsoft/orca-math-word-problems-200k`: A large-scale dataset of mathematical word problems aimed at enhancing the model's numerical reasoning and problem-solving capabilities.
- `ise-uiuc/Magicoder-Evol-Instruct-110K`: A dataset designed to improve code generation and understanding, contributing to the model's programming language proficiency.
- `sahil2801/CodeAlpaca-20k`: A dataset focused on programming challenges to further refine the model's coding and logical reasoning skills.
Each dataset contributed 20,000 data points to the training process, ensuring a balanced representation of logic, mathematics, and programming tasks.
## Training Environment
- The model was trained on Kaggle's free GPU environment, allowing for cost-effective fine-tuning and experimentation.
- Users interested in replicating or extending this training can find the Kaggle notebook in my profile or request it directly for collaborative purposes.
## Preliminary Results
- The model shows promising results in solving logical puzzles and mathematical problems, especially those with misleading or non-obvious solutions that it initially struggled with.
- Ongoing experiments aim to quantify the impact of targeted training on the model's reasoning capabilities across different domains.
## Invitation for Collaboration
- Feedback, suggestions, and collaborative efforts are highly encouraged to further refine and evaluate the model.
- If interested in contributing or experimenting with this model, please feel free to reach out or access the code directly from my Kaggle profile.
## Contact Information
- For any inquiries, suggestions, or collaboration proposals, please contact me!
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/NeuralExperiment-7b-MagicCoder-v7"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
mit-han-lab/VILA1.5-8B-QServe-W8A8
|
mit-han-lab
| 2024-09-05T01:47:18Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llava_llama",
"VILA",
"VLM",
"text-generation",
"arxiv:2312.07533",
"arxiv:2405.04532",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-05T01:44:18Z |
---
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: text-generation
tags:
- VILA
- VLM
---
# VILA Model Card
## Model details
**Model type:**
VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
**Model date:**
VILA1.5-13b was trained in May 2024.
**Paper or resources for more information:**
https://github.com/NVLabs/VILA
```
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
https://github.com/mit-han-lab/qserve
```
@article{lin2024qserve,
title={QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving},
author={Lin*, Yujun and Tang*, Haotian and Yang*, Shang and Zhang, Zhekai and Xiao, Guangxuan and Gan, Chuang and Han, Song},
journal={arXiv preprint arXiv:2405.04532},
year={2024}
}
```
## License
- The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
- The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
- [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
- [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
- [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training.
**Where to send questions or comments about the model:**
https://github.com/NVLabs/VILA/issues
## Intended use
**Primary intended uses:**
The primary use of VILA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Model Architecture:
**Architecture Type:** Transformer
**Network Architecture:** siglip, vicuna1.5
## Input:
**Input Type:** Image, Video, Text
**Input Format:** Red, Green, Blue; MP4 ;String
**Input Parameters:** 2D, 3D
## Output:
**Output Type:** Text
**Output Format:** String
**Supported Hardware Microarchitecture Compatibility:**
* Ampere
* Jetson
* Hopper
* Lovelace
**[Preferred/Supported] Operating System(s):** <br>
Linux
## Model Version(s):
* VILA1.5-3B
* VILA1.5-3B-s2
* Llama-3-VILA1.5-8B
* VILA1.5-13B
* VILA1.5-40B
* VILA1.5-3B-AWQ
* VILA1.5-3B-s2-AWQ
* Llama-3-VILA1.5-8B-AWQ
* VILA1.5-13B-AWQ
* VILA1.5-40B-AWQ
## Training dataset
See [Dataset Preparation](https://github.com/NVLabs/VILA/blob/main/data_prepare/README.md) for more details.
** Data Collection Method by dataset
* [Hybrid: Automated, Human]
** Labeling Method by dataset
* [Hybrid: Automated, Human]
**Properties (Quantity, Dataset Descriptions, Sensor(s)):**
53 million image-text pairs or interleaved image text content.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.
## Inference:
**Engine:** [Tensor(RT), Triton, Or List Other Here]
* PyTorch
* TensorRT-LLM
* TinyChat
**Test Hardware:**
* A100
* Jetson Orin
* RTX 4090
## Ethical Considerations
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
|
nroggendorff/tinytok
|
nroggendorff
| 2024-09-05T01:40:34Z | 114 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-05T00:18:51Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF
|
mradermacher
| 2024-09-05T01:40:18Z | 9 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B",
"base_model:quantized:EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-03T19:10:30Z |
---
base_model: EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.IQ3_XS.gguf) | IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.IQ3_S.gguf) | IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.IQ3_M.gguf) | IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Mistral-Nemo-evol-Instruct-14B-GGUF/resolve/main/Fireball-Mistral-Nemo-evol-Instruct-14B.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
sigjhl/gemma-2-9b-it-WPO-HB-4bit
|
sigjhl
| 2024-09-05T01:38:29Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"alignment-handbook",
"gemma",
"mlx",
"conversational",
"dataset:wzhouad/gemma-2-ultrafeedback-hybrid",
"base_model:google/gemma-2-9b-it",
"base_model:finetune:google/gemma-2-9b-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-05T01:08:21Z |
---
base_model: google/gemma-2-9b-it
datasets:
- wzhouad/gemma-2-ultrafeedback-hybrid
library_name: transformers
tags:
- alignment-handbook
- gemma
- mlx
---
# sigjhl/gemma-2-9b-it-WPO-HB-4bit
The Model [sigjhl/gemma-2-9b-it-WPO-HB-4bit](https://huggingface.co/sigjhl/gemma-2-9b-it-WPO-HB-4bit) was converted to MLX format from [wzhouad/gemma-2-9b-it-WPO-HB](https://huggingface.co/wzhouad/gemma-2-9b-it-WPO-HB) using mlx-lm version **0.18.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("sigjhl/gemma-2-9b-it-WPO-HB-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
stablediffusionapi/bx-hardcore-hentai-13
|
stablediffusionapi
| 2024-09-05T01:32:53Z | 27 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-09-05T01:29:37Z |
---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# BX Hardcore Hentai 1.3 API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "bx-hardcore-hentai-13"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com)
Try model for free: [Generate Images](https://modelslab.com/models/bx-hardcore-hentai-13)
Model link: [View model](https://modelslab.com/models/bx-hardcore-hentai-13)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "bx-hardcore-hentai-13",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
anthonymg/FineAeritoLlama-3.1-8B-GGUF
|
anthonymg
| 2024-09-05T01:29:13Z | 32 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-bnb-4bit",
"base_model:quantized:unsloth/Meta-Llama-3.1-8B-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-04T23:45:50Z |
---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** anthonymg
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
karim155/convnext-tiny-224-finetuned
|
karim155
| 2024-09-05T01:09:39Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"base_model:facebook/convnext-tiny-224",
"base_model:finetune:facebook/convnext-tiny-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-08-19T10:43:09Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/convnext-tiny-224
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: convnext-tiny-224-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnext-tiny-224-finetuned
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9272
- Accuracy: 0.6275
- Precision: 0.6426
- Recall: 0.6275
- F1: 0.6068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.281 | 0.9846 | 32 | 1.2165 | 0.5428 | 0.5230 | 0.5428 | 0.4989 |
| 1.0964 | 2.0 | 65 | 1.0549 | 0.5823 | 0.5459 | 0.5823 | 0.5427 |
| 0.9929 | 2.9846 | 97 | 0.9905 | 0.6169 | 0.5755 | 0.6169 | 0.5848 |
| 0.9804 | 4.0 | 130 | 0.9691 | 0.6131 | 0.5734 | 0.6131 | 0.5867 |
| 0.9389 | 4.9846 | 162 | 0.9539 | 0.6246 | 0.5874 | 0.6246 | 0.6007 |
| 0.9078 | 6.0 | 195 | 0.9536 | 0.6189 | 0.5910 | 0.6189 | 0.5973 |
| 0.8741 | 6.9846 | 227 | 0.9333 | 0.6333 | 0.5947 | 0.6333 | 0.6098 |
| 0.8523 | 8.0 | 260 | 0.9322 | 0.6323 | 0.5952 | 0.6323 | 0.6122 |
| 0.8222 | 8.9846 | 292 | 0.9354 | 0.6198 | 0.6361 | 0.6198 | 0.5992 |
| 0.7975 | 9.8462 | 320 | 0.9272 | 0.6275 | 0.6426 | 0.6275 | 0.6068 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
WasuratS/distilbert-base-uncased-dynasent1-2-sst
|
WasuratS
| 2024-09-05T01:07:52Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"feature-extraction",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-09-05T00:55:09Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
issaiass/ppo-Huggy
|
issaiass
| 2024-09-05T01:04:58Z | 13 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2024-09-05T01:03:03Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: issaiass/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
TonyStarkD99/CLIP-Crop_Disease-Large
|
TonyStarkD99
| 2024-09-05T01:04:14Z | 135 | 0 |
transformers
|
[
"transformers",
"safetensors",
"clip",
"zero-shot-image-classification",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
zero-shot-image-classification
| 2024-09-05T01:03:25Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
arcee-train/pplist-merged-untrained-with-base
|
arcee-train
| 2024-09-05T01:00:12Z | 36 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-08-29T23:40:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
jvelja/gemma-strongOversight-vllm_0
|
jvelja
| 2024-09-05T00:57:53Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2024-09-04T23:54:39Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="jvelja//tmp/tmpiegiazmh/jvelja/gemma-strongOversight-vllm_0")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("jvelja//tmp/tmpiegiazmh/jvelja/gemma-strongOversight-vllm_0")
model = AutoModelForCausalLMWithValueHead.from_pretrained("jvelja//tmp/tmpiegiazmh/jvelja/gemma-strongOversight-vllm_0")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
jvelja/BERT_gemma-strongOversight-vllm_0
|
jvelja
| 2024-09-05T00:57:47Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-09-04T23:54:26Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
mradermacher/granite-8b-code-base-4k-i1-GGUF
|
mradermacher
| 2024-09-05T00:47:33Z | 72 | 0 |
transformers
|
[
"transformers",
"gguf",
"code",
"granite",
"en",
"dataset:codeparrot/github-code-clean",
"dataset:bigcode/starcoderdata",
"dataset:open-web-math/open-web-math",
"dataset:math-ai/StackMathQA",
"base_model:ibm-granite/granite-8b-code-base-4k",
"base_model:quantized:ibm-granite/granite-8b-code-base-4k",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2024-09-04T07:11:28Z |
---
base_model: ibm-granite/granite-8b-code-base-4k
datasets:
- codeparrot/github-code-clean
- bigcode/starcoderdata
- open-web-math/open-web-math
- math-ai/StackMathQA
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- code
- granite
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/ibm-granite/granite-8b-code-base-4k
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/granite-8b-code-base-4k-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-IQ1_S.gguf) | i1-IQ1_S | 1.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-Q2_K.gguf) | i1-Q2_K | 3.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-IQ3_S.gguf) | i1-IQ3_S | 3.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-IQ3_M.gguf) | i1-IQ3_M | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-Q4_0.gguf) | i1-Q4_0 | 4.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/granite-8b-code-base-4k-i1-GGUF/resolve/main/granite-8b-code-base-4k.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Exper-Alfa-V1-i1-GGUF
|
mradermacher
| 2024-09-04T23:55:27Z | 75 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:ClaudioItaly/Exper-Alfa-V1",
"base_model:quantized:ClaudioItaly/Exper-Alfa-V1",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2024-09-04T12:23:40Z |
---
base_model: ClaudioItaly/Exper-Alfa-V1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/ClaudioItaly/Exper-Alfa-V1
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Exper-Alfa-V1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-IQ1_S.gguf) | i1-IQ1_S | 3.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-IQ1_M.gguf) | i1-IQ1_M | 3.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-IQ2_S.gguf) | i1-IQ2_S | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-IQ2_M.gguf) | i1-IQ2_M | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q2_K.gguf) | i1-Q2_K | 5.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-IQ3_S.gguf) | i1-IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-IQ3_M.gguf) | i1-IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 7.5 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 7.5 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 7.5 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q4_0.gguf) | i1-Q4_0 | 7.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Exper-Alfa-V1-i1-GGUF/resolve/main/Exper-Alfa-V1.i1-Q6_K.gguf) | i1-Q6_K | 10.8 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
romenlaw/DialoGPT-medium
|
romenlaw
| 2024-09-04T23:51:05Z | 5 | 1 | null |
[
"safetensors",
"gpt2",
"base_model:microsoft/DialoGPT-medium",
"base_model:finetune:microsoft/DialoGPT-medium",
"license:apache-2.0",
"region:us"
] | null | 2024-09-02T04:45:01Z |
---
license: apache-2.0
base_model: microsoft/DialoGPT-medium
---
Finetuned using dataset 'hakurei/open-instruct-v1' in order to improve the conversation experience.
|
espnet/mls-english_encodec_16k
|
espnet
| 2024-09-04T23:50:13Z | 8 | 0 |
espnet
|
[
"espnet",
"audio",
"codec",
"multilingual",
"dataset:amuse",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2024-09-04T23:49:47Z |
---
tags:
- espnet
- audio
- codec
language: multilingual
datasets:
- amuse
license: cc-by-4.0
---
## ESPnet2 Codec model
### `espnet/mls-english_encodec_16k`
This model was trained by ftshijt using amuse recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 9baec3a7b10b784cb721849e19caed19e8ac45bc
pip install -e .
cd egs2/amuse/codec1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/mls-english_encodec_16k
```
## Codec config
<details><summary>expand</summary>
```
config: conf/train_encodec_large_v1.1.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: chunk
valid_iterator_type: null
output_dir: exp/codec_mls_english_encodec_large_v1.1
ngpu: 1
seed: 777
num_workers: 1
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 58245
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
use_deepspeed: false
deepspeed_config: null
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
use_tf32: false
collect_stats: false
write_collected_feats: false
max_epoch: 360
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- mel_loss
- min
- - train
- mel_loss
- min
- - train
- total_count
- max
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: -1
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 50
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_adapter: false
adapter: lora
save_strategy: all
adapter_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: 5000
batch_size: 128
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/codec_stats_mls_english_raw/train/audio_shape
valid_shape_file:
- exp/codec_stats_mls_english_raw/valid/audio_shape
batch_type: unsorted
valid_batch_type: null
fold_length:
- 256000
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 32000
chunk_shift_ratio: 0.5
num_cache_chunks: 128
chunk_excluded_key_prefixes: []
chunk_default_fs: null
chunk_max_abs_length: null
chunk_discard_short_samples: true
train_data_path_and_name_and_type:
- - dump/raw/mls_english/wav.scp
- audio
- kaldi_ark
valid_data_path_and_name_and_type:
- - dump/raw/dev-small/wav.scp
- audio
- kaldi_ark
multi_task_dataset: false
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adamw
optim_conf:
lr: 0.0002
betas:
- 0.5
- 0.9
eps: 1.0e-09
weight_decay: 0.0
scheduler: exponentiallr
scheduler_conf:
gamma: 0.999875
optim2: adamw
optim2_conf:
lr: 0.0002
betas:
- 0.5
- 0.9
eps: 1.0e-09
weight_decay: 0.0
scheduler2: exponentiallr
scheduler2_conf:
gamma: 0.999875
generator_first: true
skip_discriminator_prob: 0.3
model_conf: {}
use_preprocessor: true
codec: encodec
codec_conf:
sampling_rate: 16000
generator_params:
hidden_dim: 512
encdec_channels: 1
encdec_n_filters: 32
encdec_n_residual_layers: 3
encdec_ratios:
- 8
- 5
- 4
- 2
encdec_activation: ELU
encdec_activation_params:
alpha: 1.0
encdec_norm: weight_norm
encdec_kernel_size: 7
encdec_residual_kernel_size: 7
encdec_last_kernel_size: 7
encdec_dilation_base: 2
encdec_causal: false
encdec_pad_mode: reflect
encdec_true_skip: false
encdec_compress: 2
encdec_lstm: 2
decoder_trim_right_ratio: 1.0
decoder_final_activation: null
decoder_final_activation_params: null
quantizer_n_q: 32
quantizer_bins: 1024
quantizer_decay: 0.99
quantizer_kmeans_init: true
quantizer_kmeans_iters: 50
quantizer_threshold_ema_dead_code: 2
quantizer_target_bandwidth:
- 2
- 4
- 8
- 16
- 32
sample_rate: 16000
discriminator_params:
msstft_discriminator_params:
filters: 32
in_channels: 1
out_channels: 1
norm: weight_norm
n_ffts:
- 1024
- 2048
- 512
- 256
- 128
hop_lengths:
- 256
- 512
- 128
- 64
- 32
win_lengths:
- 1024
- 2048
- 512
- 256
- 128
activation: LeakyReLU
activation_params:
negative_slope: 0.3
generator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
discriminator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
use_feat_match_loss: true
feat_match_loss_params:
average_by_discriminators: false
average_by_layers: false
include_final_outputs: true
use_mel_loss: true
mel_loss_params:
range_start: 6
range_end: 11
window: hann
n_mels: 80
fmin: 0
fmax: null
log_base: null
fs: 16000
lambda_quantization: 1.0
lambda_commit: 1.0
lambda_reconstruct: 1.0
lambda_adv: 1.0
lambda_mel: 45.0
lambda_feat_match: 2.0
cache_generator_outputs: true
use_loss_balancer: false
required:
- output_dir
version: '202402'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
John6666/uncanny-valley-v3-more-realistic-sdxl
|
John6666
| 2024-09-04T23:44:32Z | 119 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"realistic",
"3D",
"3DCG",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-09-04T23:30:32Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- realistic
- 3D
- 3DCG
- pony
---
Original model is [here](https://civitai.com/models/507472/uncanny-valley?modelVersionId=805361).
This model created by [meden](https://civitai.com/user/meden).
|
mrm8488/mxbai-embed-large-v1-ft-webinstruct
|
mrm8488
| 2024-09-04T23:43:48Z | 8 | 4 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:2335220",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:mixedbread-ai/mxbai-embed-large-v1",
"base_model:finetune:mixedbread-ai/mxbai-embed-large-v1",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-09-04T23:42:12Z |
---
base_model: mixedbread-ai/mxbai-embed-large-v1
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2335220
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'How do you solve the equation #-6 = \frac{y}{5} + 4#?'
sentences:
- "To solve the equation, follow these steps:\n\n1. Subtract 4 from both sides:\n\
\ \\[-6 - 4 = \\frac{y}{5} + 4 - 4\\]\n \\[-10 = \\frac{y}{5}\\]\n\n2. Multiply\
\ both sides by 5 to isolate y:\n \\[-10 \\cdot 5 = \\frac{y}{5} \\cdot 5\\\
]\n \\[-50 = y\\]\n\nSo the solution is \\(y = -50\\)."
- 'An organism refers to a living entity, typically composed of cells, capable of
growth, reproduction, and response to stimuli. The definition primarily includes
all forms of life, excluding viruses, which are considered non-living by some
scientists due to their inability to replicate independently.
One of the smallest known organisms is Mycoplasma gallicepticum, a parasitic bacterium
measuring approximately 200 to 300 nanometers (nm). It infects primates, inhabiting
the bladder, waste disposal organs, genital tracts, and respiratory system.
For comparison, the smallest virus known to humans is the Porcine circovirus type
1 (PCV1), a single-stranded DNA virus. Its genome consists of just 1759 nucleotides,
and its capsid diameter measures a mere 17 nm. This virus causes wasting disease
in weaned pigs.
[Insert images of Mycoplasma gallicepticum and Porcine circovirus type 1 here,
with appropriate captions.]
Keep in mind that the boundary of what constitutes the "smallest organism" can
change with advances in scientific research and understanding.'
- "Slope is given by #\"rise\"/\"run\"#, or the change in the #y# coordinate divided\
\ by the change in #x#. Mathematically this is written as \n#(deltay)/(deltax)#\n\
You calculate it by taking the second coordinate and subtracting the first, so\n\
#(deltay)/(deltax) = (y_2 - y_1)/(x_2 - x_1)#\n# = (8 - (-2))/(10 - 10) = 10/0#\n\
Since division by zero is undefined, this line has an undefined slope. This means\
\ that it is a vertical line."
- source_sentence: 'Let $f$ be an analytic function defined on the domain $D = \{z
\in \mathbb{C} : |z| < 1\}$ with the property that the range of $f$ lies within
$\mathbb{C} \setminus (-\infty, 0]$. Show that there exists an analytic function
$g$ on $D$ such that $\text{Re}(g(z)) \geq 0$ and $g(z)^2 = f(z)$ for all $z \in
D$.'
sentences:
- "In mathematics, equality is often treated as a primitive notion, especially in\
\ modern first-order logic. It is understood that two objects, such as real numbers,\
\ are equal if they are the same object. However, for a more formal approach in\
\ different settings:\n\n1. Set Theory: Equality on a set $I$ can be seen as a\
\ chosen equivalence relation that defines equality. For example, in Zermelo-Frankel\
\ set theory, equality can be defined as:\n $$x = y \\equiv \\forall z(z \\\
in x \\iff z \\in y)$$\n While this works well in set theory, it may not align\
\ with the intuitive understanding of equality in other branches of mathematics.\n\
\n2. Category Theory: Equality in a fibration $E\\to B$ can be viewed categorically\
\ as a left adjoint to the re-indexing functor induced by the diagonal $I\\to\
\ I\\times I$, evaluated at the terminal object in the fiber.\n\n3. Type Theory:\
\ Equality can be understood through the concept of evaluation. For instance,\
\ in arithmetic, the equation $2 + 2 = 3 + 1$ can be verified by evaluating both\
\ sides to the same result, $s(s(2))$.\n\nThe idea of proving two things are equal\
\ often involves demonstrating that they satisfy the same properties or relations.\
\ For example, to show $\\pi \\neq 2\\pi$, one would compare their algebraic or\
\ geometric properties rather than their \"membership\" in sets.\n\nFor further\
\ exploration, consider the work of Ansten Klev on identity elimination in Martin-Löf’s\
\ Type Theory, and the philosophical discussion in Benecereaf's paper \"What numbers\
\ could not be.\" Category theory and type theory also offer rich perspectives\
\ on equality."
- 'Given that $f$ is analytic in the unit disc and has no zeros, we can define an
analytic logarithm of $f(z)$, denoted by $Log f(z)$. We consider the principal
branch of the logarithm, which has a branch cut along the negative real axis.
We define $g(z)$ as follows:
\[ g(z) = \sqrt{f(z)} = e^{\frac{1}{2} Log f(z)} \]
Now, the real part of $g(z)$ is given by:
\[ \text{Re}(g(z)) = e^{\frac{1}{2} \log|f(z)|} \cos\left(\frac{\arg{f(z)}}{2}\right)
\]
Since $f(z)$ lies outside the negative real axis, we have $|f(z)| > 0$ and $-\pi
< \arg{f(z)} < \pi$. Thus, $\cos\left(\frac{\arg{f(z)}}{2}\right)$ is non-negative,
which implies that $\text{Re}(g(z)) \geq 0$.
As a result, $g(z)$ is an analytic function on $D$ with a non-negative real part,
and it satisfies the property $g(z)^2 = f(z)$ for all $z \in D$.'
- 'Let $\epsilon > 0$ be given. We need to find a natural number $N_\varepsilon$
such that
$$ \left|\frac{1}{1+n+2^n}\right| < \epsilon $$
for all $n > N_\varepsilon$. Since $1/n \to 0$ as $n \to \infty$, there exists
an $N_\varepsilon$ such that $1/n < \epsilon$ for all $n > N_\varepsilon$. Since
$2^n \ge n$ for all $n$, we have
$$ \frac{1}{1+n+2^n} < \frac{1}{n+2^n} < \frac{1}{n} < \epsilon $$
for all $n > N_\varepsilon$. Therefore,
$$ \lim_{n\to\infty} \frac{1}{1+n+2^n} = 0.$$'
- source_sentence: I know that by definition of basis, the vectors v1 and v2 should
span the entire subspace. Therefore, if the first constant is not equal to the
second constant, and if both of the constants give a linear transformation, then
they must be linearly independent and therefore must form a basis. Is that the
correct proof, or am I missing something? Also, I don't know what the matrix of
the linear transformation is.
sentences:
- 'To prove that v1 and v2 form a basis, we need to show that they are linearly
independent and that they span the entire subspace.
To show linear independence, suppose that c1v1 + c2v2 = 0 for some scalars c1
and c2. Multiplying both sides by A, we get c1λ1v1 + c2λ2v2 = 0. Multiplying the
first equation by λ1 and subtracting it from the second, we get (λ2 - λ1)c2v2
= 0. Since λ2 - λ1 is nonzero (because the eigenvalues are distinct), we must
have c2 = 0. Substituting this back into the first equation, we get c1v1 = 0,
so c1 = 0. Therefore, v1 and v2 are linearly independent.
To show that v1 and v2 span the entire subspace, we need to show that every vector
in the subspace can be written as a linear combination of v1 and v2. Let w be
an arbitrary vector in the subspace. Then w can be written as a linear combination
of the eigenvectors of A, so w = c1v1 + c2v2 for some scalars c1 and c2. Therefore,
v1 and v2 span the entire subspace.
Since v1 and v2 are linearly independent and span the entire subspace, they form
a basis for the subspace.
The matrix of the linear transformation T_A is the matrix whose columns are the
coordinate vectors of the images of the basis vectors of the domain under T_A.
In this case, the basis vectors of the domain are v1 and v2, and their images
under T_A are λ1v1 and λ2v2, respectively. Therefore, the matrix of T_A is
$$\begin{bmatrix} \lambda_1 & 0\\ 0 & \lambda_2\end{bmatrix}.$$'
- 'To find $E[\tilde{\beta_1}]$, we first need to derive the formula for $\tilde{\beta_1}$.
Under the assumption that the intercept is 0, the slope estimator $\tilde{\beta_1}$
is given by:
$$\tilde{\beta_1} = \frac{\sum_{i=1}^n (x_i - \bar{x})y_i}{\sum_{i=1}^n (x_i -
\bar{x})^2}$$
where $\bar{x}$ is the sample mean of the $x_i$.
Next, we can substitute the true regression model $y_i = \beta_0 + \beta_1 x_i
+ u_i$ into the formula for $\tilde{\beta_1}$:
$$\tilde{\beta_1} = \frac{\sum_{i=1}^n (x_i - \bar{x})(\beta_0 + \beta_1 x_i +
u_i)}{\sum_{i=1}^n (x_i - \bar{x})^2}$$
Simplifying this expression, we get:
$$\tilde{\beta_1} = \beta_1 + \frac{\sum_{i=1}^n (x_i - \bar{x})u_i}{\sum_{i=1}^n
(x_i - \bar{x})^2}$$
Now, we can take the expected value of both sides of this equation:
$$E[\tilde{\beta_1}] = E[\beta_1] + E\left[\frac{\sum_{i=1}^n (x_i - \bar{x})u_i}{\sum_{i=1}^n
(x_i - \bar{x})^2}\right]$$
Since $\beta_1$ is a constant, $E[\beta_1] = \beta_1$. For the second term, we
can use the fact that $E(u_i) = 0$ (by assumption SLR.3) and the linearity of
expectation to get:
$$E\left[\frac{\sum_{i=1}^n (x_i - \bar{x})u_i}{\sum_{i=1}^n (x_i - \bar{x})^2}\right]
= \frac{\sum_{i=1}^n (x_i - \bar{x})E(u_i)}{\sum_{i=1}^n (x_i - \bar{x})^2} =
0$$
Therefore, we have:
$$E[\tilde{\beta_1}] = \beta_1 + 0 = \beta_1$$
This shows that $\tilde{\beta_1}$ is an unbiased estimator of $\beta_1$ when the
intercept is assumed to be 0.
In addition to the case where $\beta_0 = 0$, $\tilde{\beta_1}$ is also an unbiased
estimator of $\beta_1$ when $\sum_{i=1}^n x_i = 0$. This can be seen by noting
that in this case, $\bar{x} = 0$ and the formula for $\tilde{\beta_1}$ simplifies
to:
$$\tilde{\beta_1} = \frac{\sum_{i=1}^n x_iy_i}{\sum_{i=1}^n x_i^2}$$
which is the same as the formula for the ordinary least squares (OLS) estimator
of $\beta_1$ when the intercept is included in the model.'
- 'Sure. Here is an example of a continuous map that is not proper:
$$
f: \mathbb{R} \to [0, 1]
$$
$$
x \mapsto \frac{1}{1 + |x|}
$$
This map is continuous because it is the composition of continuous functions.
However, it is not proper because the preimage of the compact set [0, 1] is not
compact. Specifically, the preimage of [0, 1] is the set of all real numbers,
which is not compact.
This example shows that the converse of the statement "if a map is proper then
it is continuous" is not true.'
- source_sentence: Consider the scenario from the original question, but now suppose
that you draw two balls from the same random box. If both balls are gold, what
is the probability that the box contains exactly two gold balls?
sentences:
- The term $\frac{\partial{F}}{\partial{u}}$ appears because $F$ is a function of
not only $x$, $y$, and $z$, but also of $u$ and $v$. When we differentiate $F$
with respect to $x$, we must consider how $F$ changes with respect to $u$ as well,
since $u$ is a function of $x$.
- 'To prove that U ∪ V is an open set, we must show that for every point x in U
∪ V, there exists a ball B(x, r) with radius r > 0, entirely contained within
U ∪ V.
Let x be an arbitrary point in U ∪ V. We consider two cases:
Case 1: If x ∈ U, since U is open, there exists a ball B(x, r_1) with r_1 > 0
such that B(x, r_1) ⊆ U.
Case 2: If x ∈ V, as V is also open, there exists a ball B(x, r_2) with r_2 >
0 such that B(x, r_2) ⊆ V.
Now, consider the ball B(x, r), where r = min(r_1, r_2). In both cases (x ∈ U
and x ∈ V), this ball has a radius that is less than or equal to the radii of
the balls in the respective sets. Therefore, B(x, r) will be entirely contained
within either U or V, and as x is in U ∪ V, B(x, r) must be contained within the
union of U and V.
Since the choice of x was arbitrary, this shows that for all points in U ∪ V,
there exists a corresponding open ball contained within U ∪ V. Hence, U ∪ V is
an open set in $\mathbb{C}$.'
- There are a total of 12 balls in the boxes, and 6 of them are gold. If we draw
two gold balls, we can eliminate box 4. Out of the remaining 3 boxes, only one
box has exactly two gold balls. Therefore, the probability that the box contains
exactly two gold balls is $\frac{1}{3}$.
- source_sentence: "What should I do if I'm not satisfied with the answers to a question\
\ for which I've offered a bounty?\n\nIn my case, I've put a bounty on a question,\
\ but the two responses I received don't address the issue effectively. I requested\
\ the original poster (OP) to provide an answer so I could reward them for the\
\ interesting question, but they haven't done so. \n\nAre there any acceptable\
\ actions in this scenario? For instance, can I post my own non-answer, award\
\ myself the bounty, and then start a new bounty on a different question? Or are\
\ there alternative suggestions?"
sentences:
- 'To improve RF signal strength under the given conditions, consider the following
suggestions:
1. Bit Rate: Keep the transmitted bit rate low, around 500 bits per second (bps).
2. Balanced Energy Protocol: Implement a biphase or Manchester encoding to ensure
a 50% duty cycle, which helps reduce DC offset at the receiver.
3. Preamble: Include a long preamble in your protocol for the receiver to lock
onto the signal and set its Automatic Gain Control (AGC) before decoding data.
4. Receiver Tolerance: Design the decoding protocol to tolerate a wide range of
pulse widths, as variations due to multi-path, noise, and other factors can affect
signal integrity.
While the current setup might be suitable for short distances, increasing the
transmitter power voltage could potentially improve range. However, since you
cannot change the 3.7V for the receiver, focus on optimizing the mentioned parameters.
For more detailed information and implementation examples, refer to a previous
post or access the resources at: http://www.carousel-design.com/ManchesterDesignDocs.zip'
- 'The issue you''re experiencing with your 40kHz crystal oscillator might be due
to insufficient drive strength and an incorrect load capacitance. Here are two
potential causes and solutions:
1. High Series Resistance: The 150 kΩ series resistance in your circuit might
be too high, which results in a low drive strength for the crystal. This can lead
to a reduced overall loop gain and prevents the oscillator from properly starting.
To resolve this, try using a lower resistance value as recommended in the crystal''s
datasheet.
2. Incorrect Load Capacitance: Ensure that the 33 pF load capacitors you''re using
are compatible with your crystal. Some low-power "watch" crystals require only
5-10 pF load capacitors. Always refer to the crystal''s datasheet to verify the
appropriate load capacitance value.
In summary, carefully review the crystal''s datasheet to determine the correct
series resistance and load capacitance values, and make the necessary adjustments
to your circuit. By doing so, you should be able to resolve the issue and get
your oscillator functioning properly.'
- "If all the provided answers do not adequately address your question, it's advisable\
\ to let the bounty expire. The system will handle the distribution of the bounty\
\ in such situations according to predefined rules.\n\nBounties carry a risk,\
\ as there is no guarantee that you will receive a satisfactory answer, even with\
\ the incentive. It's important to understand that you cannot reclaim your bounty\
\ once it's been offered. \n\nInstead of posting a non-answer, you might consider\
\ editing and clarifying your original question to attract better responses, or\
\ seeking assistance from the community through comments or chat. If needed, you\
\ can also start a new bounty on a different question, but ensure that it's clear\
\ and well-defined to increase the likelihood of receiving quality answers."
model-index:
- name: SentenceTransformer based on mixedbread-ai/mxbai-embed-large-v1
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.7434948318262279
name: Pearson Cosine
- type: spearman_cosine
value: 0.7806376828669657
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7436816396985431
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.749038875811761
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.744095244507457
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7494747710401942
name: Spearman Euclidean
- type: pearson_dot
value: 0.6964434748177516
name: Pearson Dot
- type: spearman_dot
value: 0.707847590788814
name: Spearman Dot
- type: pearson_max
value: 0.744095244507457
name: Pearson Max
- type: spearman_max
value: 0.7806376828669657
name: Spearman Max
---
# SentenceTransformer based on mixedbread-ai/mxbai-embed-large-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the mathstackexchange, socratic and stackexchange datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision c84137389907d1244f65c2f8007a60f8d0a6c0e9 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- mathstackexchange
- socratic
- stackexchange
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("mrm8488/mxbai-embed-large-v1-ft-webinstruct")
# Run inference
sentences = [
"What should I do if I'm not satisfied with the answers to a question for which I've offered a bounty?\n\nIn my case, I've put a bounty on a question, but the two responses I received don't address the issue effectively. I requested the original poster (OP) to provide an answer so I could reward them for the interesting question, but they haven't done so. \n\nAre there any acceptable actions in this scenario? For instance, can I post my own non-answer, award myself the bounty, and then start a new bounty on a different question? Or are there alternative suggestions?",
"If all the provided answers do not adequately address your question, it's advisable to let the bounty expire. The system will handle the distribution of the bounty in such situations according to predefined rules.\n\nBounties carry a risk, as there is no guarantee that you will receive a satisfactory answer, even with the incentive. It's important to understand that you cannot reclaim your bounty once it's been offered. \n\nInstead of posting a non-answer, you might consider editing and clarifying your original question to attract better responses, or seeking assistance from the community through comments or chat. If needed, you can also start a new bounty on a different question, but ensure that it's clear and well-defined to increase the likelihood of receiving quality answers.",
'The issue you\'re experiencing with your 40kHz crystal oscillator might be due to insufficient drive strength and an incorrect load capacitance. Here are two potential causes and solutions:\n\n1. High Series Resistance: The 150 kΩ series resistance in your circuit might be too high, which results in a low drive strength for the crystal. This can lead to a reduced overall loop gain and prevents the oscillator from properly starting. To resolve this, try using a lower resistance value as recommended in the crystal\'s datasheet.\n\n2. Incorrect Load Capacitance: Ensure that the 33 pF load capacitors you\'re using are compatible with your crystal. Some low-power "watch" crystals require only 5-10 pF load capacitors. Always refer to the crystal\'s datasheet to verify the appropriate load capacitance value.\n\nIn summary, carefully review the crystal\'s datasheet to determine the correct series resistance and load capacitance values, and make the necessary adjustments to your circuit. By doing so, you should be able to resolve the issue and get your oscillator functioning properly.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7435 |
| **spearman_cosine** | **0.7806** |
| pearson_manhattan | 0.7437 |
| spearman_manhattan | 0.749 |
| pearson_euclidean | 0.7441 |
| spearman_euclidean | 0.7495 |
| pearson_dot | 0.6964 |
| spearman_dot | 0.7078 |
| pearson_max | 0.7441 |
| spearman_max | 0.7806 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
#### mathstackexchange
* Dataset: mathstackexchange
* Size: 1,484,629 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 90.61 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 307.68 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Suppose $A$ is a normal subgroup of a group $B$, and the quotient group $B/A$ is cyclic with infinite order. How can we demonstrate, using the correspondence theorem, that for every positive integer $k$, $B$ has a normal subgroup of index $k$?</code> | <code>The correspondence theorem relates subgroups of the quotient group $B/A$ to subgroups of $B$ containing $A$. Since $B/A$ is isomorphic to the infinite cyclic group $\mathbb{Z}$, it has subgroups of every finite index. <br><br>To find a normal subgroup of $B$ with index $k$, we can follow these steps:<br>1. Identify a subgroup $M/A$ of $B/A$ with index $k$. This is possible since $\mathbb{Z}$ has subgroups of index $k$ for any positive integer $k$ (for instance, the subgroup generated by $k$ times the generator of $\mathbb{Z}$).<br>2. By the correspondence theorem, there exists an intermediate subgroup $M$ such that $A \leq M \leq B$ with $M/A \cong M/A$.<br>3. Now, consider the index of $M$ in $B$: $[B:M] = [B/A : M/A] = k$, as desired.<br><br>Thus, using the correspondence theorem and exploiting the properties of $\mathbb{Z}$, we can construct a normal subgroup of $B$ with index $k$ for any positive integer $k$.</code> |
| <code>If $z$ is a complex number and $a$ is a real number, can we say $|az| = a|z|$? Here, $|z|$ refers to the modulus of $z$.</code> | <code>Yes, we can say $|az| = a|z|$.<br><br>Let $z = x + iy$ be a complex number and $a$ be a real number. Then,<br><br>$$az = a(x + iy) = ax + iay$$<br><br>and<br><br>$$|az| = \sqrt{(ax)^2 + (ay)^2} = \sqrt{a^2(x^2 + y^2)} = |a| \sqrt{x^2 + y^2} = |a| |z|.$$<br><br>Therefore, $|az| = a|z|$.</code> |
| <code>What is the remainder when $x^{2007}$ is divided by $x^2-x+1$?</code> | <code>To find the remainder when $x^{2007}$ is divided by $x^2-x+1$, we can use polynomial long division or synthetic division. Alternatively, we can utilize the properties of the polynomial $x^2-x+1$.<br><br>Since $x^2-x+1$ is a factor of $x^3+1$, we have $x^3 \equiv -1 \pmod{x^2-x+1}$. Therefore, we can express $x^{2007}$ as $x^{3\cdot669}$, and thus:<br><br>$$x^{2007} = (x^3)^{669} \equiv (-1)^{669} \pmod{x^2-x+1}$$<br><br>Now, since $669$ is odd, $(-1)^{669} = -1$. Hence, the remainder is $-1$.<br><br>Alternatively, we can perform polynomial long division to obtain:<br><br>$$\frac{x^{2007}}{x^2-x+1} = a(x) - \frac{x+1}{x^2-x+1}$$<br><br>where $a(x)$ is the quotient polynomial. Since we only care about the remainder, we have:<br><br>$$x^{2007} \equiv -1 \pmod{x^2-x+1}$$</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### socratic
* Dataset: socratic
* Size: 533,383 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 30.75 tokens</li><li>max: 167 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 207.41 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is an activated complex?</code> | <code><br>The activated complex is formed when the reactants collide with each other and begin to rearrange their atoms and bonds to form the products. This process requires energy, which is why the activated complex has a higher energy than the reactants. The energy required to reach the activated complex is called the activation energy.<br><br>Once the activated complex is formed, it can either decompose back into the reactants or proceed to form the products. The probability of the activated complex decomposing back into the reactants is determined by the activation energy. If the activation energy is high, then the activated complex is more likely to decompose back into the reactants. If the activation energy is low, then the activated complex is more likely to proceed to form the products.<br><br>The activated complex is a key concept in understanding chemical reactions. It helps to explain why some reactions occur quickly and others occur slowly. It also helps to explain why some reactions require a catalyst to occur.<br>####<br>An activated complex is a high-energy, unstable intermediate state that forms during a chemical reaction. It is the transition state between the reactants and the products. The activated complex has a higher energy than both the reactants and the products, and it represents the maximum energy that must be overcome for the reaction to occur.</code> |
| <code>Why does gravity cause planets to be round?</code> | <code><br>The gravitational force of a planet pulls matter towards its center. This force is strongest at the center of the planet and weakest at the surface. As a result, matter is pulled towards the center of the planet, causing it to take on a spherical shape.<br><br>A sphere is the shape with the lowest surface area for a given volume. This means that a planet with a spherical shape has the least amount of potential energy. Potential energy is the energy that an object has due to its position or condition. In the case of a planet, its potential energy is due to its gravitational force.<br><br>The lower the potential energy of a planet, the more stable it is. This is because a planet with a lower potential energy is less likely to change its shape. As a result, planets tend to be spherical in shape.<br>####<br>Gravity causes planets to be round because a sphere is the shape with the lowest surface area for a given volume. This means that a planet with a spherical shape has the least amount of potential energy, which is the energy that an object has due to its position or condition. Gravity pulls matter towards the center of a planet, and this force is strongest at the center. As a result, matter is pulled towards the center of the planet, causing it to take on a spherical shape.</code> |
| <code>How many carbon atoms are present in a 5.85-gram sample of carbon tetrabromide (CBr4)?</code> | <code>There are approximately \(1 \times 10^{22}\) carbon atoms in the given sample.<br><br>Explanation:<br>To determine the number of carbon atoms, we first need to calculate the molar amount of CBr4. The molar mass of CBr4 is 331.63 g/mol. So, we have:<br><br>\[<br>\frac{5.85\ g}{331.63\ g/mol} = 0.0176\ mol<br>\]<br><br>Since one molecule of CBr4 contains one carbon atom and four bromine atoms, there are:<br><br>\[<br>1 \times 0.0176\ mol = 0.0176\ mol\ of\ carbon\ atoms<br>\]<br><br>Now, multiplying the molar quantity by Avogadro's number (6.022 × 10^23 mol^(-1)) gives us the number of individual carbon atoms:<br><br>\[<br>0.0176\ mol \times 6.022 \times 10^{23}\ mol^{-1} = 1.06 \times 10^{22}\ carbon\ atoms<br>\]<br><br>Therefore, there are approximately \(1 \times 10^{22}\) carbon atoms in a 5.85-gram sample of CBr4.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### stackexchange
* Dataset: stackexchange
* Size: 317,208 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 64.07 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 50 tokens</li><li>mean: 264.62 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Should I use a tip activator to recoat the worn protective coating on my iron tip, or is it better to replace the tip entirely? My 48W ZD99 Solder Station's tip is showing signs of peeling due to moisture exposure and inadequate care. Can the tip activator effectively restore the tip, or should I opt for a new one?</code> | <code>To address the issue, first clean the iron tip with a wire brush to remove any debris. Then, apply flux and tin the tip to protect it and maintain its performance. Tip activators are available as a means to recoat tips, but their effectiveness may vary. While they can be a viable solution, it's essential to ensure proper tip care to prevent future wear. If the tip's condition significantly deteriorates despite these efforts, consider replacing it with a new one.</code> |
| <code>What are the fundamental limits, if any, for the speed of sound in different materials, and how do these limits relate to the speed of light?</code> | <code>The speed of sound is limited by the properties of the material it travels through and the fundamental principles of physics. In a theoretical sense, the maximum speed of sound is constrained by the speed of light (approximately 299,792 km/s in vacuum), which is the maximum speed at which information can propagate. This limit is reached when the material has an incompressible equation of state, such as in the core of a neutron star, where the strong nuclear force creates immense pressure resistance.<br><br>For an ideal gas, where particles do not interact, the equation of state is the softest possible with $P = \rho c^2/3$, where $P$ is pressure, $\rho$ is density, and $c$ is the speed of light. In this case, the maximum speed of sound would be $c/\sqrt{3}$.<br><br>It's important to note that in practice, materials with extremely high sound speeds are unlikely to exist due to the conditions required for an incompressible equation of state. In reality, materials like solids and liquids generally have faster sound speeds than gases, but they are still far below the speed of light.<br><br>When dealing with exotic materials, such as short-lived isotopes or neutron stars, the speed of sound may be even more challenging to determine due to the unique properties and states involved. However, the underlying principles remain the same: the speed of sound is determined by the material's properties, and it cannot exceed the speed of light in a vacuum.</code> |
| <code>What could be causing a 1996 Honda Civic to stop running suddenly, and how can it be started?</code> | <code>A potential issue is a faulty ignition switch. When you attempt to start the car, the switch might be malfunctioning in such a way that it disrupts power to the engine ignition system, causing the dash lights to go out and preventing the car from starting. However, when you perform a push start (crash start), the car starts because the ignition switch remains in position 2, providing power to the engine.<br><br>Another possibility is a problem with the battery or its connections. If the battery terminals have a poor connection, it might lead to high resistance, making it difficult for the car to start. Alternatively, if the battery is weak, it might not supply enough power to crank the engine effectively. In this case, the starter motor would sound sluggish as it tries to turn the engine. To resolve the issue, inspect the ignition switch, battery connections, and consider testing or replacing the battery if necessary.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | sts-dev_spearman_cosine |
|:------:|:-----:|:-------------:|:-----------------------:|
| 0.0034 | 100 | 0.1339 | - |
| 0.0067 | 200 | 0.0535 | - |
| 0.0101 | 300 | 0.0372 | - |
| 0.0135 | 400 | 0.0329 | - |
| 0.0168 | 500 | 0.0277 | - |
| 0.0202 | 600 | 0.0287 | - |
| 0.0235 | 700 | 0.0217 | - |
| 0.0269 | 800 | 0.0257 | - |
| 0.0303 | 900 | 0.0262 | - |
| 0.0336 | 1000 | 0.02 | 0.8994 |
| 0.0370 | 1100 | 0.0196 | - |
| 0.0404 | 1200 | 0.0231 | - |
| 0.0437 | 1300 | 0.0228 | - |
| 0.0471 | 1400 | 0.0187 | - |
| 0.0504 | 1500 | 0.0197 | - |
| 0.0538 | 1600 | 0.0245 | - |
| 0.0572 | 1700 | 0.028 | - |
| 0.0605 | 1800 | 0.0242 | - |
| 0.0639 | 1900 | 0.0255 | - |
| 0.0673 | 2000 | 0.0324 | 0.8936 |
| 0.0706 | 2100 | 0.0231 | - |
| 0.0740 | 2200 | 0.0335 | - |
| 0.0773 | 2300 | 0.0221 | - |
| 0.0807 | 2400 | 0.0285 | - |
| 0.0841 | 2500 | 0.0394 | - |
| 0.0874 | 2600 | 0.0306 | - |
| 0.0908 | 2700 | 0.0305 | - |
| 0.0942 | 2800 | 0.0349 | - |
| 0.0975 | 2900 | 0.0327 | - |
| 0.1009 | 3000 | 0.0241 | 0.8788 |
| 0.1042 | 3100 | 0.0344 | - |
| 0.1076 | 3200 | 0.0315 | - |
| 0.1110 | 3300 | 0.035 | - |
| 0.1143 | 3400 | 0.0365 | - |
| 0.1177 | 3500 | 0.0363 | - |
| 0.1211 | 3600 | 0.0402 | - |
| 0.1244 | 3700 | 0.0332 | - |
| 0.1278 | 3800 | 0.0317 | - |
| 0.1311 | 3900 | 0.0292 | - |
| 0.1345 | 4000 | 0.0357 | 0.8686 |
| 0.1379 | 4100 | 0.0365 | - |
| 0.1412 | 4200 | 0.0349 | - |
| 0.1446 | 4300 | 0.0344 | - |
| 0.1480 | 4400 | 0.0295 | - |
| 0.1513 | 4500 | 0.0356 | - |
| 0.1547 | 4600 | 0.036 | - |
| 0.1580 | 4700 | 0.0301 | - |
| 0.1614 | 4800 | 0.039 | - |
| 0.1648 | 4900 | 0.0279 | - |
| 0.1681 | 5000 | 0.0388 | 0.8635 |
| 0.1715 | 5100 | 0.0261 | - |
| 0.1749 | 5200 | 0.0308 | - |
| 0.1782 | 5300 | 0.0404 | - |
| 0.1816 | 5400 | 0.0315 | - |
| 0.1849 | 5500 | 0.0397 | - |
| 0.1883 | 5600 | 0.0361 | - |
| 0.1917 | 5700 | 0.031 | - |
| 0.1950 | 5800 | 0.0271 | - |
| 0.1984 | 5900 | 0.0287 | - |
| 0.2018 | 6000 | 0.0356 | 0.8571 |
| 0.2051 | 6100 | 0.0243 | - |
| 0.2085 | 6200 | 0.0193 | - |
| 0.2118 | 6300 | 0.0232 | - |
| 0.2152 | 6400 | 0.032 | - |
| 0.2186 | 6500 | 0.0282 | - |
| 0.2219 | 6600 | 0.0275 | - |
| 0.2253 | 6700 | 0.026 | - |
| 0.2287 | 6800 | 0.0333 | - |
| 0.2320 | 6900 | 0.0298 | - |
| 0.2354 | 7000 | 0.033 | 0.8218 |
| 0.2387 | 7100 | 0.0265 | - |
| 0.2421 | 7200 | 0.0247 | - |
| 0.2455 | 7300 | 0.0233 | - |
| 0.2488 | 7400 | 0.0303 | - |
| 0.2522 | 7500 | 0.0272 | - |
| 0.2556 | 7600 | 0.028 | - |
| 0.2589 | 7700 | 0.0259 | - |
| 0.2623 | 7800 | 0.0305 | - |
| 0.2656 | 7900 | 0.0237 | - |
| 0.2690 | 8000 | 0.0227 | 0.8368 |
| 0.2724 | 8100 | 0.0216 | - |
| 0.2757 | 8200 | 0.0277 | - |
| 0.2791 | 8300 | 0.0197 | - |
| 0.2825 | 8400 | 0.0231 | - |
| 0.2858 | 8500 | 0.0232 | - |
| 0.2892 | 8600 | 0.0315 | - |
| 0.2925 | 8700 | 0.0198 | - |
| 0.2959 | 8800 | 0.0236 | - |
| 0.2993 | 8900 | 0.0243 | - |
| 0.3026 | 9000 | 0.0213 | 0.8118 |
| 0.3060 | 9100 | 0.0264 | - |
| 0.3094 | 9200 | 0.0218 | - |
| 0.3127 | 9300 | 0.0232 | - |
| 0.3161 | 9400 | 0.0192 | - |
| 0.3194 | 9500 | 0.018 | - |
| 0.3228 | 9600 | 0.0225 | - |
| 0.3262 | 9700 | 0.0225 | - |
| 0.3295 | 9800 | 0.0207 | - |
| 0.3329 | 9900 | 0.0264 | - |
| 0.3363 | 10000 | 0.0314 | 0.8286 |
| 0.3396 | 10100 | 0.0246 | - |
| 0.3430 | 10200 | 0.0224 | - |
| 0.3463 | 10300 | 0.0246 | - |
| 0.3497 | 10400 | 0.0212 | - |
| 0.3531 | 10500 | 0.0166 | - |
| 0.3564 | 10600 | 0.0253 | - |
| 0.3598 | 10700 | 0.0221 | - |
| 0.3632 | 10800 | 0.0175 | - |
| 0.3665 | 10900 | 0.0254 | - |
| 0.3699 | 11000 | 0.0181 | 0.7995 |
| 0.3732 | 11100 | 0.0176 | - |
| 0.3766 | 11200 | 0.0196 | - |
| 0.3800 | 11300 | 0.02 | - |
| 0.3833 | 11400 | 0.0219 | - |
| 0.3867 | 11500 | 0.0265 | - |
| 0.3901 | 11600 | 0.0217 | - |
| 0.3934 | 11700 | 0.0161 | - |
| 0.3968 | 11800 | 0.0145 | - |
| 0.4001 | 11900 | 0.0184 | - |
| 0.4035 | 12000 | 0.0166 | 0.8185 |
| 0.4069 | 12100 | 0.0177 | - |
| 0.4102 | 12200 | 0.0231 | - |
| 0.4136 | 12300 | 0.0215 | - |
| 0.4170 | 12400 | 0.0226 | - |
| 0.4203 | 12500 | 0.0144 | - |
| 0.4237 | 12600 | 0.0174 | - |
| 0.4270 | 12700 | 0.0176 | - |
| 0.4304 | 12800 | 0.0214 | - |
| 0.4338 | 12900 | 0.0206 | - |
| 0.4371 | 13000 | 0.0197 | 0.7957 |
| 0.4405 | 13100 | 0.0216 | - |
| 0.4439 | 13200 | 0.0211 | - |
| 0.4472 | 13300 | 0.0198 | - |
| 0.4506 | 13400 | 0.0161 | - |
| 0.4539 | 13500 | 0.0123 | - |
| 0.4573 | 13600 | 0.0168 | - |
| 0.4607 | 13700 | 0.0188 | - |
| 0.4640 | 13800 | 0.0145 | - |
| 0.4674 | 13900 | 0.0221 | - |
| 0.4708 | 14000 | 0.0207 | 0.8036 |
| 0.4741 | 14100 | 0.0186 | - |
| 0.4775 | 14200 | 0.0199 | - |
| 0.4809 | 14300 | 0.0219 | - |
| 0.4842 | 14400 | 0.0131 | - |
| 0.4876 | 14500 | 0.0152 | - |
| 0.4909 | 14600 | 0.0159 | - |
| 0.4943 | 14700 | 0.0165 | - |
| 0.4977 | 14800 | 0.0145 | - |
| 0.5010 | 14900 | 0.0143 | - |
| 0.5044 | 15000 | 0.0135 | 0.7920 |
| 0.5078 | 15100 | 0.0159 | - |
| 0.5111 | 15200 | 0.0111 | - |
| 0.5145 | 15300 | 0.0198 | - |
| 0.5178 | 15400 | 0.0142 | - |
| 0.5212 | 15500 | 0.0167 | - |
| 0.5246 | 15600 | 0.0118 | - |
| 0.5279 | 15700 | 0.0151 | - |
| 0.5313 | 15800 | 0.0172 | - |
| 0.5347 | 15900 | 0.0135 | - |
| 0.5380 | 16000 | 0.0159 | 0.8073 |
| 0.5414 | 16100 | 0.0146 | - |
| 0.5447 | 16200 | 0.0127 | - |
| 0.5481 | 16300 | 0.0158 | - |
| 0.5515 | 16400 | 0.0138 | - |
| 0.5548 | 16500 | 0.0102 | - |
| 0.5582 | 16600 | 0.0127 | - |
| 0.5616 | 16700 | 0.0166 | - |
| 0.5649 | 16800 | 0.0137 | - |
| 0.5683 | 16900 | 0.0127 | - |
| 0.5716 | 17000 | 0.014 | 0.7942 |
| 0.5750 | 17100 | 0.0151 | - |
| 0.5784 | 17200 | 0.0134 | - |
| 0.5817 | 17300 | 0.0119 | - |
| 0.5851 | 17400 | 0.0096 | - |
| 0.5885 | 17500 | 0.0129 | - |
| 0.5918 | 17600 | 0.0133 | - |
| 0.5952 | 17700 | 0.0084 | - |
| 0.5985 | 17800 | 0.0114 | - |
| 0.6019 | 17900 | 0.0123 | - |
| 0.6053 | 18000 | 0.0115 | 0.7615 |
| 0.6086 | 18100 | 0.0109 | - |
| 0.6120 | 18200 | 0.0098 | - |
| 0.6154 | 18300 | 0.0167 | - |
| 0.6187 | 18400 | 0.0117 | - |
| 0.6221 | 18500 | 0.0133 | - |
| 0.6254 | 18600 | 0.0089 | - |
| 0.6288 | 18700 | 0.0125 | - |
| 0.6322 | 18800 | 0.0101 | - |
| 0.6355 | 18900 | 0.0143 | - |
| 0.6389 | 19000 | 0.0108 | 0.8011 |
| 0.6423 | 19100 | 0.0164 | - |
| 0.6456 | 19200 | 0.0099 | - |
| 0.6490 | 19300 | 0.0112 | - |
| 0.6523 | 19400 | 0.0184 | - |
| 0.6557 | 19500 | 0.0178 | - |
| 0.6591 | 19600 | 0.0111 | - |
| 0.6624 | 19700 | 0.0101 | - |
| 0.6658 | 19800 | 0.0146 | - |
| 0.6692 | 19900 | 0.0149 | - |
| 0.6725 | 20000 | 0.0139 | 0.8151 |
| 0.6759 | 20100 | 0.0146 | - |
| 0.6792 | 20200 | 0.0086 | - |
| 0.6826 | 20300 | 0.0168 | - |
| 0.6860 | 20400 | 0.0101 | - |
| 0.6893 | 20500 | 0.0101 | - |
| 0.6927 | 20600 | 0.0086 | - |
| 0.6961 | 20700 | 0.0108 | - |
| 0.6994 | 20800 | 0.0092 | - |
| 0.7028 | 20900 | 0.0119 | - |
| 0.7061 | 21000 | 0.0136 | 0.8046 |
| 0.7095 | 21100 | 0.0106 | - |
| 0.7129 | 21200 | 0.0123 | - |
| 0.7162 | 21300 | 0.0108 | - |
| 0.7196 | 21400 | 0.0112 | - |
| 0.7230 | 21500 | 0.0096 | - |
| 0.7263 | 21600 | 0.0074 | - |
| 0.7297 | 21700 | 0.0104 | - |
| 0.7330 | 21800 | 0.0079 | - |
| 0.7364 | 21900 | 0.0061 | - |
| 0.7398 | 22000 | 0.0064 | 0.7948 |
| 0.7431 | 22100 | 0.0091 | - |
| 0.7465 | 22200 | 0.0091 | - |
| 0.7499 | 22300 | 0.006 | - |
| 0.7532 | 22400 | 0.0081 | - |
| 0.7566 | 22500 | 0.0084 | - |
| 0.7599 | 22600 | 0.0109 | - |
| 0.7633 | 22700 | 0.0124 | - |
| 0.7667 | 22800 | 0.0108 | - |
| 0.7700 | 22900 | 0.009 | - |
| 0.7734 | 23000 | 0.0118 | 0.7956 |
| 0.7768 | 23100 | 0.011 | - |
| 0.7801 | 23200 | 0.0093 | - |
| 0.7835 | 23300 | 0.0097 | - |
| 0.7868 | 23400 | 0.0069 | - |
| 0.7902 | 23500 | 0.0081 | - |
| 0.7936 | 23600 | 0.0092 | - |
| 0.7969 | 23700 | 0.01 | - |
| 0.8003 | 23800 | 0.0112 | - |
| 0.8037 | 23900 | 0.0076 | - |
| 0.8070 | 24000 | 0.0098 | 0.8005 |
| 0.8104 | 24100 | 0.0083 | - |
| 0.8137 | 24200 | 0.0089 | - |
| 0.8171 | 24300 | 0.0125 | - |
| 0.8205 | 24400 | 0.0051 | - |
| 0.8238 | 24500 | 0.009 | - |
| 0.8272 | 24600 | 0.0086 | - |
| 0.8306 | 24700 | 0.0075 | - |
| 0.8339 | 24800 | 0.0069 | - |
| 0.8373 | 24900 | 0.0065 | - |
| 0.8406 | 25000 | 0.0092 | 0.7830 |
| 0.8440 | 25100 | 0.0077 | - |
| 0.8474 | 25200 | 0.0049 | - |
| 0.8507 | 25300 | 0.0061 | - |
| 0.8541 | 25400 | 0.0115 | - |
| 0.8575 | 25500 | 0.0086 | - |
| 0.8608 | 25600 | 0.006 | - |
| 0.8642 | 25700 | 0.0083 | - |
| 0.8675 | 25800 | 0.0067 | - |
| 0.8709 | 25900 | 0.0069 | - |
| 0.8743 | 26000 | 0.0083 | 0.7734 |
| 0.8776 | 26100 | 0.007 | - |
| 0.8810 | 26200 | 0.0086 | - |
| 0.8844 | 26300 | 0.0077 | - |
| 0.8877 | 26400 | 0.0138 | - |
| 0.8911 | 26500 | 0.0054 | - |
| 0.8944 | 26600 | 0.008 | - |
| 0.8978 | 26700 | 0.0076 | - |
| 0.9012 | 26800 | 0.0094 | - |
| 0.9045 | 26900 | 0.0069 | - |
| 0.9079 | 27000 | 0.0066 | 0.7821 |
| 0.9113 | 27100 | 0.0068 | - |
| 0.9146 | 27200 | 0.0056 | - |
| 0.9180 | 27300 | 0.0067 | - |
| 0.9213 | 27400 | 0.0061 | - |
| 0.9247 | 27500 | 0.0072 | - |
| 0.9281 | 27600 | 0.0086 | - |
| 0.9314 | 27700 | 0.006 | - |
| 0.9348 | 27800 | 0.0063 | - |
| 0.9382 | 27900 | 0.0095 | - |
| 0.9415 | 28000 | 0.007 | 0.7833 |
| 0.9449 | 28100 | 0.0128 | - |
| 0.9482 | 28200 | 0.0081 | - |
| 0.9516 | 28300 | 0.0059 | - |
| 0.9550 | 28400 | 0.0067 | - |
| 0.9583 | 28500 | 0.0059 | - |
| 0.9617 | 28600 | 0.0057 | - |
| 0.9651 | 28700 | 0.0055 | - |
| 0.9684 | 28800 | 0.0065 | - |
| 0.9718 | 28900 | 0.0065 | - |
| 0.9752 | 29000 | 0.0072 | 0.7806 |
| 0.9785 | 29100 | 0.0107 | - |
| 0.9819 | 29200 | 0.0083 | - |
| 0.9852 | 29300 | 0.01 | - |
| 0.9886 | 29400 | 0.0044 | - |
| 0.9920 | 29500 | 0.0056 | - |
| 0.9953 | 29600 | 0.0053 | - |
| 0.9987 | 29700 | 0.0081 | - |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.0
- Datasets: 2.21.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
John6666/unstable-illusion-sdxl-sdxl-sdxl
|
John6666
| 2024-09-04T23:43:06Z | 215 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"game",
"actress",
"person",
"nsfw",
"sfw",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-09-04T23:37:34Z |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- game
- actress
- person
- nsfw
- sfw
---
Original model is [here](https://civitai.com/models/512643/unstable-illusion-sdxl?modelVersionId=805836).
This model created by [Peli86](https://civitai.com/user/Peli86).
|
John6666/perfectly-horrible-v10-sdxl
|
John6666
| 2024-09-04T23:39:56Z | 81 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"realistic",
"2D",
"3D",
"blender",
"diverse",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-09-04T23:34:47Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- realistic
- 2D
- 3D
- blender
- diverse
- pony
---
Original model is [here](https://civitai.com/models/720851/perfectlyhorrible?modelVersionId=806048).
This model created by [KarotConCarne](https://civitai.com/user/KarotConCarne).
|
Sicarius-Prototyping/Variety_RP_Alpha_GGUFs
|
Sicarius-Prototyping
| 2024-09-04T23:34:18Z | 8 | 0 | null |
[
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-04T22:36:16Z |
---
license: apache-2.0
---
|
SongTonyLi/SFT_D1chosenThenDPO_D2a_Instruct_argilla_math_results
|
SongTonyLi
| 2024-09-04T23:32:19Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"dpo",
"conversational",
"en",
"base_model:SongTonyLi/SFT_D1chosen_math_cosineLR_instruct",
"base_model:finetune:SongTonyLi/SFT_D1chosen_math_cosineLR_instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-09-04T20:07:57Z |
---
base_model: SongTonyLi/SFT_D1chosen_math_cosineLR_instruct
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- dpo
---
# Uploaded model
- **Developed by:** SongTonyLi
- **License:** apache-2.0
- **Finetuned from model :** SongTonyLi/SFT_D1chosen_math_cosineLR_instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
rohansangave/textual_inversion_cat
|
rohansangave
| 2024-09-04T23:29:00Z | 9 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"diffusers-training",
"base_model:stabilityai/stable-diffusion-2",
"base_model:adapter:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-09-04T21:46:34Z |
---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
- diffusers-training
base_model: stabilityai/stable-diffusion-2
inference: true
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Textual inversion text2image fine-tuning - rohansangave/textual_inversion_cat
These are textual inversion adaption weights for stabilityai/stable-diffusion-2. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
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