modelId
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-28 00:48:09
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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Avitas8485/Dialogpt-medium-v2
|
Avitas8485
| 2023-06-22T02:05:05Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-27T05:26:04Z |
---
pipeline_tag: conversational
---
|
natope/mT5-tfidf-10pass-all-questions-QA-22-06-2023
|
natope
| 2023-06-22T01:59:17Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-22T00:35:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mT5-tfidf-10pass-all-questions-QA-22-06-2023
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. -->
# mT5-tfidf-10pass-all-questions-QA-22-06-2023
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1052
- Rouge1: 0.135
- Rouge2: 0.0293
- Rougel: 0.1091
- Rougelsum: 0.1091
- Gen Len: 18.3641
## 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: 2
- eval_batch_size: 2
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.3074 | 1.0 | 3288 | 2.3090 | 0.0802 | 0.0067 | 0.0711 | 0.0711 | 15.4922 |
| 2.7161 | 2.0 | 6576 | 2.1227 | 0.0805 | 0.0166 | 0.0665 | 0.0664 | 13.4977 |
| 2.6099 | 3.0 | 9864 | 2.1052 | 0.135 | 0.0293 | 0.1091 | 0.1091 | 18.3641 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
mike-ravkine/BlueHeeler-12M
|
mike-ravkine
| 2023-06-22T01:55:14Z | 6 | 0 |
transformers
|
[
"transformers",
"gpt2",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-22T01:44:07Z |
---
license: mit
language:
- en
pipeline_tag: text-generation
widget:
- text: 'Bluey:'
example_title: Dialogue 1
- text: 'Mom:'
example_title: Dialogue 2
library_name: transformers
---
BlueHeeler-10M is a nanoGPT (GPT-2) 6-head x 6-layer x 192-deep model with a context size of 64 trained on scripts from the children's show Bluey
`iter 2000: loss 1.2913, time 30647.72ms, mfu 0.05%`
|
benbav97/ppo-LunarLander-v2
|
benbav97
| 2023-06-22T01:54:37Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T01:41:22Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 278.12 +/- 17.65
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
johnpaulbin/gpt2-skript-1m-v5
|
johnpaulbin
| 2023-06-22T01:48:20Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
## GPT-2 for Skript
## Complete your Skript automatically via a finetuned GPT-2 model
`0.57` Training loss on about 2 epochs (in total)
1.2 million lines of Skript is inside the dataset.
Inference Colab: https://colab.research.google.com/drive/1ujtLt7MOk7Nsag3q-BYK62Kpoe4Lr4PE
|
bluemoonwj/my_awesome_eli5_clm-model
|
bluemoonwj
| 2023-06-22T01:34:22Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-22T00:53:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
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. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7297
## 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: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8699 | 1.0 | 1109 | 3.7485 |
| 3.7734 | 2.0 | 2218 | 3.7342 |
| 3.7371 | 3.0 | 3327 | 3.7297 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
agustinl/ppo-Huggy
|
agustinl
| 2023-06-22T01:29:30Z | 13 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-22T01:29:20Z |
---
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: agustinl/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
zslrmhb/ppo-LunarLander-v2
|
zslrmhb
| 2023-06-22T00:59:42Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-20T18:25:42Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 285.48 +/- 18.94
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
cactusfriend/nightmare-invokeai-prompts
|
cactusfriend
| 2023-06-22T00:48:13Z | 126 | 6 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt_neo",
"text-generation",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-04-02T15:30:11Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
widget:
- text: "a photograph of"
example_title: "photo"
- text: "a bizarre cg render"
example_title: "render"
- text: "the spaghetti"
example_title: "meal?"
- text: "a (detailed+ intricate)+ picture"
example_title: "weights"
- text: "photograph of various"
example_title: "variety"
inference:
parameters:
temperature: 2.6
max_new_tokens: 250
---
A model based upon the prompts of all the images in my InvokeAI's output directory, meant to be used with [InvokeAI](https://github.com/invoke-ai/InvokeAI) (a Stable Diffusion implementation/UI) to generate new, probably wild nightmare images.
This is mostly trained on positive prompts, though you may catch some words in [] brackets, which will be treated as negative.
GPT-Neo is usually quite good at pairing parenthesis, quotation marks, etc - however, don't be too surprised if it generates something that's not quite InvokeAI prompt syntax.
To use this model, you can import it as a pipeline like so:
```py
from transformers import pipeline
generator = pipeline(model="cactusfriend/nightmare-invokeai-prompts",
tokenizer="cactusfriend/nightmare-invokeai-prompts",
task="text-generation")
```
Here's an example function that'll generate by default 20 prompts, at a temperature of 1.8 which seems good for this model.
```py
def makePrompts(prompt: str, *, p: float=0.9,
k: int = 40, num: int = 20,
temp: float = 1.8, mnt: int = 150):
outputs = generator(prompt, max_new_tokens=mnt,
temperature=temp, do_sample=True,
top_p=p, top_k=k, num_return_sequences=num)
items = set([i['generated_text'] for i in outputs])
print("-" * 60)
print("\n ---\n".join(items))
print("-" * 60)
```
Then, you can call it like so:
```py
makePrompts("a photograph of")
# or, to change some defaults:
makePrompts("spaghetti all over", temp=1.4, p=0.92, k=45)
```
|
MiguelQr/ppo-LunarLander-v2
|
MiguelQr
| 2023-06-22T00:45:26Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T00:45:06Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 243.57 +/- 34.67
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Leuserrrr/finetuning-sentiment-model-amazonbaby5000
|
Leuserrrr
| 2023-06-22T00:43:37Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-21T23:57:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-amazonbaby5000
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. -->
# finetuning-sentiment-model-amazonbaby5000
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8039
- Accuracy: 0.9008
## 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: 1
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.0
- Tokenizers 0.11.0
|
mihirdeo16/vizdoom_health_gathering_supreme
|
mihirdeo16
| 2023-06-22T00:11:01Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T05:12:55Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.73 +/- 4.77
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r mihirdeo16/vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
sxx123/finetune_jingzhan
|
sxx123
| 2023-06-22T00:10:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:customized",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-22T00:07:26Z |
---
tags:
- generated_from_trainer
datasets:
- customized
model-index:
- name: finetune_jingzhan
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. -->
# finetune_jingzhan
This model is a fine-tuned version of [/home/sxx/LMFlow/models/gpt2](https://huggingface.co//home/sxx/LMFlow/models/gpt2) on the customized 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: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.01
### Training results
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.3
|
agustinl/dqn-LunarLander-v2
|
agustinl
| 2023-06-21T23:59:16Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T23:58:50Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -51.76 +/- 82.58
name: mean_reward
verified: false
---
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ztjona/scopic-diffusion-OW-v1.4.1
|
ztjona
| 2023-06-21T23:53:15Z | 11 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"dataset:ztjona/oswaldo-guayasamin-blip-captions-v2",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-21T15:54:37Z |
---
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
widget:
- text: city and clouds
example_title: city and clouds
- text: tea party
example_title: tea party
- text: mother working
example_title: mother working
- text: buddhist monk
example_title: buddhist monk
datasets:
- ztjona/oswaldo-guayasamin-blip-captions-v2
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
---
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Finetuned from model:** CompVis/stable-diffusion-v1-4
|
hannahh7/a2c-PandaReachDense-v2
|
hannahh7
| 2023-06-21T22:26:50Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-15T10:20:48Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -6.04 +/- 3.80
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hts98/whisper-large-paper_
|
hts98
| 2023-06-21T22:18:40Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-21T18:32:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-paper_
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-large-paper_
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4374
- Wer: 47.9863
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 143 | 0.3754 | 47.3394 |
| No log | 2.0 | 286 | 0.3418 | 44.5511 |
| No log | 3.0 | 429 | 0.3522 | 47.7507 |
| 0.3895 | 4.0 | 572 | 0.3795 | 48.9312 |
| 0.3895 | 5.0 | 715 | 0.4091 | 51.5160 |
| 0.3895 | 6.0 | 858 | 0.4374 | 47.9863 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.2
|
enkaell/short-jokes
|
enkaell
| 2023-06-21T22:11:02Z | 5 | 0 |
transformers
|
[
"transformers",
"gpt2",
"text-generation",
"en",
"dataset:Fraser/short-jokes",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T16:38:44Z |
---
datasets:
- Fraser/short-jokes
language:
- en
---
|
thisjustinh/falcon-7b-cnn-dailymail
|
thisjustinh
| 2023-06-21T22:01:30Z | 0 | 0 | null |
[
"text-generation-inference",
"dataset:cnn_dailymail",
"license:apache-2.0",
"region:us"
] | null | 2023-06-20T01:05:22Z |
---
tags:
- text-generation-inference
datasets:
- cnn_dailymail
model-index:
- name: falcon-7b-cnn-dailymail
results: []
license: apache-2.0
---
# falcon-7b-cnn-dailymail
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on the cnn_dailymail dataset.
## Model description
The model inherits the architecture and tokenizer from falcon-7b, but was finetuned using 4-bit quantization from `bitsandbytes` and QLORA from the `peft` library. The HuggingFace `trl` library has a SFTTrainer class that oversaw the fine-tune process.
The resulting model comes from fine-tuning on a single NVIDIA L4 instance (24 GB VRAM) from Google Cloud Platform.
## Intended uses & limitations
The model is intended to be used for summarizing news articles. Since the fine-tuning dataset is cnn_dailymail, it's worth limiting to shorter articles from CNN and the Daily Mail for best results. The model is not intended for other summarization purposes, although it would be interesting to see if its summarization capabilities extend to other short forms of text.
## Training and evaluation data
The model was fine-tuned over the [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail) dataset (the train set specifically), where articles were the "prompts" and highlights were the "responses." Prior to training, the two columns were combined for the causal LM task.
Each observation was formatted as the following:
```
### Article
Article goes here...
### Summary
Highlights go here...
```
For inference, formatting the article in the same way and finishing with the summary tag indicates that the model should generate a summary.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 5
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 25
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 500
### Training results
Good question, haven't really looked into it yet. Also worth noting that these are generally arbitrary hyperparameters, since no tuning was performed.
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
VMware/bert-tiny-mrqa
|
VMware
| 2023-06-21T21:59:31Z | 171 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:mrqa",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-17T20:52:30Z |
---
license: apache-2.0
datasets:
- mrqa
language:
- en
metrics:
- exact_match
- f1
model-index:
- name: VMware/TinyRoBERTa-MRQA
results:
- task:
type: Question-Answering
dataset:
type: mrqa # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: mrqa # Required. A pretty name for the dataset. Example: Common Voice (French)
metrics:
- type: exact_match
value: 22.78
name: Eval EM
- type: f1
value: 32.42
name: Eval F1
- type: exact_match
value: 10.18
name: Test EM
- type: f1
value: 18.72
name: Test F1
---
This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab.
# Model Details
- **Model name:** BERT-Tiny-MRQA
- **Model type:** Extractive Question Answering
- **Parent Model:** [BERT-Tiny-uncased](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2)
- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
- **Training data size:** 516,819 examples
- **Training time:** 26:11 on 1 Nvidia V100 32GB GPU
- **Language:** English
- **Framework:** PyTorch
- **Model version:** 1.0
# Intended Use
This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.
# How to Use
```python
from transformers import pipeline
question_answerer = pipeline("question-answering", model='VMware/bert-tiny-mrqa')
context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."
question = "What is MRQA?"
result = question_answerer(question=question, context=context)
print(result)
# {
# 'score': 0.134057879447937,
# 'start': 76,
# 'end': 80,
# 'answer': '2019'
# }
```
Yes, you read that correctly ... this model thinks MRQA is "2019". Look at its eval and test scores. A coin toss is more likely to get you a decent answer, haha.
# Training Details
The model was trained for 1 epoch on the MRQA training set.
## Training Hyperparameters
```python
args = TrainingArguments(
"bert-tiny-mrqa",
save_strategy="epoch",
learning_rate=1e-5,
num_train_epochs=1,
weight_decay=0.01,
per_device_train_batch_size=16,
)
```
# Evaluation Metrics
The model was evaluated using standard metrics for question-answering models, including:
Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.
F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.
# Model Family Performance
| Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 |
|---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 |
| BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 |
| BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 |
| DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 |
| DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 |
| DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** |
| ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 |
| ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 |
| ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 |
| MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 |
| MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 |
| MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 |
| MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 |
| MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 |
| TinyRoBERTa | 81,529.346 | 4:27:06\* | 9:54 | 1:04 | 69.38 | 80.07 | 53.29 | 64.16 |
| RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 |
| RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 |
\* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA.
# Limitations and Bias
The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
- Language: The model is designed to work with English text only and may not perform as well on other languages.
- Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
- Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.
In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
|
owaiskaifi/ai-qr-generator
|
owaiskaifi
| 2023-06-21T21:42:57Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-06-21T21:40:17Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [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 Data 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 Data 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]
|
Mursel/falcon-7b-instruct-finetuned
|
Mursel
| 2023-06-21T21:32:44Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-13T13:15:02Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
ShaneEP77/tolkientexts
|
ShaneEP77
| 2023-06-21T20:54:01Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"text generation",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-12T14:46:14Z |
---
language: en
thumbnail: "https://www.billboard.com/wp-content/uploads/media/Middle-earth-Shadow-of-War-GAME-Screenshot-2017-billboard-1548.jpg"
tags:
- text generation
- pytorch
license: mit
---
### Tolkientexts Model
Welcome! This README.md aims to provide a synopsis of how this model was trained and fine-tuned. Additonally, code examples will be included with information on how to use this model.
## Description
This model was trained on 4 novels written by J.R.R. Tolkien that were accessed via open source from the internet and through (https://www.kaggle.com/), which is an open source hub for datasets and data science projects.
The style is that of J.R.R. Tolkien, which is fantasy-esque with vivid and complex descriptions as well as being poetic and medieval.
## Downstream Uses
This model can be used for fans of Tolkien's work for entertainment purposes.
## Recommended Usage
The recommended usage of this model is with Kobold AI Colab.
Click one of the links below and where you are prompted to select a **Model:** there will be a drop down menu. Type "ShaneEP77/tolkientexts" into that drop down menu and select that model. A clickable link will load for you to click on, and from there you can either enter text right away, or you can toggle to "New Game/Story" and the options "Blank Game/Story" and "Random Game/Story" are available.
Links to the GPU and TPU version can be found below:
1. **GPU**: https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb
2. **TPU**: https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb
## Example Code
```
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('ShaneEP77/tolkientexts')
tokenizer = AutoTokenizer.from_pretrained('ShaneEP77/tolkientexts')
prompt = '''In the deep twilight of the Shire, beneath a sky adorned with a tapestry of shimmering stars, Bilbo Baggins embarked on a journey with an old friend, Gandalf.'''
input_ids = tokenizer.encode(prompt, return_tensors='pt')
ouput = model.generate(input_ids, do_sample = True, temperature = 0.8, top_p=0.85, top_k = 50, typical_p = 0.9, repition_penalty=1.5, max_length=len(input_ids[0])+100, pad_token_id=tokenizer.eos_token_id)
generated_text = tokenizer.decode(output[0])
print(generated_text)
```
## tolkientexts
This model is a fine-tuned version of **EleutherAI/pythia-2.8b-deduped** (https://huggingface.co/EleutherAI/pythia-2.8b-deduped) on **CoreWeave's** infrastructure (https://www.coreweave.com/).
**The books that the model was trained on include the following novels all written by J.R.R. Tolkien, which made up 1.48MiB of text:**
* "The Hobbit"
* "The Lord of the Rings: The Fellowship of the Ring"
* "The Lord of the Rings: The Two Towers"
* "The Lord of the Rings: The Return of the King"
**Epochs:** 1
**Steps:** 500
## loss and accuracy
Runs of the model were logged with Weights and Biases (https://wandb.ai/site). Charts were created based on 10-20 runs of the model and show a downward trend for loss as the number of steps increase. On the other hand, there appears to be an upward trend for accuracy as the number of steps increases.


## Meet the Team and Acknowledgements!
* Shane Epstein-Petrullo - Author
* CoreWeave- Computation Materials
*A huge thanks goes out to Wes Brown, David Finster, and Rex Wang for help with this project!*
*Referencing CoreWeave's tutorial and finetuner doc was pivotal to this project. This document can be found at (https://docs.coreweave.com/~/changes/UdikeGislByaE9hH8a7T/machine-learning-and-ai/training/fine-tuning/finetuning-machine-learning-models).*
|
sertemo/bert-finetuned-ner
|
sertemo
| 2023-06-21T20:37:43Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-21T20:11:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9350520575111552
- name: Recall
type: recall
value: 0.9522046449007069
- name: F1
type: f1
value: 0.9435504044025682
- name: Accuracy
type: accuracy
value: 0.9867840113027609
---
<!-- 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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0606
- Precision: 0.9351
- Recall: 0.9522
- F1: 0.9436
- Accuracy: 0.9868
## 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: 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0874 | 1.0 | 1756 | 0.0674 | 0.9167 | 0.9313 | 0.9240 | 0.9818 |
| 0.0352 | 2.0 | 3512 | 0.0628 | 0.9230 | 0.9446 | 0.9337 | 0.9855 |
| 0.0175 | 3.0 | 5268 | 0.0606 | 0.9351 | 0.9522 | 0.9436 | 0.9868 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
antphb/DS-Chatbox-facebook-xglm-564M-V4-FT
|
antphb
| 2023-06-21T20:36:48Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xglm",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T18:17:03Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: DS-Chatbox-facebook-xglm-564M-V4-FT
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. -->
# DS-Chatbox-facebook-xglm-564M-V4-FT
This model is a fine-tuned version of [antphb/DS-Chatbox-facebook-xglm-564M-V3](https://huggingface.co/antphb/DS-Chatbox-facebook-xglm-564M-V3) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3576
- eval_runtime: 5.133
- eval_samples_per_second: 51.822
- eval_steps_per_second: 25.911
- epoch: 12.65
- step: 5200
## 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: 1.5e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 15
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
|
agshruti/distilbert-base-uncased-finetuned-imdb-r3
|
agshruti
| 2023-06-21T20:35:41Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-21T20:33:00Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: agshruti/distilbert-base-uncased-finetuned-imdb-r3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# agshruti/distilbert-base-uncased-finetuned-imdb-r3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.2879
- Validation Loss: 2.9902
- Epoch: 0
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -997, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.2879 | 2.9902 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.13.0
- Tokenizers 0.13.3
|
henri28/final_tcc_model
|
henri28
| 2023-06-21T20:32:39Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-21T16:42:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- sacrebleu
model-index:
- name: final_tcc_model
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. -->
# final_tcc_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7783
- Sacrebleu: 7.6467
- Gen Len: 17.9035
## 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: 16
- eval_batch_size: 16
- 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 | Sacrebleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| No log | 1.0 | 275 | 0.8201 | 7.1607 | 17.8917 |
| 0.9564 | 2.0 | 550 | 0.7971 | 7.3848 | 17.9008 |
| 0.9564 | 3.0 | 825 | 0.7862 | 7.5097 | 17.909 |
| 0.8977 | 4.0 | 1100 | 0.7803 | 7.5882 | 17.9035 |
| 0.8977 | 5.0 | 1375 | 0.7783 | 7.6467 | 17.9035 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cpu
- Datasets 2.13.0
- Tokenizers 0.13.3
|
DigKingy/ToonYou-JP-Alpha1
|
DigKingy
| 2023-06-21T20:26:32Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T20:26:32Z |
---
license: creativeml-openrail-m
---
|
magnustragardh/distilhubert-finetuned-gtzan
|
magnustragardh
| 2023-06-21T20:16:30Z | 160 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-06-21T18:53:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7058
- Accuracy: 0.79
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7675 | 1.0 | 112 | 1.8184 | 0.42 |
| 1.2504 | 2.0 | 225 | 1.3015 | 0.62 |
| 1.0353 | 3.0 | 337 | 0.9890 | 0.72 |
| 0.8318 | 4.0 | 450 | 0.8237 | 0.8 |
| 0.4429 | 5.0 | 562 | 0.8123 | 0.78 |
| 0.4286 | 6.0 | 675 | 0.6820 | 0.8 |
| 0.2553 | 7.0 | 787 | 0.7826 | 0.78 |
| 0.3022 | 8.0 | 900 | 0.6811 | 0.77 |
| 0.1889 | 9.0 | 1012 | 0.6761 | 0.8 |
| 0.1073 | 9.96 | 1120 | 0.7058 | 0.79 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
newsrx/instructor-large-newsrx
|
newsrx
| 2023-06-21T20:05:33Z | 7 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"t5",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"prompt-retrieval",
"text-reranking",
"feature-extraction",
"sentence-similarity",
"transformers",
"English",
"Sentence Similarity",
"natural_questions",
"ms_marco",
"fever",
"hotpot_qa",
"mteb",
"en",
"arxiv:2212.09741",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
sentence-similarity
| 2023-06-21T20:05:33Z |
---
pipeline_tag: sentence-similarity
tags:
- text-embedding
- embeddings
- information-retrieval
- beir
- text-classification
- language-model
- text-clustering
- text-semantic-similarity
- text-evaluation
- prompt-retrieval
- text-reranking
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- t5
- English
- Sentence Similarity
- natural_questions
- ms_marco
- fever
- hotpot_qa
- mteb
language: en
inference: false
license: apache-2.0
model-index:
- name: INSTRUCTOR
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 88.13432835820896
- type: ap
value: 59.298209334395665
- type: f1
value: 83.31769058643586
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.526375
- type: ap
value: 88.16327709705504
- type: f1
value: 91.51095801287843
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.856
- type: f1
value: 45.41490917650942
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.223
- type: map_at_10
value: 47.947
- type: map_at_100
value: 48.742000000000004
- type: map_at_1000
value: 48.745
- type: map_at_3
value: 43.137
- type: map_at_5
value: 45.992
- type: mrr_at_1
value: 32.432
- type: mrr_at_10
value: 48.4
- type: mrr_at_100
value: 49.202
- type: mrr_at_1000
value: 49.205
- type: mrr_at_3
value: 43.551
- type: mrr_at_5
value: 46.467999999999996
- type: ndcg_at_1
value: 31.223
- type: ndcg_at_10
value: 57.045
- type: ndcg_at_100
value: 60.175
- type: ndcg_at_1000
value: 60.233000000000004
- type: ndcg_at_3
value: 47.171
- type: ndcg_at_5
value: 52.322
- type: precision_at_1
value: 31.223
- type: precision_at_10
value: 8.599
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.63
- type: precision_at_5
value: 14.282
- type: recall_at_1
value: 31.223
- type: recall_at_10
value: 85.989
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 58.89
- type: recall_at_5
value: 71.408
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 43.1621946393635
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 32.56417132407894
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.29539304390207
- type: mrr
value: 76.44484017060196
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 84.38746499431112
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 78.51298701298701
- type: f1
value: 77.49041754069235
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.61848554098577
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.32623280148178
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.803000000000004
- type: map_at_10
value: 48.848
- type: map_at_100
value: 50.5
- type: map_at_1000
value: 50.602999999999994
- type: map_at_3
value: 45.111000000000004
- type: map_at_5
value: 47.202
- type: mrr_at_1
value: 44.635000000000005
- type: mrr_at_10
value: 55.593
- type: mrr_at_100
value: 56.169999999999995
- type: mrr_at_1000
value: 56.19499999999999
- type: mrr_at_3
value: 53.361999999999995
- type: mrr_at_5
value: 54.806999999999995
- type: ndcg_at_1
value: 44.635000000000005
- type: ndcg_at_10
value: 55.899
- type: ndcg_at_100
value: 60.958
- type: ndcg_at_1000
value: 62.302
- type: ndcg_at_3
value: 51.051
- type: ndcg_at_5
value: 53.351000000000006
- type: precision_at_1
value: 44.635000000000005
- type: precision_at_10
value: 10.786999999999999
- type: precision_at_100
value: 1.6580000000000001
- type: precision_at_1000
value: 0.213
- type: precision_at_3
value: 24.893
- type: precision_at_5
value: 17.740000000000002
- type: recall_at_1
value: 35.803000000000004
- type: recall_at_10
value: 68.657
- type: recall_at_100
value: 89.77199999999999
- type: recall_at_1000
value: 97.67
- type: recall_at_3
value: 54.066
- type: recall_at_5
value: 60.788
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.706
- type: map_at_10
value: 44.896
- type: map_at_100
value: 46.299
- type: map_at_1000
value: 46.44
- type: map_at_3
value: 41.721000000000004
- type: map_at_5
value: 43.486000000000004
- type: mrr_at_1
value: 41.592
- type: mrr_at_10
value: 50.529
- type: mrr_at_100
value: 51.22
- type: mrr_at_1000
value: 51.258
- type: mrr_at_3
value: 48.205999999999996
- type: mrr_at_5
value: 49.528
- type: ndcg_at_1
value: 41.592
- type: ndcg_at_10
value: 50.77199999999999
- type: ndcg_at_100
value: 55.383
- type: ndcg_at_1000
value: 57.288
- type: ndcg_at_3
value: 46.324
- type: ndcg_at_5
value: 48.346000000000004
- type: precision_at_1
value: 41.592
- type: precision_at_10
value: 9.516
- type: precision_at_100
value: 1.541
- type: precision_at_1000
value: 0.2
- type: precision_at_3
value: 22.399
- type: precision_at_5
value: 15.770999999999999
- type: recall_at_1
value: 33.706
- type: recall_at_10
value: 61.353
- type: recall_at_100
value: 80.182
- type: recall_at_1000
value: 91.896
- type: recall_at_3
value: 48.204
- type: recall_at_5
value: 53.89699999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 44.424
- type: map_at_10
value: 57.169000000000004
- type: map_at_100
value: 58.202
- type: map_at_1000
value: 58.242000000000004
- type: map_at_3
value: 53.825
- type: map_at_5
value: 55.714
- type: mrr_at_1
value: 50.470000000000006
- type: mrr_at_10
value: 60.489000000000004
- type: mrr_at_100
value: 61.096
- type: mrr_at_1000
value: 61.112
- type: mrr_at_3
value: 58.192
- type: mrr_at_5
value: 59.611999999999995
- type: ndcg_at_1
value: 50.470000000000006
- type: ndcg_at_10
value: 63.071999999999996
- type: ndcg_at_100
value: 66.964
- type: ndcg_at_1000
value: 67.659
- type: ndcg_at_3
value: 57.74399999999999
- type: ndcg_at_5
value: 60.367000000000004
- type: precision_at_1
value: 50.470000000000006
- type: precision_at_10
value: 10.019
- type: precision_at_100
value: 1.29
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 25.558999999999997
- type: precision_at_5
value: 17.467
- type: recall_at_1
value: 44.424
- type: recall_at_10
value: 77.02
- type: recall_at_100
value: 93.738
- type: recall_at_1000
value: 98.451
- type: recall_at_3
value: 62.888
- type: recall_at_5
value: 69.138
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.294
- type: map_at_10
value: 34.503
- type: map_at_100
value: 35.641
- type: map_at_1000
value: 35.724000000000004
- type: map_at_3
value: 31.753999999999998
- type: map_at_5
value: 33.190999999999995
- type: mrr_at_1
value: 28.362
- type: mrr_at_10
value: 36.53
- type: mrr_at_100
value: 37.541000000000004
- type: mrr_at_1000
value: 37.602000000000004
- type: mrr_at_3
value: 33.917
- type: mrr_at_5
value: 35.358000000000004
- type: ndcg_at_1
value: 28.362
- type: ndcg_at_10
value: 39.513999999999996
- type: ndcg_at_100
value: 44.815
- type: ndcg_at_1000
value: 46.839
- type: ndcg_at_3
value: 34.02
- type: ndcg_at_5
value: 36.522
- type: precision_at_1
value: 28.362
- type: precision_at_10
value: 6.101999999999999
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 14.161999999999999
- type: precision_at_5
value: 9.966
- type: recall_at_1
value: 26.294
- type: recall_at_10
value: 53.098
- type: recall_at_100
value: 76.877
- type: recall_at_1000
value: 91.834
- type: recall_at_3
value: 38.266
- type: recall_at_5
value: 44.287
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.407
- type: map_at_10
value: 25.185999999999996
- type: map_at_100
value: 26.533
- type: map_at_1000
value: 26.657999999999998
- type: map_at_3
value: 22.201999999999998
- type: map_at_5
value: 23.923
- type: mrr_at_1
value: 20.522000000000002
- type: mrr_at_10
value: 29.522
- type: mrr_at_100
value: 30.644
- type: mrr_at_1000
value: 30.713
- type: mrr_at_3
value: 26.679000000000002
- type: mrr_at_5
value: 28.483000000000004
- type: ndcg_at_1
value: 20.522000000000002
- type: ndcg_at_10
value: 30.656
- type: ndcg_at_100
value: 36.864999999999995
- type: ndcg_at_1000
value: 39.675
- type: ndcg_at_3
value: 25.319000000000003
- type: ndcg_at_5
value: 27.992
- type: precision_at_1
value: 20.522000000000002
- type: precision_at_10
value: 5.795999999999999
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 12.396
- type: precision_at_5
value: 9.328
- type: recall_at_1
value: 16.407
- type: recall_at_10
value: 43.164
- type: recall_at_100
value: 69.695
- type: recall_at_1000
value: 89.41900000000001
- type: recall_at_3
value: 28.634999999999998
- type: recall_at_5
value: 35.308
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.473
- type: map_at_10
value: 41.676
- type: map_at_100
value: 43.120999999999995
- type: map_at_1000
value: 43.230000000000004
- type: map_at_3
value: 38.306000000000004
- type: map_at_5
value: 40.355999999999995
- type: mrr_at_1
value: 37.536
- type: mrr_at_10
value: 47.643
- type: mrr_at_100
value: 48.508
- type: mrr_at_1000
value: 48.551
- type: mrr_at_3
value: 45.348
- type: mrr_at_5
value: 46.744
- type: ndcg_at_1
value: 37.536
- type: ndcg_at_10
value: 47.823
- type: ndcg_at_100
value: 53.395
- type: ndcg_at_1000
value: 55.271
- type: ndcg_at_3
value: 42.768
- type: ndcg_at_5
value: 45.373000000000005
- type: precision_at_1
value: 37.536
- type: precision_at_10
value: 8.681
- type: precision_at_100
value: 1.34
- type: precision_at_1000
value: 0.165
- type: precision_at_3
value: 20.468
- type: precision_at_5
value: 14.495
- type: recall_at_1
value: 30.473
- type: recall_at_10
value: 60.092999999999996
- type: recall_at_100
value: 82.733
- type: recall_at_1000
value: 94.875
- type: recall_at_3
value: 45.734
- type: recall_at_5
value: 52.691
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.976000000000003
- type: map_at_10
value: 41.097
- type: map_at_100
value: 42.547000000000004
- type: map_at_1000
value: 42.659000000000006
- type: map_at_3
value: 37.251
- type: map_at_5
value: 39.493
- type: mrr_at_1
value: 37.557
- type: mrr_at_10
value: 46.605000000000004
- type: mrr_at_100
value: 47.487
- type: mrr_at_1000
value: 47.54
- type: mrr_at_3
value: 43.721
- type: mrr_at_5
value: 45.411
- type: ndcg_at_1
value: 37.557
- type: ndcg_at_10
value: 47.449000000000005
- type: ndcg_at_100
value: 53.052
- type: ndcg_at_1000
value: 55.010999999999996
- type: ndcg_at_3
value: 41.439
- type: ndcg_at_5
value: 44.292
- type: precision_at_1
value: 37.557
- type: precision_at_10
value: 8.847
- type: precision_at_100
value: 1.357
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 20.091
- type: precision_at_5
value: 14.384
- type: recall_at_1
value: 29.976000000000003
- type: recall_at_10
value: 60.99099999999999
- type: recall_at_100
value: 84.245
- type: recall_at_1000
value: 96.97200000000001
- type: recall_at_3
value: 43.794
- type: recall_at_5
value: 51.778999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.099166666666665
- type: map_at_10
value: 38.1365
- type: map_at_100
value: 39.44491666666667
- type: map_at_1000
value: 39.55858333333334
- type: map_at_3
value: 35.03641666666666
- type: map_at_5
value: 36.79833333333334
- type: mrr_at_1
value: 33.39966666666667
- type: mrr_at_10
value: 42.42583333333333
- type: mrr_at_100
value: 43.28575
- type: mrr_at_1000
value: 43.33741666666667
- type: mrr_at_3
value: 39.94975
- type: mrr_at_5
value: 41.41633333333334
- type: ndcg_at_1
value: 33.39966666666667
- type: ndcg_at_10
value: 43.81741666666667
- type: ndcg_at_100
value: 49.08166666666667
- type: ndcg_at_1000
value: 51.121166666666674
- type: ndcg_at_3
value: 38.73575
- type: ndcg_at_5
value: 41.18158333333333
- type: precision_at_1
value: 33.39966666666667
- type: precision_at_10
value: 7.738916666666667
- type: precision_at_100
value: 1.2265833333333331
- type: precision_at_1000
value: 0.15983333333333336
- type: precision_at_3
value: 17.967416666666665
- type: precision_at_5
value: 12.78675
- type: recall_at_1
value: 28.099166666666665
- type: recall_at_10
value: 56.27049999999999
- type: recall_at_100
value: 78.93291666666667
- type: recall_at_1000
value: 92.81608333333334
- type: recall_at_3
value: 42.09775
- type: recall_at_5
value: 48.42533333333334
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.663
- type: map_at_10
value: 30.377
- type: map_at_100
value: 31.426
- type: map_at_1000
value: 31.519000000000002
- type: map_at_3
value: 28.069
- type: map_at_5
value: 29.256999999999998
- type: mrr_at_1
value: 26.687
- type: mrr_at_10
value: 33.107
- type: mrr_at_100
value: 34.055
- type: mrr_at_1000
value: 34.117999999999995
- type: mrr_at_3
value: 31.058000000000003
- type: mrr_at_5
value: 32.14
- type: ndcg_at_1
value: 26.687
- type: ndcg_at_10
value: 34.615
- type: ndcg_at_100
value: 39.776
- type: ndcg_at_1000
value: 42.05
- type: ndcg_at_3
value: 30.322
- type: ndcg_at_5
value: 32.157000000000004
- type: precision_at_1
value: 26.687
- type: precision_at_10
value: 5.491
- type: precision_at_100
value: 0.877
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 13.139000000000001
- type: precision_at_5
value: 9.049
- type: recall_at_1
value: 23.663
- type: recall_at_10
value: 45.035
- type: recall_at_100
value: 68.554
- type: recall_at_1000
value: 85.077
- type: recall_at_3
value: 32.982
- type: recall_at_5
value: 37.688
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.403
- type: map_at_10
value: 25.197000000000003
- type: map_at_100
value: 26.355
- type: map_at_1000
value: 26.487
- type: map_at_3
value: 22.733
- type: map_at_5
value: 24.114
- type: mrr_at_1
value: 21.37
- type: mrr_at_10
value: 29.091
- type: mrr_at_100
value: 30.018
- type: mrr_at_1000
value: 30.096
- type: mrr_at_3
value: 26.887
- type: mrr_at_5
value: 28.157
- type: ndcg_at_1
value: 21.37
- type: ndcg_at_10
value: 30.026000000000003
- type: ndcg_at_100
value: 35.416
- type: ndcg_at_1000
value: 38.45
- type: ndcg_at_3
value: 25.764
- type: ndcg_at_5
value: 27.742
- type: precision_at_1
value: 21.37
- type: precision_at_10
value: 5.609
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 12.423
- type: precision_at_5
value: 9.009
- type: recall_at_1
value: 17.403
- type: recall_at_10
value: 40.573
- type: recall_at_100
value: 64.818
- type: recall_at_1000
value: 86.53699999999999
- type: recall_at_3
value: 28.493000000000002
- type: recall_at_5
value: 33.660000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.639
- type: map_at_10
value: 38.951
- type: map_at_100
value: 40.238
- type: map_at_1000
value: 40.327
- type: map_at_3
value: 35.842
- type: map_at_5
value: 37.617
- type: mrr_at_1
value: 33.769
- type: mrr_at_10
value: 43.088
- type: mrr_at_100
value: 44.03
- type: mrr_at_1000
value: 44.072
- type: mrr_at_3
value: 40.656
- type: mrr_at_5
value: 42.138999999999996
- type: ndcg_at_1
value: 33.769
- type: ndcg_at_10
value: 44.676
- type: ndcg_at_100
value: 50.416000000000004
- type: ndcg_at_1000
value: 52.227999999999994
- type: ndcg_at_3
value: 39.494
- type: ndcg_at_5
value: 42.013
- type: precision_at_1
value: 33.769
- type: precision_at_10
value: 7.668
- type: precision_at_100
value: 1.18
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 18.221
- type: precision_at_5
value: 12.966
- type: recall_at_1
value: 28.639
- type: recall_at_10
value: 57.687999999999995
- type: recall_at_100
value: 82.541
- type: recall_at_1000
value: 94.896
- type: recall_at_3
value: 43.651
- type: recall_at_5
value: 49.925999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.57
- type: map_at_10
value: 40.004
- type: map_at_100
value: 41.75
- type: map_at_1000
value: 41.97
- type: map_at_3
value: 36.788
- type: map_at_5
value: 38.671
- type: mrr_at_1
value: 35.375
- type: mrr_at_10
value: 45.121
- type: mrr_at_100
value: 45.994
- type: mrr_at_1000
value: 46.04
- type: mrr_at_3
value: 42.227
- type: mrr_at_5
value: 43.995
- type: ndcg_at_1
value: 35.375
- type: ndcg_at_10
value: 46.392
- type: ndcg_at_100
value: 52.196
- type: ndcg_at_1000
value: 54.274
- type: ndcg_at_3
value: 41.163
- type: ndcg_at_5
value: 43.813
- type: precision_at_1
value: 35.375
- type: precision_at_10
value: 8.676
- type: precision_at_100
value: 1.678
- type: precision_at_1000
value: 0.253
- type: precision_at_3
value: 19.104
- type: precision_at_5
value: 13.913
- type: recall_at_1
value: 29.57
- type: recall_at_10
value: 58.779
- type: recall_at_100
value: 83.337
- type: recall_at_1000
value: 95.979
- type: recall_at_3
value: 44.005
- type: recall_at_5
value: 50.975
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.832
- type: map_at_10
value: 29.733999999999998
- type: map_at_100
value: 30.727
- type: map_at_1000
value: 30.843999999999998
- type: map_at_3
value: 26.834999999999997
- type: map_at_5
value: 28.555999999999997
- type: mrr_at_1
value: 22.921
- type: mrr_at_10
value: 31.791999999999998
- type: mrr_at_100
value: 32.666000000000004
- type: mrr_at_1000
value: 32.751999999999995
- type: mrr_at_3
value: 29.144
- type: mrr_at_5
value: 30.622
- type: ndcg_at_1
value: 22.921
- type: ndcg_at_10
value: 34.915
- type: ndcg_at_100
value: 39.744
- type: ndcg_at_1000
value: 42.407000000000004
- type: ndcg_at_3
value: 29.421000000000003
- type: ndcg_at_5
value: 32.211
- type: precision_at_1
value: 22.921
- type: precision_at_10
value: 5.675
- type: precision_at_100
value: 0.872
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 12.753999999999998
- type: precision_at_5
value: 9.353
- type: recall_at_1
value: 20.832
- type: recall_at_10
value: 48.795
- type: recall_at_100
value: 70.703
- type: recall_at_1000
value: 90.187
- type: recall_at_3
value: 34.455000000000005
- type: recall_at_5
value: 40.967
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.334
- type: map_at_10
value: 19.009999999999998
- type: map_at_100
value: 21.129
- type: map_at_1000
value: 21.328
- type: map_at_3
value: 15.152
- type: map_at_5
value: 17.084
- type: mrr_at_1
value: 23.453
- type: mrr_at_10
value: 36.099
- type: mrr_at_100
value: 37.069
- type: mrr_at_1000
value: 37.104
- type: mrr_at_3
value: 32.096000000000004
- type: mrr_at_5
value: 34.451
- type: ndcg_at_1
value: 23.453
- type: ndcg_at_10
value: 27.739000000000004
- type: ndcg_at_100
value: 35.836
- type: ndcg_at_1000
value: 39.242
- type: ndcg_at_3
value: 21.263
- type: ndcg_at_5
value: 23.677
- type: precision_at_1
value: 23.453
- type: precision_at_10
value: 9.199
- type: precision_at_100
value: 1.791
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 16.2
- type: precision_at_5
value: 13.147
- type: recall_at_1
value: 10.334
- type: recall_at_10
value: 35.177
- type: recall_at_100
value: 63.009
- type: recall_at_1000
value: 81.938
- type: recall_at_3
value: 19.914
- type: recall_at_5
value: 26.077
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.212
- type: map_at_10
value: 17.386
- type: map_at_100
value: 24.234
- type: map_at_1000
value: 25.724999999999998
- type: map_at_3
value: 12.727
- type: map_at_5
value: 14.785
- type: mrr_at_1
value: 59.25
- type: mrr_at_10
value: 68.687
- type: mrr_at_100
value: 69.133
- type: mrr_at_1000
value: 69.14099999999999
- type: mrr_at_3
value: 66.917
- type: mrr_at_5
value: 67.742
- type: ndcg_at_1
value: 48.625
- type: ndcg_at_10
value: 36.675999999999995
- type: ndcg_at_100
value: 41.543
- type: ndcg_at_1000
value: 49.241
- type: ndcg_at_3
value: 41.373
- type: ndcg_at_5
value: 38.707
- type: precision_at_1
value: 59.25
- type: precision_at_10
value: 28.525
- type: precision_at_100
value: 9.027000000000001
- type: precision_at_1000
value: 1.8339999999999999
- type: precision_at_3
value: 44.833
- type: precision_at_5
value: 37.35
- type: recall_at_1
value: 8.212
- type: recall_at_10
value: 23.188
- type: recall_at_100
value: 48.613
- type: recall_at_1000
value: 73.093
- type: recall_at_3
value: 14.419
- type: recall_at_5
value: 17.798
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.725
- type: f1
value: 46.50743309855908
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.086
- type: map_at_10
value: 66.914
- type: map_at_100
value: 67.321
- type: map_at_1000
value: 67.341
- type: map_at_3
value: 64.75800000000001
- type: map_at_5
value: 66.189
- type: mrr_at_1
value: 59.28600000000001
- type: mrr_at_10
value: 71.005
- type: mrr_at_100
value: 71.304
- type: mrr_at_1000
value: 71.313
- type: mrr_at_3
value: 69.037
- type: mrr_at_5
value: 70.35
- type: ndcg_at_1
value: 59.28600000000001
- type: ndcg_at_10
value: 72.695
- type: ndcg_at_100
value: 74.432
- type: ndcg_at_1000
value: 74.868
- type: ndcg_at_3
value: 68.72200000000001
- type: ndcg_at_5
value: 71.081
- type: precision_at_1
value: 59.28600000000001
- type: precision_at_10
value: 9.499
- type: precision_at_100
value: 1.052
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 27.503
- type: precision_at_5
value: 17.854999999999997
- type: recall_at_1
value: 55.086
- type: recall_at_10
value: 86.453
- type: recall_at_100
value: 94.028
- type: recall_at_1000
value: 97.052
- type: recall_at_3
value: 75.821
- type: recall_at_5
value: 81.6
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.262999999999998
- type: map_at_10
value: 37.488
- type: map_at_100
value: 39.498
- type: map_at_1000
value: 39.687
- type: map_at_3
value: 32.529
- type: map_at_5
value: 35.455
- type: mrr_at_1
value: 44.907000000000004
- type: mrr_at_10
value: 53.239000000000004
- type: mrr_at_100
value: 54.086
- type: mrr_at_1000
value: 54.122
- type: mrr_at_3
value: 51.235
- type: mrr_at_5
value: 52.415
- type: ndcg_at_1
value: 44.907000000000004
- type: ndcg_at_10
value: 45.446
- type: ndcg_at_100
value: 52.429
- type: ndcg_at_1000
value: 55.169000000000004
- type: ndcg_at_3
value: 41.882000000000005
- type: ndcg_at_5
value: 43.178
- type: precision_at_1
value: 44.907000000000004
- type: precision_at_10
value: 12.931999999999999
- type: precision_at_100
value: 2.025
- type: precision_at_1000
value: 0.248
- type: precision_at_3
value: 28.652
- type: precision_at_5
value: 21.204
- type: recall_at_1
value: 22.262999999999998
- type: recall_at_10
value: 52.447
- type: recall_at_100
value: 78.045
- type: recall_at_1000
value: 94.419
- type: recall_at_3
value: 38.064
- type: recall_at_5
value: 44.769
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.519
- type: map_at_10
value: 45.831
- type: map_at_100
value: 46.815
- type: map_at_1000
value: 46.899
- type: map_at_3
value: 42.836
- type: map_at_5
value: 44.65
- type: mrr_at_1
value: 65.037
- type: mrr_at_10
value: 72.16
- type: mrr_at_100
value: 72.51100000000001
- type: mrr_at_1000
value: 72.53
- type: mrr_at_3
value: 70.682
- type: mrr_at_5
value: 71.54599999999999
- type: ndcg_at_1
value: 65.037
- type: ndcg_at_10
value: 55.17999999999999
- type: ndcg_at_100
value: 58.888
- type: ndcg_at_1000
value: 60.648
- type: ndcg_at_3
value: 50.501
- type: ndcg_at_5
value: 52.977
- type: precision_at_1
value: 65.037
- type: precision_at_10
value: 11.530999999999999
- type: precision_at_100
value: 1.4460000000000002
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 31.483
- type: precision_at_5
value: 20.845
- type: recall_at_1
value: 32.519
- type: recall_at_10
value: 57.657000000000004
- type: recall_at_100
value: 72.30199999999999
- type: recall_at_1000
value: 84.024
- type: recall_at_3
value: 47.225
- type: recall_at_5
value: 52.113
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 88.3168
- type: ap
value: 83.80165516037135
- type: f1
value: 88.29942471066407
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 20.724999999999998
- type: map_at_10
value: 32.736
- type: map_at_100
value: 33.938
- type: map_at_1000
value: 33.991
- type: map_at_3
value: 28.788000000000004
- type: map_at_5
value: 31.016
- type: mrr_at_1
value: 21.361
- type: mrr_at_10
value: 33.323
- type: mrr_at_100
value: 34.471000000000004
- type: mrr_at_1000
value: 34.518
- type: mrr_at_3
value: 29.453000000000003
- type: mrr_at_5
value: 31.629
- type: ndcg_at_1
value: 21.361
- type: ndcg_at_10
value: 39.649
- type: ndcg_at_100
value: 45.481
- type: ndcg_at_1000
value: 46.775
- type: ndcg_at_3
value: 31.594
- type: ndcg_at_5
value: 35.543
- type: precision_at_1
value: 21.361
- type: precision_at_10
value: 6.3740000000000006
- type: precision_at_100
value: 0.931
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.514999999999999
- type: precision_at_5
value: 10.100000000000001
- type: recall_at_1
value: 20.724999999999998
- type: recall_at_10
value: 61.034
- type: recall_at_100
value: 88.062
- type: recall_at_1000
value: 97.86399999999999
- type: recall_at_3
value: 39.072
- type: recall_at_5
value: 48.53
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.8919288645691
- type: f1
value: 93.57059586398059
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.97993616051072
- type: f1
value: 48.244319183606535
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.90047074646941
- type: f1
value: 66.48999056063725
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.34566240753195
- type: f1
value: 73.54164154290658
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.21866934757011
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.000936217235534
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.68189362520352
- type: mrr
value: 32.69603637784303
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.078
- type: map_at_10
value: 12.671
- type: map_at_100
value: 16.291
- type: map_at_1000
value: 17.855999999999998
- type: map_at_3
value: 9.610000000000001
- type: map_at_5
value: 11.152
- type: mrr_at_1
value: 43.963
- type: mrr_at_10
value: 53.173
- type: mrr_at_100
value: 53.718999999999994
- type: mrr_at_1000
value: 53.756
- type: mrr_at_3
value: 50.980000000000004
- type: mrr_at_5
value: 52.42
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 34.086
- type: ndcg_at_100
value: 32.545
- type: ndcg_at_1000
value: 41.144999999999996
- type: ndcg_at_3
value: 39.434999999999995
- type: ndcg_at_5
value: 37.888
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 25.014999999999997
- type: precision_at_100
value: 8.594
- type: precision_at_1000
value: 2.169
- type: precision_at_3
value: 37.049
- type: precision_at_5
value: 33.065
- type: recall_at_1
value: 6.078
- type: recall_at_10
value: 16.17
- type: recall_at_100
value: 34.512
- type: recall_at_1000
value: 65.447
- type: recall_at_3
value: 10.706
- type: recall_at_5
value: 13.158
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.378000000000004
- type: map_at_10
value: 42.178
- type: map_at_100
value: 43.32
- type: map_at_1000
value: 43.358000000000004
- type: map_at_3
value: 37.474000000000004
- type: map_at_5
value: 40.333000000000006
- type: mrr_at_1
value: 30.823
- type: mrr_at_10
value: 44.626
- type: mrr_at_100
value: 45.494
- type: mrr_at_1000
value: 45.519
- type: mrr_at_3
value: 40.585
- type: mrr_at_5
value: 43.146
- type: ndcg_at_1
value: 30.794
- type: ndcg_at_10
value: 50.099000000000004
- type: ndcg_at_100
value: 54.900999999999996
- type: ndcg_at_1000
value: 55.69499999999999
- type: ndcg_at_3
value: 41.238
- type: ndcg_at_5
value: 46.081
- type: precision_at_1
value: 30.794
- type: precision_at_10
value: 8.549
- type: precision_at_100
value: 1.124
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 18.926000000000002
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 27.378000000000004
- type: recall_at_10
value: 71.842
- type: recall_at_100
value: 92.565
- type: recall_at_1000
value: 98.402
- type: recall_at_3
value: 49.053999999999995
- type: recall_at_5
value: 60.207
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.557
- type: map_at_10
value: 84.729
- type: map_at_100
value: 85.369
- type: map_at_1000
value: 85.382
- type: map_at_3
value: 81.72
- type: map_at_5
value: 83.613
- type: mrr_at_1
value: 81.3
- type: mrr_at_10
value: 87.488
- type: mrr_at_100
value: 87.588
- type: mrr_at_1000
value: 87.589
- type: mrr_at_3
value: 86.53
- type: mrr_at_5
value: 87.18599999999999
- type: ndcg_at_1
value: 81.28999999999999
- type: ndcg_at_10
value: 88.442
- type: ndcg_at_100
value: 89.637
- type: ndcg_at_1000
value: 89.70700000000001
- type: ndcg_at_3
value: 85.55199999999999
- type: ndcg_at_5
value: 87.154
- type: precision_at_1
value: 81.28999999999999
- type: precision_at_10
value: 13.489999999999998
- type: precision_at_100
value: 1.54
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.553
- type: precision_at_5
value: 24.708
- type: recall_at_1
value: 70.557
- type: recall_at_10
value: 95.645
- type: recall_at_100
value: 99.693
- type: recall_at_1000
value: 99.995
- type: recall_at_3
value: 87.359
- type: recall_at_5
value: 91.89699999999999
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 63.65060114776209
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.63271250680617
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.263
- type: map_at_10
value: 10.801
- type: map_at_100
value: 12.888
- type: map_at_1000
value: 13.224
- type: map_at_3
value: 7.362
- type: map_at_5
value: 9.149000000000001
- type: mrr_at_1
value: 21
- type: mrr_at_10
value: 31.416
- type: mrr_at_100
value: 32.513
- type: mrr_at_1000
value: 32.58
- type: mrr_at_3
value: 28.116999999999997
- type: mrr_at_5
value: 29.976999999999997
- type: ndcg_at_1
value: 21
- type: ndcg_at_10
value: 18.551000000000002
- type: ndcg_at_100
value: 26.657999999999998
- type: ndcg_at_1000
value: 32.485
- type: ndcg_at_3
value: 16.834
- type: ndcg_at_5
value: 15.204999999999998
- type: precision_at_1
value: 21
- type: precision_at_10
value: 9.84
- type: precision_at_100
value: 2.16
- type: precision_at_1000
value: 0.35500000000000004
- type: precision_at_3
value: 15.667
- type: precision_at_5
value: 13.62
- type: recall_at_1
value: 4.263
- type: recall_at_10
value: 19.922
- type: recall_at_100
value: 43.808
- type: recall_at_1000
value: 72.14500000000001
- type: recall_at_3
value: 9.493
- type: recall_at_5
value: 13.767999999999999
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 81.27446313317233
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 76.27963301217527
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 88.18495048450949
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 81.91982338692046
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 89.00896818385291
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 85.48814644586132
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 90.30116926966582
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_spearman
value: 67.74132963032342
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 86.87741355780479
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.0019012295875
- type: mrr
value: 94.70267024188593
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 50.05
- type: map_at_10
value: 59.36
- type: map_at_100
value: 59.967999999999996
- type: map_at_1000
value: 60.023
- type: map_at_3
value: 56.515
- type: map_at_5
value: 58.272999999999996
- type: mrr_at_1
value: 53
- type: mrr_at_10
value: 61.102000000000004
- type: mrr_at_100
value: 61.476
- type: mrr_at_1000
value: 61.523
- type: mrr_at_3
value: 58.778
- type: mrr_at_5
value: 60.128
- type: ndcg_at_1
value: 53
- type: ndcg_at_10
value: 64.43100000000001
- type: ndcg_at_100
value: 66.73599999999999
- type: ndcg_at_1000
value: 68.027
- type: ndcg_at_3
value: 59.279
- type: ndcg_at_5
value: 61.888
- type: precision_at_1
value: 53
- type: precision_at_10
value: 8.767
- type: precision_at_100
value: 1.01
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 23.444000000000003
- type: precision_at_5
value: 15.667
- type: recall_at_1
value: 50.05
- type: recall_at_10
value: 78.511
- type: recall_at_100
value: 88.5
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 64.117
- type: recall_at_5
value: 70.867
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72178217821782
- type: cos_sim_ap
value: 93.0728601593541
- type: cos_sim_f1
value: 85.6727976766699
- type: cos_sim_precision
value: 83.02063789868667
- type: cos_sim_recall
value: 88.5
- type: dot_accuracy
value: 99.72178217821782
- type: dot_ap
value: 93.07287396168348
- type: dot_f1
value: 85.6727976766699
- type: dot_precision
value: 83.02063789868667
- type: dot_recall
value: 88.5
- type: euclidean_accuracy
value: 99.72178217821782
- type: euclidean_ap
value: 93.07285657982895
- type: euclidean_f1
value: 85.6727976766699
- type: euclidean_precision
value: 83.02063789868667
- type: euclidean_recall
value: 88.5
- type: manhattan_accuracy
value: 99.72475247524753
- type: manhattan_ap
value: 93.02792973059809
- type: manhattan_f1
value: 85.7727737973388
- type: manhattan_precision
value: 87.84067085953879
- type: manhattan_recall
value: 83.8
- type: max_accuracy
value: 99.72475247524753
- type: max_ap
value: 93.07287396168348
- type: max_f1
value: 85.7727737973388
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 68.77583615550819
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.151636938606956
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.16607939471187
- type: mrr
value: 52.95172046091163
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.314646669495666
- type: cos_sim_spearman
value: 31.83562491439455
- type: dot_pearson
value: 31.314590842874157
- type: dot_spearman
value: 31.83363065810437
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.198
- type: map_at_10
value: 1.3010000000000002
- type: map_at_100
value: 7.2139999999999995
- type: map_at_1000
value: 20.179
- type: map_at_3
value: 0.528
- type: map_at_5
value: 0.8019999999999999
- type: mrr_at_1
value: 72
- type: mrr_at_10
value: 83.39999999999999
- type: mrr_at_100
value: 83.39999999999999
- type: mrr_at_1000
value: 83.39999999999999
- type: mrr_at_3
value: 81.667
- type: mrr_at_5
value: 83.06700000000001
- type: ndcg_at_1
value: 66
- type: ndcg_at_10
value: 58.059000000000005
- type: ndcg_at_100
value: 44.316
- type: ndcg_at_1000
value: 43.147000000000006
- type: ndcg_at_3
value: 63.815999999999995
- type: ndcg_at_5
value: 63.005
- type: precision_at_1
value: 72
- type: precision_at_10
value: 61.4
- type: precision_at_100
value: 45.62
- type: precision_at_1000
value: 19.866
- type: precision_at_3
value: 70
- type: precision_at_5
value: 68.8
- type: recall_at_1
value: 0.198
- type: recall_at_10
value: 1.517
- type: recall_at_100
value: 10.587
- type: recall_at_1000
value: 41.233
- type: recall_at_3
value: 0.573
- type: recall_at_5
value: 0.907
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.894
- type: map_at_10
value: 8.488999999999999
- type: map_at_100
value: 14.445
- type: map_at_1000
value: 16.078
- type: map_at_3
value: 4.589
- type: map_at_5
value: 6.019
- type: mrr_at_1
value: 22.448999999999998
- type: mrr_at_10
value: 39.82
- type: mrr_at_100
value: 40.752
- type: mrr_at_1000
value: 40.771
- type: mrr_at_3
value: 34.354
- type: mrr_at_5
value: 37.721
- type: ndcg_at_1
value: 19.387999999999998
- type: ndcg_at_10
value: 21.563
- type: ndcg_at_100
value: 33.857
- type: ndcg_at_1000
value: 46.199
- type: ndcg_at_3
value: 22.296
- type: ndcg_at_5
value: 21.770999999999997
- type: precision_at_1
value: 22.448999999999998
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.142999999999999
- type: precision_at_1000
value: 1.541
- type: precision_at_3
value: 24.490000000000002
- type: precision_at_5
value: 22.448999999999998
- type: recall_at_1
value: 1.894
- type: recall_at_10
value: 14.931
- type: recall_at_100
value: 45.524
- type: recall_at_1000
value: 83.243
- type: recall_at_3
value: 5.712
- type: recall_at_5
value: 8.386000000000001
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.049
- type: ap
value: 13.85116971310922
- type: f1
value: 54.37504302487686
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.1312959818902
- type: f1
value: 64.11413877009383
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 54.13103431861502
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.327889372355
- type: cos_sim_ap
value: 77.42059895975699
- type: cos_sim_f1
value: 71.02706903250873
- type: cos_sim_precision
value: 69.75324344950394
- type: cos_sim_recall
value: 72.34828496042216
- type: dot_accuracy
value: 87.327889372355
- type: dot_ap
value: 77.4209479346677
- type: dot_f1
value: 71.02706903250873
- type: dot_precision
value: 69.75324344950394
- type: dot_recall
value: 72.34828496042216
- type: euclidean_accuracy
value: 87.327889372355
- type: euclidean_ap
value: 77.42096495861037
- type: euclidean_f1
value: 71.02706903250873
- type: euclidean_precision
value: 69.75324344950394
- type: euclidean_recall
value: 72.34828496042216
- type: manhattan_accuracy
value: 87.31000774870358
- type: manhattan_ap
value: 77.38930750711619
- type: manhattan_f1
value: 71.07935314027831
- type: manhattan_precision
value: 67.70957726295677
- type: manhattan_recall
value: 74.80211081794195
- type: max_accuracy
value: 87.327889372355
- type: max_ap
value: 77.42096495861037
- type: max_f1
value: 71.07935314027831
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.58939729110878
- type: cos_sim_ap
value: 87.17594155025475
- type: cos_sim_f1
value: 79.21146953405018
- type: cos_sim_precision
value: 76.8918527109307
- type: cos_sim_recall
value: 81.67539267015707
- type: dot_accuracy
value: 89.58939729110878
- type: dot_ap
value: 87.17593963273593
- type: dot_f1
value: 79.21146953405018
- type: dot_precision
value: 76.8918527109307
- type: dot_recall
value: 81.67539267015707
- type: euclidean_accuracy
value: 89.58939729110878
- type: euclidean_ap
value: 87.17592466925834
- type: euclidean_f1
value: 79.21146953405018
- type: euclidean_precision
value: 76.8918527109307
- type: euclidean_recall
value: 81.67539267015707
- type: manhattan_accuracy
value: 89.62626615438352
- type: manhattan_ap
value: 87.16589873161546
- type: manhattan_f1
value: 79.25143598295348
- type: manhattan_precision
value: 76.39494177323712
- type: manhattan_recall
value: 82.32984293193716
- type: max_accuracy
value: 89.62626615438352
- type: max_ap
value: 87.17594155025475
- type: max_f1
value: 79.25143598295348
duplicated_from: hkunlp/instructor-large
---
# hkunlp/instructor-large
We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))!
The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)!
**************************** **Updates** ****************************
* 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance.
* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out!
## Quick start
<hr />
## Installation
```bash
pip install InstructorEmbedding
```
## Compute your customized embeddings
Then you can use the model like this to calculate domain-specific and task-aware embeddings:
```python
from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-large')
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Science title:"
embeddings = model.encode([[instruction,sentence]])
print(embeddings)
```
## Use cases
<hr />
## Calculate embeddings for your customized texts
If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:
Represent the `domain` `text_type` for `task_objective`:
* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
## Calculate Sentence similarities
You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
```python
from sklearn.metrics.pairwise import cosine_similarity
sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'],
['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
embeddings_a = model.encode(sentences_a)
embeddings_b = model.encode(sentences_b)
similarities = cosine_similarity(embeddings_a,embeddings_b)
print(similarities)
```
## Information Retrieval
You can also use **customized embeddings** for information retrieval.
```python
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
query_embeddings = model.encode(query)
corpus_embeddings = model.encode(corpus)
similarities = cosine_similarity(query_embeddings,corpus_embeddings)
retrieved_doc_id = np.argmax(similarities)
print(retrieved_doc_id)
```
## Clustering
Use **customized embeddings** for clustering texts in groups.
```python
import sklearn.cluster
sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']]
embeddings = model.encode(sentences)
clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
clustering_model.fit(embeddings)
cluster_assignment = clustering_model.labels_
print(cluster_assignment)
```
|
ufal/byt5-small-multilexnorm2021-en
|
ufal
| 2023-06-21T19:42:07Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lexical normalization",
"en",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- mc4
- wikipedia
- multilexnorm
tags:
- lexical normalization
license: apache-2.0
---
# Fine-tuned ByT5-small for MultiLexNorm (English version)

This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://noisy-text.github.io/2021/multi-lexnorm.html), which evaluates lexical-normalization systems on 12 social media datasets in 11 languages.
Our system is based on [ByT5](https://arxiv.org/abs/2105.13626), which we first pre-train on synthetic data and then fine-tune on authentic normalization data. It achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. In addition to these fine-tuned models, we also release the source files on [GitHub](https://github.com/ufal/multilexnorm2021) and an interactive demo on [Google Colab](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing).
## How to use
The model was *not* fine-tuned in a standard sentence-to-sentence setting – instead, it was tailored to the token-to-token definition of MultiLexNorm data. Please refer to [**the interactive demo on Colab notebook**](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing) to learn how to use these models.
## How to cite
```bibtex
@inproceedings{wnut-ufal,
title= "{ÚFAL} at {MultiLexNorm} 2021: Improving Multilingual Lexical Normalization by Fine-tuning {ByT5}",
author = "Samuel, David and Straka, Milan",
booktitle = "Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)",
year = "2021",
publisher = "Association for Computational Linguistics",
address = "Punta Cana, Dominican Republic"
}
```
## ByT5 - Small
ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small).
ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-small` significantly outperforms [mt5-small](https://huggingface.co/google/mt5-small) on [TweetQA](https://arxiv.org/abs/1907.06292).
Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626)
Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel*
|
rd124/distilbert-base-uncased-finetuned-imdb-v2
|
rd124
| 2023-06-21T19:36:28Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-21T19:24:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb-v2
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. -->
# distilbert-base-uncased-finetuned-imdb-v2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3723
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6273 | 1.0 | 381 | 2.4473 |
| 2.5148 | 2.0 | 762 | 2.3930 |
| 2.4786 | 3.0 | 1143 | 2.3852 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
breadlicker45/llama-test
|
breadlicker45
| 2023-06-21T19:32:17Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-20T12:31:51Z |
this is fine-tuned/trained on nothing, DO NOT DOWNLOAD
|
keremnazliel/distilbert_squad_for_musique_7
|
keremnazliel
| 2023-06-21T19:24:41Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-21T19:11:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert_squad_for_musique_7
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. -->
# distilbert_squad_for_musique_7
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the None 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: 0.5452
- 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
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
fatzetob/Ponti_Object_Classification
|
fatzetob
| 2023-06-21T19:24:07Z | 1 | 0 |
tf-keras
|
[
"tf-keras",
"vgg16",
"image-classification",
"region:us"
] |
image-classification
| 2023-06-13T08:09:07Z |
---
pipeline_tag: image-classification
---
|
zslrmhb/Reinforce-Cartpole-v1
|
zslrmhb
| 2023-06-21T19:20:17Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T19:19:37Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 495.24 +/- 47.36
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
keremnazliel/distilbert_squad_for_musique_6
|
keremnazliel
| 2023-06-21T19:05:34Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-21T18:39:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert_squad_for_musique_6
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. -->
# distilbert_squad_for_musique_6
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the None 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: 0.1
- 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
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
kchen621/Reinforce-Pixelcopter-PLE-v0
|
kchen621
| 2023-06-21T19:00:37Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T16:04:33Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 30.30 +/- 32.28
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
S3S3/ppo-LunarLander-v2.2
|
S3S3
| 2023-06-21T18:53:40Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T18:53:21Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 283.11 +/- 22.06
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
koreadaeil/my_awesome_qa_model
|
koreadaeil
| 2023-06-21T18:53:31Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-21T17:53:20Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: koreadaeil/my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# koreadaeil/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.8709
- Validation Loss: 5.8422
- Epoch: 2
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.9555 | 5.8683 | 0 |
| 5.9065 | 5.8422 | 1 |
| 5.8709 | 5.8422 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.0
- Tokenizers 0.13.3
|
DunnBC22/codebert-base-mlm-Malicious_URLs
|
DunnBC22
| 2023-06-21T18:37:32Z | 11 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-21T14:47:04Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: codebert-base-mlm-Malicious_URLs
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. -->
# codebert-base-mlm-Malicious_URLs
This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7442
- Accuracy: 0.7322
- Weighted f1: 0.6538
- Micro f1: 0.7322
- Macro f1: 0.4303
- Weighted recall: 0.7322
- Micro recall: 0.7322
- Macro recall: 0.4233
- Weighted precision: 0.6314
- Micro precision: 0.7322
- Macro precision: 0.6034
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
shahafw/a2c-PandaReachDense-v2
|
shahafw
| 2023-06-21T18:32:17Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-10T21:59:12Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.76 +/- 0.71
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
John1561/Web_Ui_Stable_Diffusion
|
John1561
| 2023-06-21T17:54:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-06-21T17:53:38Z |
# Stable Diffusion Webui Bot With Telegram
- This is an open gettokensource project, no charges are allowed!
- Owner use `/ 30` to get 30days token
- Recommended Stable Diffusion Webui Start Command Args `export COMMANDLINE_ARGS="--api --no-hashing --skip-torch-cuda-test --skip-version-check --disable-nan-check --no-download-sd-model --no-half-controlnet --upcast-sampling --no-half-vae --opt-sdp-attention --disable-safe-unpickle --lowram --opt-split-attention --opt-channelslast --deepdanbooru"`
- The necessary extensions
- `https://github.com/zijiren233/sd-webui-controlnet`
|
swl-models/dvArch-Exterior
|
swl-models
| 2023-06-21T17:54:31Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T17:49:14Z |
---
license: creativeml-openrail-m
---
|
keremnazliel/distilbert_squad_for_musique_4
|
keremnazliel
| 2023-06-21T17:51:25Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-21T17:48:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert_squad_for_musique_4
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. -->
# distilbert_squad_for_musique_4
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the None 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: 0.5452
- 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
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
swl-models/Cetus-Mix-v2
|
swl-models
| 2023-06-21T17:51:05Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T17:44:12Z |
---
license: creativeml-openrail-m
---
|
openlamm/epcl_vit-L_256tokens
|
openlamm
| 2023-06-21T17:49:43Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-06-20T18:30:31Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [OpenLAMM]
- **Model type:** [Pytorch]
- **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]:** [FrozenCLIP]
- **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 Data 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. -->
ScanNet
[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 Data 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]
|
swl-models/Cetus-Mix-CodaEdition
|
swl-models
| 2023-06-21T17:37:37Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T17:30:16Z |
---
license: creativeml-openrail-m
---
|
bri25yu/wmt19-ende-t5-small
|
bri25yu
| 2023-06-21T17:06:55Z | 19 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt19",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-14T04:11:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt19
metrics:
- bleu
model-index:
- name: wmt19-ende-t5-small
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt19
type: wmt19
config: de-en
split: validation
args: de-en
metrics:
- name: Bleu
type: bleu
value: 16.085214160195623
---
<!-- 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. -->
# wmt19-ende-t5-small
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt19 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5150
- Bleu: 16.0852
- Brevity Penalty: 0.5512
## 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: 256
- eval_batch_size: 512
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Brevity Penalty |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------------:|
| 2.7369 | 0.01 | 100 | 2.0018 | 9.0851 | 0.5107 |
| 3.3896 | 0.02 | 200 | 1.9108 | 9.9970 | 0.5127 |
| 3.0442 | 0.03 | 300 | 1.8627 | 10.7670 | 0.5245 |
| 2.5136 | 0.04 | 400 | 1.8244 | 10.9280 | 0.5132 |
| 2.4092 | 0.05 | 500 | 1.7951 | 11.4717 | 0.5260 |
| 3.2441 | 0.06 | 600 | 1.7736 | 11.7350 | 0.5197 |
| 2.6997 | 0.07 | 700 | 1.7563 | 12.0741 | 0.5260 |
| 2.5072 | 0.08 | 800 | 1.7416 | 12.3735 | 0.5283 |
| 2.3788 | 0.09 | 900 | 1.7267 | 12.4288 | 0.5285 |
| 2.3533 | 0.1 | 1000 | 1.7247 | 12.4395 | 0.5249 |
| 2.2911 | 0.11 | 1100 | 1.7078 | 12.3887 | 0.5201 |
| 2.3949 | 0.12 | 1200 | 1.6997 | 12.8109 | 0.5288 |
| 2.2343 | 0.13 | 1300 | 1.6930 | 12.8213 | 0.5283 |
| 2.2525 | 0.14 | 1400 | 1.6851 | 13.1221 | 0.5285 |
| 2.2604 | 0.15 | 1500 | 1.6795 | 13.0896 | 0.5261 |
| 2.3146 | 0.16 | 1600 | 1.6723 | 13.1741 | 0.5291 |
| 2.5767 | 0.17 | 1700 | 1.6596 | 13.4224 | 0.5248 |
| 2.698 | 0.18 | 1800 | 1.6576 | 13.6733 | 0.5334 |
| 2.6416 | 0.19 | 1900 | 1.6514 | 13.7184 | 0.5350 |
| 3.0841 | 0.2 | 2000 | 1.6448 | 13.9079 | 0.5357 |
| 2.5039 | 0.21 | 2100 | 1.6375 | 13.9860 | 0.5361 |
| 2.5829 | 0.22 | 2200 | 1.6366 | 13.9246 | 0.5328 |
| 2.5332 | 0.23 | 2300 | 1.6348 | 13.4895 | 0.5209 |
| 2.5832 | 0.24 | 2400 | 1.6240 | 14.0445 | 0.5349 |
| 2.8577 | 0.25 | 2500 | 1.6182 | 14.1085 | 0.5344 |
| 2.9157 | 0.26 | 2600 | 1.6285 | 13.7982 | 0.5365 |
| 2.6758 | 0.27 | 2700 | 1.6249 | 13.8638 | 0.5392 |
| 2.0391 | 0.28 | 2800 | 1.6205 | 13.9645 | 0.5396 |
| 2.8146 | 0.29 | 2900 | 1.6210 | 14.2823 | 0.5409 |
| 2.6602 | 0.3 | 3000 | 1.6219 | 13.9663 | 0.5391 |
| 1.7745 | 0.31 | 3100 | 1.6088 | 14.4206 | 0.5413 |
| 2.3483 | 0.32 | 3200 | 1.6050 | 14.6208 | 0.5471 |
| 1.9911 | 0.33 | 3300 | 1.6004 | 14.5458 | 0.5396 |
| 1.8973 | 0.34 | 3400 | 1.5985 | 14.5387 | 0.5400 |
| 2.6956 | 0.35 | 3500 | 1.6005 | 14.7482 | 0.5458 |
| 2.322 | 0.36 | 3600 | 1.5949 | 14.7322 | 0.5448 |
| 1.5147 | 0.37 | 3700 | 1.5966 | 14.8456 | 0.5431 |
| 2.0606 | 0.38 | 3800 | 1.5899 | 14.6267 | 0.5333 |
| 3.0341 | 0.39 | 3900 | 1.5842 | 14.7705 | 0.5414 |
| 1.5069 | 0.4 | 4000 | 1.5911 | 14.6861 | 0.5372 |
| 2.339 | 0.41 | 4100 | 1.5949 | 14.6970 | 0.5481 |
| 2.5221 | 0.42 | 4200 | 1.5870 | 14.6996 | 0.5403 |
| 1.6398 | 0.43 | 4300 | 1.5790 | 14.8826 | 0.5431 |
| 2.2758 | 0.44 | 4400 | 1.5818 | 14.5580 | 0.5375 |
| 2.2622 | 0.45 | 4500 | 1.5821 | 15.0062 | 0.5428 |
| 1.3329 | 0.46 | 4600 | 1.5792 | 14.7609 | 0.5377 |
| 1.7537 | 0.47 | 4700 | 1.5744 | 15.1037 | 0.5425 |
| 2.5379 | 0.48 | 4800 | 1.5756 | 15.2684 | 0.5479 |
| 2.1236 | 0.49 | 4900 | 1.5822 | 14.8229 | 0.5478 |
| 2.9621 | 0.5 | 5000 | 1.5747 | 14.9948 | 0.5443 |
| 1.9832 | 0.51 | 5100 | 1.5838 | 14.8682 | 0.5468 |
| 1.4962 | 0.52 | 5200 | 1.5836 | 14.8094 | 0.5397 |
| 2.4318 | 0.53 | 5300 | 1.5826 | 14.8213 | 0.5422 |
| 1.9338 | 0.54 | 5400 | 1.5869 | 14.5571 | 0.5402 |
| 1.404 | 0.55 | 5500 | 1.5891 | 14.5103 | 0.5414 |
| 2.2803 | 0.56 | 5600 | 1.5864 | 14.6338 | 0.5417 |
| 2.3725 | 0.57 | 5700 | 1.5893 | 14.3405 | 0.5385 |
| 1.1436 | 0.58 | 5800 | 1.5703 | 15.3309 | 0.5457 |
| 2.1695 | 0.59 | 5900 | 1.5690 | 15.3571 | 0.5438 |
| 1.7295 | 0.6 | 6000 | 1.5653 | 15.3547 | 0.5421 |
| 1.3033 | 0.61 | 6100 | 1.5649 | 15.3084 | 0.5442 |
| 2.396 | 0.62 | 6200 | 1.5592 | 15.5594 | 0.5440 |
| 2.133 | 0.63 | 6300 | 1.5634 | 15.3689 | 0.5420 |
| 1.1775 | 0.64 | 6400 | 1.5639 | 15.4869 | 0.5389 |
| 2.0793 | 0.65 | 6500 | 1.5541 | 15.6320 | 0.5453 |
| 1.7569 | 0.66 | 6600 | 1.5588 | 15.7405 | 0.5429 |
| 1.1035 | 0.67 | 6700 | 1.5520 | 15.7011 | 0.5450 |
| 1.5799 | 0.68 | 6800 | 1.5517 | 15.9203 | 0.5490 |
| 1.7737 | 0.69 | 6900 | 1.5473 | 15.8992 | 0.5480 |
| 1.3071 | 0.7 | 7000 | 1.5491 | 15.7140 | 0.5446 |
| 2.2214 | 0.71 | 7100 | 1.5460 | 15.9360 | 0.5479 |
| 1.7848 | 0.72 | 7200 | 1.5431 | 15.9338 | 0.5490 |
| 1.1231 | 0.73 | 7300 | 1.5398 | 15.8774 | 0.5444 |
| 1.7741 | 0.74 | 7400 | 1.5399 | 15.9724 | 0.5451 |
| 1.7098 | 0.75 | 7500 | 1.5361 | 15.9098 | 0.5447 |
| 1.0787 | 0.76 | 7600 | 1.5393 | 15.9781 | 0.5457 |
| 1.9856 | 0.77 | 7700 | 1.5348 | 15.9521 | 0.5462 |
| 2.1294 | 0.78 | 7800 | 1.5345 | 16.0042 | 0.5463 |
| 1.1938 | 0.79 | 7900 | 1.5314 | 16.0554 | 0.5495 |
| 1.9579 | 0.8 | 8000 | 1.5307 | 15.9349 | 0.5482 |
| 1.844 | 0.81 | 8100 | 1.5285 | 15.8589 | 0.5448 |
| 1.1464 | 0.82 | 8200 | 1.5413 | 15.9210 | 0.5435 |
| 2.2903 | 0.83 | 8300 | 1.5230 | 16.0164 | 0.5405 |
| 2.1489 | 0.84 | 8400 | 1.5263 | 15.9423 | 0.5443 |
| 1.8138 | 0.85 | 8500 | 1.5350 | 15.8267 | 0.5464 |
| 2.4025 | 0.86 | 8600 | 1.5275 | 15.8493 | 0.5430 |
| 1.6758 | 0.87 | 8700 | 1.5206 | 15.9246 | 0.5464 |
| 1.3671 | 0.88 | 8800 | 1.5235 | 15.9662 | 0.5460 |
| 2.3341 | 0.89 | 8900 | 1.5221 | 16.0465 | 0.5456 |
| 1.8405 | 0.9 | 9000 | 1.5201 | 16.0834 | 0.5454 |
| 1.4133 | 0.91 | 9100 | 1.5250 | 15.8619 | 0.5442 |
| 2.4374 | 0.92 | 9200 | 1.5261 | 15.8174 | 0.5429 |
| 1.3627 | 0.93 | 9300 | 1.5257 | 15.7541 | 0.5450 |
| 1.5003 | 0.94 | 9400 | 1.5249 | 15.9109 | 0.5463 |
| 2.2002 | 0.95 | 9500 | 1.5252 | 15.8338 | 0.5434 |
| 2.3461 | 0.96 | 9600 | 1.5262 | 15.9195 | 0.5469 |
| 1.2607 | 0.97 | 9700 | 1.5197 | 15.8370 | 0.5459 |
| 2.3737 | 0.98 | 9800 | 1.5178 | 16.0579 | 0.5475 |
| 1.3968 | 0.99 | 9900 | 1.5132 | 16.1729 | 0.5522 |
| 1.1816 | 1.0 | 10000 | 1.5150 | 16.0852 | 0.5512 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
swl-models/ShyakuJXMix-v1.0
|
swl-models
| 2023-06-21T16:59:03Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T16:03:58Z |
---
license: creativeml-openrail-m
---
|
keremnazliel/distilbert_squad_for_musique_3
|
keremnazliel
| 2023-06-21T16:58:29Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-21T16:54:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert_squad_for_musique_3
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. -->
# distilbert_squad_for_musique_3
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the None 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: 0.5452
- 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
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
antokprasetyo/Anggittt
|
antokprasetyo
| 2023-06-21T16:57:52Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T16:55:58Z |
---
license: creativeml-openrail-m
---
|
mandliya/ppo-LunarLander-v2
|
mandliya
| 2023-06-21T16:56:50Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T07:37:09Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 267.94 +/- 15.09
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Curiolearner/dqn-SpaceInvadersNoFrameskip-v4
|
Curiolearner
| 2023-06-21T16:49:56Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T16:49:21Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 549.50 +/- 96.42
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Curiolearner -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Curiolearner -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Curiolearner
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
deepghs/anime_ch_eye_color
|
deepghs
| 2023-06-21T16:46:41Z | 0 | 0 | null |
[
"onnx",
"art",
"image-classification",
"dataset:deepghs/anime_ch_eye_color",
"license:mit",
"region:us"
] |
image-classification
| 2023-06-14T03:34:13Z |
---
license: mit
datasets:
- deepghs/anime_ch_eye_color
metrics:
- accuracy
- f1
pipeline_tag: image-classification
tags:
- art
---
| Name | FLOPS | Params | Accuracy | AUC | Confusion | Labels |
|:-------------------:|:-------:|:--------:|:----------:|:------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------:|
| caformer_s36_raw | 22.10G | 37.24M | 57.27% | 0.9246 | [confusion](https://huggingface.co/deepghs/anime_ch_eye_color/blob/main/caformer_s36_raw/plot_confusion.png) | `aqua`, `blue`, `brown`, `orange`, `golden`, `yellow`, `pink`, `purple`, `red`, `grey`, `silver`, `white`, `black`, `green` |
| caformer_s36_v0 | 22.10G | 37.23M | 64.18% | 0.9278 | [confusion](https://huggingface.co/deepghs/anime_ch_eye_color/blob/main/caformer_s36_v0/plot_confusion.png) | `aqua`, `blue`, `green`, `brown`, `orange`, `yellow`, `pink`, `purple`, `red`, `light`, `black` |
| mobilenetv3_v0_dist | 0.63G | 4.18M | 60.66% | 0.9201 | [confusion](https://huggingface.co/deepghs/anime_ch_eye_color/blob/main/mobilenetv3_v0_dist/plot_confusion.png) | `aqua`, `blue`, `green`, `brown`, `orange`, `yellow`, `pink`, `purple`, `red`, `light`, `black` |
|
keremnazliel/distilbert_squad_for_musique_2
|
keremnazliel
| 2023-06-21T16:46:16Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-21T15:28:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert_squad_for_musique_2
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. -->
# distilbert_squad_for_musique_2
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the None 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: 0.5452
- 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
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
deepghs/anime_ch_hair_color
|
deepghs
| 2023-06-21T16:43:50Z | 0 | 1 | null |
[
"onnx",
"art",
"image-classification",
"dataset:deepghs/anime_ch_hair_color",
"license:mit",
"region:us"
] |
image-classification
| 2023-06-14T03:26:04Z |
---
license: mit
datasets:
- deepghs/anime_ch_hair_color
metrics:
- accuracy
- f1
pipeline_tag: image-classification
tags:
- art
---
| Name | FLOPS | Params | Accuracy | AUC | Confusion | Labels |
|:----------------------:|:-------:|:--------:|:----------:|:------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|
| caformer_s36_raw | 22.10G | 37.23M | 65.55% | 0.9382 | [confusion](https://huggingface.co/deepghs/anime_ch_hair_color/blob/main/caformer_s36_raw/plot_confusion.png) | `aqua`, `blue`, `brown`, `orange`, `pink`, `purple`, `red`, `grey`, `silver`, `white`, `black`, `green` |
| caformer_s36_v0 | 22.10G | 37.23M | 75.06% | 0.9521 | [confusion](https://huggingface.co/deepghs/anime_ch_hair_color/blob/main/caformer_s36_v0/plot_confusion.png) | `aqua`, `blue`, `green`, `brown`, `orange`, `pink`, `purple`, `red`, `light`, `black` |
| caformer_s36_v0_ncerce | 22.10G | 37.23M | 75.03% | 0.9357 | [confusion](https://huggingface.co/deepghs/anime_ch_hair_color/blob/main/caformer_s36_v0_ncerce/plot_confusion.png) | `aqua`, `blue`, `green`, `brown`, `orange`, `pink`, `purple`, `red`, `light`, `black` |
| mobilenetv3_v0_dist | 0.63G | 4.18M | 72.21% | 0.9458 | [confusion](https://huggingface.co/deepghs/anime_ch_hair_color/blob/main/mobilenetv3_v0_dist/plot_confusion.png) | `aqua`, `blue`, `green`, `brown`, `orange`, `pink`, `purple`, `red`, `light`, `black` |
|
Naseej/noon-7b
|
Naseej
| 2023-06-21T16:42:13Z | 659 | 42 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"instructional",
"question-answering",
"arabic",
"ar",
"en",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-20T19:21:26Z |
---
license: bigscience-bloom-rail-1.0
language:
- ar
- en
pipeline_tag: text-generation
tags:
- instructional
- question-answering
- arabic
widget:
- text: اكتب مقال عن الذكاء الصناعي وتطوراته.
example_title: Instruction 1
- text: اعط بعض النصائح عن كيفية الحفاظ على حياة صحية.
example_title: Instruction 2
- text: ماذا تعرف عن فوائد الصيام؟
example_title: Question 1
- text: قطف إسماعيل 5 تفاحات، وأعطى 2 منها لأخيه، فكم بقي عند إسماعيل من تفاحة؟
example_title: Question 2
---
<img src="https://i.ibb.co/3NzxfFQ/noon-banner.png" alt="noon-banner" border="0" width="85%" height="85%" style="margin:auto; display:block">
## **Noon - a 7-billion parameter Arabic Large Language Model**
We present the 7-billion parameter variant of **Noon**, an Arabic Large Language model based on **BLOOM**, a foundation model released by the [bigscience](https://huggingface.co/bigscience) workshop.
Noon was trained with the main focus of having a model that responds to various types of instructions and questions (text generation, code generation, mathematical problems, closed/open-book questions, etc.)
We trained the model using the ColossalAI framework which fully supports the HuggingFace library models, and implements different optimization and quantization techniques for billion-scale LLMs.
The training data is a combination of Arabic datasets covering multiple tasks, more details are provided in the dataset section.
مرحبًا بكم في بطاقة نموذج "نون"!
يحتوي "نون" على أكثر من 7 مليار عامل متغير، مما يجعله أكبر نموذج للغة العربية المطروح حتى الآن. تم تدريب هذا النموذج على أكثر من 110,000 سجل بيانات باللغة العربية، والتي تغطي أكثر من 11 ملايين كلمة، تتنوع ما بين إنتاج النصوص، وإنشاء الشفرات، وحل المسائل الرياضية، والأسئلة المغلقة/المفتوحة. تم تدريب هذا النموذج باستخدام تقنيات تدريب متقدمة مثل التدريب الموزع على عدة وحدات معالجة رسومية، وتكييف LoRA (Low Rank Adaptation)، وتحسين ZeRO (Zero Redundancy Optimization).
نحن فخورون بتقديم هذا النموذج الذي يمثل قفزة نوعية في تقنية معالجة اللغة العربية. نقدم في الأقسام التالية مزيد من التفاصيل عن كيفية استخدام نموذج "نون" ومختلف الخصائص التقنية المتعلقة بعملية التدريب.
على أمل أن يكون هذا النموذج خدمةً للطورين والباحثين العلميين في هذا المجال، ولكل الناطقين باللغة العربية.
### **Usage**
The usage of our model only requires the Transformers library, and can be loaded as follows:
```python
from transformers import BloomTokenizerFast, BloomForCausalLM, pipeline
text="اكتب مقالا من عدة أسطر عن الذكاء الصناعي وتطوراته"
prompt = f'Instruction:\n{text}\n\nResponse:'
model = BloomForCausalLM.from_pretrained('Naseej/noon-7b')
tokenizer = BloomTokenizerFast.from_pretrained('Naseej/noon-7b')
generation_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
# We recommend the provided hyperparameters for generation
# But encourage you to try different values
response = generation_pipeline(prompt,
pad_token_id=tokenizer.eos_token_id,
do_sample=False,
num_beams=4,
max_length=500,
top_p=0.1,
top_k=20,
repetition_penalty = 3.0,
no_repeat_ngram_size=3)[0]['generated_text']
print(response)
```
### **Training's computational requirements**
Noon-7b was trained on 8-A100 GPUs using Distributed multi-GPU training via the [ColossalAI](https://github.com/hpcaitech/ColossalAI) framework.
### **Dataset**
To ensure the diversity of data points and satisfy our purpose of instruction-tuning, we collected, labeled, filtered, and reviewed a set of datasets, each tailored to specific instruction types.
Noting that all the datasets are in Arabic, they comprise:
- [Second version of the Alpaca dataset](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM), generated using GPT4.
- Self-instruct records, split between samples generated by us using the [self-instruct](https://github.com/yizhongw/self-instruct) framework, and further translated ones.
- The instructional dataset released by [Databricks](https://github.com/databrickslabs/dolly), which comprises high quality human-generated instructions and responses.
- [TruthfulQA](https://huggingface.co/datasets/truthful_qa) dataset, to further guide the model on how to truthfully respond to factoid-based questions.
- [Grade School Math](https://huggingface.co/datasets/gsm8k) dataset, to enhance the model's performance using chain-of-thought mathematical problems.
- Arabic arithmetic problems, generated by us using ChatGPT for further improvement of the model's ability to solve mathematical problems.
The full dataset adds up to over **110K** records.
### **Evaluation**
Throughout a set of over 4000 Arabic data samples, Noon-7b was automatically evaluated using **OpenAI's [GPT3.5 Turbo](https://platform.openai.com/docs/models)** model.
Provided with clear and carefully crafted evaluation criteria (aligning with the model's training objective as well as the syntactic and grammatical rules of the Arabic language), GPT3.5 Turbo was prompted to evaluate each of Noon's responses to an input instruction on a scale of **1 - 5**.
We concluded the evaluation by averaging the provided scores, adding up to an impressive final score of **4.07/5**.
**NOTE:** Although we acknowledge that this proposed framework is not an exact solution and that it remains an ongoing area of research, we hold the belief that it has the potential to replicate human assessments to a reasonably satisfactory extent.
### **Disclaimer**
The generated responses from this AI model are purely algorithmic and should be interpreted with caution. The model's outputs may occasionally exhibit bias, offensive language, or potentially harmful content. It is important to note that these responses do not reflect the personal preferences or viewpoints of the authors or the organization of Naseej.
While every effort is made to mitigate the harmfulness of the model's outputs, it is impossible to guarantee complete elimination of biases or offensive content. The model learns from vast amounts of data and may inadvertently replicate or amplify existing societal biases present in the training data.
Users are advised to critically evaluate and verify the information provided by the model. Exercise discretion when utilizing the model's responses, particularly in sensitive or controversial topics.
We are committed to ongoing research and development to improve the model's performance, minimize biases, and reduce harmful outputs. Your feedback and insights are valuable in helping us achieve these goals.
|
deepghs/anime_ch_ear
|
deepghs
| 2023-06-21T16:34:57Z | 0 | 0 | null |
[
"onnx",
"art",
"image-classification",
"dataset:deepghs/anime_ch_ear",
"license:mit",
"region:us"
] |
image-classification
| 2023-06-17T02:15:03Z |
---
license: mit
datasets:
- deepghs/anime_ch_ear
metrics:
- accuracy
- f1
pipeline_tag: image-classification
tags:
- art
---
| Name | FLOPS | Params | Accuracy | AUC | Confusion | Labels |
|:-------------------:|:-------:|:--------:|:----------:|:------:|:---------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| caformer_s36_raw | 22.10G | 37.27M | 82.56% | 0.9847 | [confusion](https://huggingface.co/deepghs/anime_ch_ear/blob/main/caformer_s36_raw/plot_confusion.png) | `alpaca`, `bat`, `bear`, `bunny`, `cat`, `cow`, `deer`, `dog`, `ermine`, `ferret`, `fox`, `goat`, `horse`, `jackal`, `lion`, `monkey`, `mouse`, `panda`, `pig`, `pikachu`, `pointed`, `raccoon`, `reindeer`, `robot`, `sheep`, `squirrel`, `tiger`, `wolf`, `none` |
| caformer_s36_v0 | 22.10G | 37.27M | 83.33% | 0.9845 | [confusion](https://huggingface.co/deepghs/anime_ch_ear/blob/main/caformer_s36_v0/plot_confusion.png) | `alpaca`, `bat`, `bear`, `bunny`, `cat`, `cow`, `deer`, `dog`, `ermine`, `ferret`, `fox`, `goat`, `horse`, `jackal`, `lion`, `monkey`, `mouse`, `panda`, `pig`, `pikachu`, `pointed`, `raccoon`, `robot`, `sheep`, `squirrel`, `tiger`, `wolf`, `none` |
| mobilenetv3_v0_dist | 0.63G | 4.18M | 74.70% | 0.9716 | [confusion](https://huggingface.co/deepghs/anime_ch_ear/blob/main/mobilenetv3_v0_dist/plot_confusion.png) | `alpaca`, `bat`, `bear`, `bunny`, `cat`, `cow`, `deer`, `dog`, `ermine`, `ferret`, `fox`, `goat`, `horse`, `jackal`, `lion`, `monkey`, `mouse`, `panda`, `pig`, `pikachu`, `pointed`, `raccoon`, `robot`, `sheep`, `squirrel`, `tiger`, `wolf`, `none` |
|
bandrocks/my_awesome_eli5_clm-model
|
bandrocks
| 2023-06-21T16:34:57Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T16:02:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
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. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7378
## 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: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8621 | 1.0 | 1148 | 3.7567 |
| 3.7762 | 2.0 | 2296 | 3.7399 |
| 3.7328 | 3.0 | 3444 | 3.7378 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Nacholmo/Counterfeit-V2.5-vae-swapped
|
Nacholmo
| 2023-06-21T16:34:48Z | 34 | 2 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-02-10T20:29:47Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
---
# Counterfeit-V2.5 vae swapped, converted to diffusers for your enjoyment.
1. Safetensors to ckpt
2. Swap vae
3. Ckpt to diffusers
4. ??
5. profit
Original model: https://huggingface.co/gsdf/Counterfeit-V2.5
|
Nacholmo/meinamixv7-diffusers
|
Nacholmo
| 2023-06-21T16:34:39Z | 23 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-06T02:08:03Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
---
Original model: https://huggingface.co/Meina/MeinaMix
|
anrojasor/ppo-LunarLander-v2
|
anrojasor
| 2023-06-21T16:33:24Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T01:42:17Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.39 +/- 23.64
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
koreadaeil/finetuned-bert-piqa
|
koreadaeil
| 2023-06-21T16:33:07Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T16:27:46Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: koreadaeil/finetuned-bert-piqa
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# koreadaeil/finetuned-bert-piqa
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8264
- Validation Loss: 2.6491
- Epoch: 2
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8757 | 2.7555 | 0 |
| 2.8434 | 2.7213 | 1 |
| 2.8264 | 2.6491 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Jinouga/brie-larson-v1
|
Jinouga
| 2023-06-21T16:23:28Z | 32 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-21T16:19:32Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### brie-larson-V1 Dreambooth model trained by Jinouga with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
UMUTeam/spanish_capitalization_punctuation_restoration
|
UMUTeam
| 2023-06-21T16:23:15Z | 50 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-21T16:11:47Z |
---
widget:
- text: qué rico está el helado
example_title: Example 1
- text: estás bien
example_title: Example 2
- text: mi equipo favorito es real madrid
example_title: Example 3
language:
- es
---
|
SouhilOuchene/ACPRECBERT_Part2_islem
|
SouhilOuchene
| 2023-06-21T16:21:46Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"camembert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-06-21T16:21:02Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# SouhilOuchene/ACPRECBERT_Part2_islem
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text 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.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("SouhilOuchene/ACPRECBERT_Part2_islem")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
|
snailgood/NWsnail
|
snailgood
| 2023-06-21T16:19:43Z | 0 | 2 | null |
[
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T15:38:23Z |
---
license: creativeml-openrail-m
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [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 Data 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 Data 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]
|
catrabbitbear/dqn-SpaceInvadersNoFrameskip-v4
|
catrabbitbear
| 2023-06-21T16:15:29Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T16:14:49Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 579.00 +/- 282.12
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga catrabbitbear -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga catrabbitbear -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga catrabbitbear
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 2000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
TheFools/Nabilafbynt
|
TheFools
| 2023-06-21T16:13:36Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T16:02:55Z |
---
license: creativeml-openrail-m
---
|
koreadaeil/my_awesome_eli5_clm-model
|
koreadaeil
| 2023-06-21T16:02:39Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T05:50:23Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: koreadaeil/my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# koreadaeil/my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.9069
- Validation Loss: 2.7550
- Epoch: 2
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.0432 | 2.9414 | 0 |
| 3.0152 | 2.7736 | 1 |
| 2.9069 | 2.7550 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.0
- Tokenizers 0.13.3
|
swl-models/Sakuramochimix-v1.0
|
swl-models
| 2023-06-21T16:02:10Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T15:59:02Z |
---
license: creativeml-openrail-m
---
|
pellucid/my_awesome_opus100_model
|
pellucid
| 2023-06-21T15:57:28Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"longt5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-21T07:37:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: my_awesome_opus100_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
config: en-ko
split: train
args: en-ko
metrics:
- name: Bleu
type: bleu
value: 0.0
---
<!-- 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. -->
# my_awesome_opus100_model
This model is a fine-tuned version of [KETI-AIR-Downstream/long-ke-t5-base-translation-aihub-en2ko](https://huggingface.co/KETI-AIR-Downstream/long-ke-t5-base-translation-aihub-en2ko) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Bleu: 0.0
- Gen Len: 0.0
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- 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 | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 250 | nan | 2.9676 | 12.146 |
| 2.5985 | 2.0 | 500 | nan | 0.0 | 0.0 |
| 2.5985 | 3.0 | 750 | nan | 0.0 | 0.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
DioulaD/falcon-7b-qlora-ge-dq
|
DioulaD
| 2023-06-21T15:48:22Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-21T15:48:20Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
DeepLake/Alchemy_Stars_Vocal
|
DeepLake
| 2023-06-21T15:47:09Z | 0 | 0 | null |
[
"vocal",
"games",
"zh",
"ja",
"license:unknown",
"region:us"
] | null | 2023-06-21T07:43:01Z |
---
license: unknown
language:
- zh
- ja
tags:
- vocal
- games
---
For VITS. Trained with Alchemy Stars vocal data. JP or CN vocal are denoted in the file name.
用于VITS。用《白夜极光》的语音制作。日配JP,中配CN,见于文件名,注意区分。
|
bemc22/ppo-luna-lander-mark-i
|
bemc22
| 2023-06-21T15:46:51Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-20T14:07:52Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 288.66 +/- 12.85
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
swl-models/Shanzhagao-v1
|
swl-models
| 2023-06-21T15:45:04Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T15:42:34Z |
---
license: creativeml-openrail-m
---
|
swl-models/Entity_404
|
swl-models
| 2023-06-21T15:40:31Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T15:36:36Z |
---
license: creativeml-openrail-m
---
|
hoyincheung/redpj3B-lora-int8-alpaca
|
hoyincheung
| 2023-06-21T15:29:51Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-21T15:29:50Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
abhishek-ignite/gpt-neo-1.3b-ignite-3
|
abhishek-ignite
| 2023-06-21T15:18:43Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-21T15:18:41Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
p1atdev/pvc-v3
|
p1atdev
| 2023-06-21T15:14:34Z | 61 | 57 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"safetensors",
"en",
"dataset:p1atdev/pvc",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-01T21:29:48Z |
---
license: other
datasets:
- p1atdev/pvc
language:
- en
library_name: diffusers
thumbnail: "https://s3.amazonaws.com/moonup/production/uploads/1677743056321-6305db1fcfbde33ef7d480ff.png"
tags:
- text-to-image
- stable-diffusion
- safetensors
widget:
- text: pvc, anime, masterpiece, best quality, exceptional, 1girl, bangs, bare shoulders, beret, black hair, black shorts, blue hair, bracelet, breasts, buttons, colored inner hair, double-breasted, eyewear removed, green headwear, green jacket, grey eyes, grey sky, hat, jacket, jewelry, long hair, looking at viewer, multicolored hair, neck ring, o-ring, off shoulder, rain, round eyewear, shorts, sidelocks, small breasts, solo, sunglasses, wavy hair, wet, zipper
example_title: The WD1.5 girl
- text: pvc, anime, masterpiece, best quality, exceptional, 1girl, blonde hair, hat, baseball cap, aqua eyes, earrings, hoop earrings, yellow shirt, looking at viewer, upper body, simple background
example_title: The blonde hair girl
- text: pvc, masterpiece, best quality, exceptional, 1girl, cat ears, red hair, long hair, hairpin, swept bangs, yellow eyes, black jacket, white shirt, blue tie, white gloves, hand up, upper body, looking at viewer, buildings
example_title: A red hair girl
- text: nendoroid, masterpiece, best quality, exceptional, 1girl, cat ears, red hair, long hair, hairpin, swept bangs, yellow eyes, black jacket, white shirt, blue tie, white gloves, hand up, upper body, looking at viewer,
example_title: nendoroid style
- text: figma, masterpiece, best quality, exceptional, 1girl, cat ears, red hair, long hair, hairpin, swept bangs, yellow eyes, black jacket, white shirt, blue tie, white gloves, hand up, upper body, looking at viewer, buildings
example_title: figma style
---
# PVC v3
This model is a latent diffusion model finetuned on Waifu Diffusion v1.5 beta 2 with PVC figure images.
You can use Danbooru tags to generate images.
## Downloads
<div class="flex flex-col dark:bg-gray-900 rounded-md divide-y dark:divide-gray-800">
<div class="flex justify-between px-4 py-2">
<a class="underline" href="https://huggingface.co/p1atdev/pvc-v3/resolve/checkpoints/pvc-v3-fp16.safetensors">pvc-v3-fp16.safetensors</a>
<div>2.58 GB</div>
</div>
<div class="flex justify-between px-4 py-2">
<a class="underline" href="https://huggingface.co/p1atdev/pvc-v3/resolve/checkpoints/pvc-v3-fp16.ckpt">pvc-v3-fp16.ckpt</a>
<div>2.58 GB</div>
</div>
<div class="flex justify-between px-4 py-2">
<a class="underline" href="https://huggingface.co/p1atdev/pvc-v3/resolve/checkpoints/pvc-v3-fp32.safetensors">pvc-v3-fp32.safetensors</a>
<div>5.16 GB</div>
</div>
<div class="flex justify-between px-4 py-2">
<a class="underline" href="https://huggingface.co/p1atdev/pvc-v3/resolve/checkpoints/pvc-v3-fp32.ckpt">pvc-v3-fp32.ckpt</a>
<div>5.16 GB</div>
</div>
<div class="flex justify-between px-4 py-1">
<a class="underline opacity-75" href="https://huggingface.co/p1atdev/pvc-v3/tree/checkpoints">Show all</a>
</div>
</div>
Please use [WD's vae](https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt) to get good results!
Also, you can use [badquality embedding](https://huggingface.co/p1atdev/badquality) in negative prompt!
## Prompt guide
### Trigger words
- `pvc` means the pvc material style but not needed always.
- `figma` is the figure style that has joints, and more tend to be product thumbnail images. Use with `doll joints` to get better joints.
- `nendoroid` means the style of chibi figures. Use with `chibi` to get better results.
### Tips
The PVC figure style is closer to the anime style than to the realistic style.
So, it is recommended to put `anime` to **positive** prompt or `realistic` to **negative** prompt to get better results sometimes.
If you want to avoid too realistic faces, try this!
## Examples
<div class="not-prose grid grid-cols-1 lg:grid-cols-2 gap-4">
<div class="border dakr:border-gray-750 dark:bg-gray-850 rounded-md overflow-hidden">
<img class="w-full" src="https://s3.amazonaws.com/moonup/production/uploads/1677723651374-6305db1fcfbde33ef7d480ff.jpeg"/>
<div class="px-4 py-2">
<details>
<p class="whitespace-pre-line">
masterpiece, best quality, pvc, 1girl, cat ears, blue hair, gradient hair, colored inner hair, long hair, floating hair, blue eyes, school uniform, blue shirt, ribbon, short skirt, thighhighs, zettai ryouiki, school bag, from above, cowboy shot, looking at viewer, wind, street, day,
Negative prompt: badquality, oldest, chibi,
Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 10, Seed: 744670484, Size: 576x768, Model hash: 0866b17d46, Model: pvc-v3-fp16, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
</p>
</details>
</div>
</div>
<div class="border dakr:border-gray-750 dark:bg-gray-850 rounded-md overflow-hidden">
<img class="w-full" src="https://s3.amazonaws.com/moonup/production/uploads/1677725923219-6305db1fcfbde33ef7d480ff.jpeg"/>
<div class="px-4 py-2">
<details>
<p class="whitespace-pre-line">
masterpiece, best quality, exceptional, pvc, 1girl, bangs, bare shoulders, beret, black hair, black shorts, blue hair, bracelet, breasts, buttons, colored inner hair, double-breasted, eyewear removed, green headwear, green jacket, grey eyes, grey sky, hat, jacket, jewelry, long hair, looking at viewer, multicolored hair, neck ring, o-ring, off shoulder, rain, round eyewear, shorts, sidelocks, small breasts, solo, sunglasses, wavy hair, wet, zipper,
Negative prompt: badquality, oldest, chibi,
Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 10, Seed: 2954169314, Size: 576x768, Model hash: 0866b17d46, Model: pvc-v3-fp16, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent</p>
</details>
</div>
</div>
<div class="border dakr:border-gray-750 dark:bg-gray-850 rounded-md overflow-hidden">
<img class="w-full" src="https://s3.amazonaws.com/moonup/production/uploads/1677726308338-6305db1fcfbde33ef7d480ff.jpeg"/>
<div class="px-4 py-2">
<details>
<p class="whitespace-pre-line">
masterpiece, best quality, exceptional, pvc, 1girl, cat ears, red hair, long hair, hairpin, swept bangs, yellow eyes, black jacket, white shirt, blue tie, white gloves, hand up, upper body, looking at viewer, buildings
Negative prompt: badquality, oldest, chibi, realistic
Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 10, Seed: 2320075190, Size: 576x768, Model hash: 0866b17d46, Model: pvc-v3-fp16, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
</p>
</details>
</div>
</div>
<div class="border dakr:border-gray-750 dark:bg-gray-850 rounded-md overflow-hidden">
<img class="w-full" src="https://s3.amazonaws.com/moonup/production/uploads/1677730950628-6305db1fcfbde33ef7d480ff.jpeg"/>
<div class="px-4 py-2">
<details>
<p class="whitespace-pre-line">
masterpiece, best quality, exceptional, pvc, anime, 1boy, grey hair, red eyes, holding gun, handgun, black coat, looking at viewer, dynamic,
Negative prompt: badquality, oldest, chibi,
Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 10, Seed: 2543033775, Size: 576x768, Model hash: 0866b17d46, Model: pvc-v3-fp16, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
</p>
</details>
</div>
</div>
<div class="border dakr:border-gray-750 dark:bg-gray-850 rounded-md overflow-hidden">
<img class="w-full" src="https://s3.amazonaws.com/moonup/production/uploads/1677734822577-6305db1fcfbde33ef7d480ff.jpeg"/>
<div class="px-4 py-2">
<details>
<p class="whitespace-pre-line">
masterpiece, best quality, exceptional, figma, 1girl, cat ears, blue hair, high ponytail, parted bangs, white shirt, dress shirt, short sleeves, shorts, looking at viewer, doll joints,
Negative prompt: badquality, oldest, chibi
Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 10, Seed: 595390714, Size: 576x768, Model hash: 0866b17d46, Model: pvc-v3-fp16, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
</p>
</details>
</div>
</div>
<div class="border dakr:border-gray-750 dark:bg-gray-850 rounded-md overflow-hidden">
<img class="w-full" src="https://s3.amazonaws.com/moonup/production/uploads/1677732154061-6305db1fcfbde33ef7d480ff.jpeg"/>
<div class="px-4 py-2">
<details>
<p class="whitespace-pre-line">
masterpiece, best quality, exceptional, figma, 1girl, brown hair, bob cut, blunt bangs, expressionless, red track suit, long pants, full body, running, dynamic, looking at viewer,
Negative prompt: badquality, oldest, chibi, realistic,
Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 10, Seed: 617339547, Size: 576x768, Model hash: 0866b17d46, Model: pvc-v3-fp16, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
</p>
</details>
</div>
</div>
<div class="border dakr:border-gray-750 dark:bg-gray-850 rounded-md overflow-hidden">
<img class="w-full" src="https://s3.amazonaws.com/moonup/production/uploads/1677733209241-6305db1fcfbde33ef7d480ff.jpeg"/>
<div class="px-4 py-2">
<details>
<p class="whitespace-pre-line">
masterpiece, best quality, exceptional, nendoroid, chibi, masterpiece, best quality, exceptional, 1girl, aqua eyes, baseball cap, blonde hair, closed mouth, earrings, green background, hat, hoop earrings, jewelry, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt,
Negative prompt: badquality, oldest, realistic,
Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 10, Seed: 3673139852, Size: 576x768, Model hash: 0866b17d46, Model: pvc-v3-fp16, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
</p>
</details>
</div>
</div>
<div class="border dakr:border-gray-750 dark:bg-gray-850 rounded-md overflow-hidden">
<img class="w-full" src="https://s3.amazonaws.com/moonup/production/uploads/1677733514916-6305db1fcfbde33ef7d480ff.jpeg"/>
<div class="px-4 py-2">
<details>
<p class="whitespace-pre-line">
masterpiece, best quality, exceptional, nendoroid, chibi, masterpiece, best quality, exceptional, 1girl, bare shoulders, baseball cap, black gloves, black headwear, black shirt, blue eyes, blue hair, breasts, coat, crop top, gloves, hand on hip, hat, large breasts, long hair, long sleeves, looking at viewer, mask, midriff, mouth mask, navel, off shoulder, open clothes, open coat, shirt, sleeveless, sleeveless shirt, solo, stomach, upper body, white coat, waifu,
Negative prompt: badquality, oldest, realistic,
Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 10, Seed: 3256539262, Size: 576x768, Model hash: 0866b17d46, Model: pvc-v3-fp16, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
</p>
</details>
</div>
</div>
<div class="border dakr:border-gray-750 dark:bg-gray-850 rounded-md overflow-hidden">
<img class="w-full" src="https://s3.amazonaws.com/moonup/production/uploads/1677737218611-6305db1fcfbde33ef7d480ff.jpeg"/>
<div class="px-4 py-2">
<details>
<p class="whitespace-pre-line">
masterpiece, best quality, exceptional, pvc, anime, 1girl, brown hair, school uniform, aqua ribbon, hand up, upper body, looking at viewer, beach, ocean, orange, sky, clouds, sunset,
Negative prompt: badquality, oldest, chibi, simple background, bad anatomy, realistic,
Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 8, Seed: 537083103, Size: 768x576, Model hash: 0866b17d46, Model: pvc-v3-fp16, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
</p>
</details>
</div>
</div>
<div class="border dakr:border-gray-750 dark:bg-gray-850 rounded-md overflow-hidden">
<img class="w-full" src="https://s3.amazonaws.com/moonup/production/uploads/1677742433908-6305db1fcfbde33ef7d480ff.jpeg"/>
<div class="px-4 py-2">
<details>
<p class="whitespace-pre-line">
masterpiece, best quality, exceptional, pvc, anime, 1girl, young, light purple hair, short hair, streaked hair, wavy hair, red eyes, queen, crown, white dress, crossed legs, thighhighs, boots, sitting, close-up, looking at viewer, throne, dark curtains, dark atmosphere
Negative prompt: badquality, oldest, chibi, simple background, bad anatomy, realistic
Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 8, Seed: 3981672289, Size: 768x576, Model hash: 0866b17d46, Model: pvc-v3-fp16, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
</p>
</details>
</div>
</div>
</div>
## Training information
<details>
<table>
<thead>
<tr><th>Parameter</td><td>Value</th></tr>
</thead>
<tbody>
<tr><td>Service</td><td>Runpod</td></tr>
<tr><td>GPU</td><td>A5000</td></tr>
<tr><td>Notebook</td><td><a href="https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-trainer.ipynb" target="_blank">Linaqruf/kohya-trainer</a></td></tr>
<tr><td>Cost</td><td>about $2</td></tr>
<tr><td>Hours</td><td>about 6 hours</td></tr>
<tr><td>Dataset</td><td>7467 images from p1atdev/pvc</td></tr>
<tr><td>Resolution</td><td>896</td></tr>
<tr><td>Epochs</td><td>5</td></tr>
<tr><td>Optimizer</td><td>Lion</td></tr>
<tr><td>LR</td><td>4e-7</td></tr>
<tr><td>Scheduler</td><td>cosine_with_restarts</td></tr>
<tr><td>Train Batch Size</td><td>1</td></tr>
</tbody>
</table>
</details>
## 🧨 Diffusers
Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner.
```bash
pip install diffusers transformers accelerate scipy safetensors
pip install --pre xformers
```
Using StableDiffusionPipeline:
```py
import torch
from diffusers import StableDiffusionPipeline
model_id = "p1atdev/pvc-v3"
revision = "fp16" # "main" or "fp16"
pipe = StableDiffusionPipeline.from_pretrained(
model_id,
revision=revision,
torch_dtype=torch.float16,
)
pipe = pipe.to("cuda")
pipe.enable_attention_slicing()
pipe.enable_xformers_memory_efficient_attention() # required
prompt = "pvc, masterpiece, best quality, exceptional, 1girl, cat ears, red hair, long hair, hairpin, swept bangs, yellow eyes, black jacket, white shirt, blue tie, white gloves, hand up, upper body, looking at viewer, buildings"
negative_prompt = "nsfw, nude, worst quality, low quality, oldest, bad anatomy"
image = pipe(
prompt,
negative_prompt=negative_prompt,
guidance_scale=7.0,
num_inference_steps=20
).images[0]
# save image
image.save("pvc_figure.png")
# or just display it
# display(image)
```
Using StableDiffusionLongPromptWeightingPipeline:
```py
import torch
from diffusers import DiffusionPipeline
model_id = "p1atdev/pvc-v3"
revision = "fp16" # "main" or "fp16"
pipe = DiffusionPipeline.from_pretrained(
model_id,
revision=revision,
torch_dtype=torch.float16,
custom_pipeline="lpw_stable_diffusion"
)
pipe = pipe.to("cuda")
pipe.enable_attention_slicing()
pipe.enable_xformers_memory_efficient_attention() # required
prompt = """
pvc, anime, masterpiece, best quality, exceptional,
1girl, bangs, bare shoulders, beret, black hair, black shorts, blue hair, bracelet, breasts, buttons,
colored inner hair, double-breasted, eyewear removed, green headwear, green jacket, grey eyes, grey sky,
hat, jacket, jewelry, long hair, looking at viewer, multicolored hair, neck ring, o-ring, off shoulder, rain,
round eyewear, shorts, sidelocks, small breasts, solo, sunglasses, wavy hair, wet, zipper
""" # long prompt
negative_prompt = "nsfw, nude, worst quality, low quality, oldest, bad anatomy"
image = pipe(
prompt,
negative_prompt=negative_prompt,
guidance_scale=7.0,
num_inference_steps=20
).images[0]
display(image)
```
## License
PVC v3 is released under the Fair AI Public License 1.0-SD (https://freedevproject.org/faipl-1.0-sd/). If any derivative of this model is made, please share your changes accordingly. Special thanks to ronsor/undeleted (https://undeleted.ronsor.com/) for help with the license.
|
swl-models/CMixS-v1.0
|
swl-models
| 2023-06-21T15:07:35Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T14:56:24Z |
---
license: creativeml-openrail-m
---
|
IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese
|
IDEA-CCNL
| 2023-06-21T15:05:49Z | 39 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"classification",
"zh",
"arxiv:2211.11304",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-10-21T10:08:07Z |
---
language:
- zh
license: apache-2.0
tags:
- classification
inference: false
---
# Erlangshen-TCBert-110M-Classification-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
110M参数的Topic Classification BERT (TCBert)。
The TCBert with 110M parameters is pre-trained for, not limited to, Chinese topic classification tasks.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | TCBert | 110M | Chinese |
## 模型信息 Model Information
为了提高模型在话题分类上的效果,我们收集了大量话题分类数据进行基于prompts的预训练。
To improve the model performance on the topic classification task, we collected numerous topic classification datasets for pre-training based on general prompts.
### 下游效果 Performance
我们为每个数据集设计了两个prompt模板。
We customize two prompts templates for each dataset.
第一个prompt模板:
For ***prompt template 1***:
| Dataset | Prompt template 1 |
|---------|:------------------------:|
| TNEWS | 下面是一则关于__的新闻: |
| CSLDCP | 这一句描述__的内容如下: |
| IFLYTEK | 这一句描述__的内容如下: |
第一个prompt模板的微调实验结果:
The **fine-tuning** results for prompt template 1:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 55.02 | 57.37 | 51.34 |
| Macbert-large | 55.77 | 58.99 | 50.31 |
| Erlangshen-1.3B | 57.36 | 62.35 | 53.23 |
| TCBert-base<sub>110M-Classification-Chinese | 55.57 | 58.60 | 49.63 |
| TCBert-large<sub>330M-Classification-Chinese | 56.17 | 60.06 | 51.34 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 65.10 | 53.75 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 57.46 | 65.04 | 53.06 |
第一个prompt模板的句子相似度结果:
The **sentence similarity** results for prompt template 1:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 43.53 | 47.16 | 33.50 | 36.53 | 28.99 | 33.85 |
| Macbert-large | 46.17 | 49.35 | 37.65 | 39.38 | 32.36 | 35.33 |
| Erlangshen-1.3B | 45.72 | 49.60 | 40.56 | 44.26 | 29.33 | 36.48 |
| TCBert-base<sub>110M-Classification-Chinese | 48.61 | 51.99 | 43.31 | 45.15 | 33.45 | 37.28 |
| TCBert-large<sub>330M-Classification-Chinese | 50.50 | 52.79 | 52.89 | 53.89 | 34.93 | 38.31 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 50.80 | 51.59 | 51.93 | 54.12 | 33.96 | 38.08 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 45.82 | 47.06 | 42.91 | 43.87 | 33.28 | 34.76 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.10 | 50.90 | 53.78 | 53.33 | 37.62 | 36.94 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.70 | 53.48 | 52.66 | 54.40 | 36.88 | 38.48 |
第二个prompt模板:
For ***prompt template 2***:
| Dataset | Prompt template 2 |
|---------|:------------------------:|
| TNEWS | 接下来的新闻,是跟__相关的内容: |
| CSLDCP | 接下来的学科,是跟__相关: |
| IFLYTEK | 接下来的生活内容,是跟__相关: |
第二个prompt模板的微调结果:
The **fine-tuning** results for prompt template 2:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 54.78 | 58.38 | 50.83 |
| Macbert-large | 56.77 | 60.22 | 51.63 |
| Erlangshen-1.3B | 57.81 | 62.80 | 52.77 |
| TCBert-base<sub>110M-Classification-Chinese | 54.58 | 59.16 | 49.80 |
| TCBert-large<sub>330M-Classification-Chinese | 56.22 | 61.23 | 50.77 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 64.82 | 53.34 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 56.87 | 65.83 | 52.94 |
第二个prompt模板的句子相似度结果:
The **sentence similarity** results for prompt template 2:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 42.29 | 45.22 | 34.23 | 37.48 | 29.62 | 34.13 |
| Macbert-large | 46.22 | 49.60 | 40.11 | 44.26 | 32.36 | 35.16 |
| Erlangshen-1.3B | 46.17 | 49.10 | 40.45 | 45.88 | 30.36 | 36.88 |
| TCBert-base<sub>110M-Classification-Chinese | 48.31 | 51.34 | 43.42 | 45.27 | 33.10 | 36.19 |
| TCBert-large<sub>330M-Classification-Chinese | 51.19 | 51.69 | 52.55 | 53.28 | 34.31 | 37.45 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 52.14 | 52.39 | 51.71 | 53.89 | 33.62 | 38.14 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 46.72 | 48.86 | 43.19 | 43.53 | 34.08 | 35.79 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.65 | 51.94 | 53.84 | 53.67 | 37.74 | 36.65 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.75 | 54.78 | 51.43 | 54.34 | 36.48 | 38.36 |
更多关于TCBERTs的细节,请参考我们的技术报告。基于新的数据,我们会更新TCBERTs,请留意我们仓库的更新。
For more details about TCBERTs, please refer to our paper. We may regularly update TCBERTs upon new coming data, please keep an eye on the repo!
## 使用 Usage
### 使用示例 Usage Examples
```python
# Prompt-based MLM fine-tuning
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese")
# Prepare the data
inputs = tokenizer("下面是一则关于[MASK][MASK]的新闻:怎样的房子才算户型方正?", return_tensors="pt")
labels = tokenizer("下面是一则关于房产的新闻:怎样的房子才算户型方正?", return_tensors="pt")["input_ids"]
labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
# Output the loss
outputs = model(**inputs, labels=labels)
loss = outputs.loss
```
```python
# Prompt-based Sentence Similarity
# To extract sentence representations.
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese")
# Cosine similarity function
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8)
with torch.no_grad():
# To extract sentence representations for training data
training_input = tokenizer("怎样的房子才算户型方正?", return_tensors="pt")
training_output = BertForMaskedLM(**token_text, output_hidden_states=True)
training_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# To extract sentence representations for training data
test_input = tokenizer("下面是一则关于[MASK][MASK]的新闻:股票放量下趺,大资金出逃谁在接盘?", return_tensors="pt")
test_output = BertForMaskedLM(**token_text, output_hidden_states=True)
test_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# Calculate similarity scores
similarity_score = cos(training_representation, test_representation)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型,可以引用我们的[技术报告](https://arxiv.org/abs/2211.11304):
If you use for your work, please cite the following paper
```
@article{han2022tcbert,
title={TCBERT: A Technical Report for Chinese Topic Classification BERT},
author={Han, Ting and Pan, Kunhao and Chen, Xinyu and Song, Dingjie and Fan, Yuchen and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing},
journal={arXiv preprint arXiv:2211.11304},
year={2022}
}
```
如果您在您的工作中使用了我们的模型,可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
IDEA-CCNL/Erlangshen-TCBert-1.3B-Classification-Chinese
|
IDEA-CCNL
| 2023-06-21T15:05:14Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"classification",
"zh",
"arxiv:2211.11304",
"license:apache-2.0",
"region:us"
] | null | 2022-10-21T10:30:50Z |
---
language:
- zh
license: apache-2.0
tags:
- classification
inference: false
---
# Erlangshen-TCBert-1.3B-Classification-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
1.3BM参数的Topic Classification BERT (TCBert)。
The TCBert with 1.3BM parameters is pre-trained for, not limited to, Chinese topic classification tasks.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | TCBert | 1.3BM | Chinese |
## 模型信息 Model Information
为了提高模型在话题分类上的效果,我们收集了大量话题分类数据进行基于prompts的预训练。
To improve the model performance on the topic classification task, we collected numerous topic classification datasets for pre-training based on general prompts.
### 下游效果 Performance
我们为每个数据集设计了两个prompt模板。
We customize two prompts templates for each dataset.
第一个prompt模板:
For ***prompt template 1***:
| Dataset | Prompt template 1 |
|---------|:------------------------:|
| TNEWS | 下面是一则关于__的新闻: |
| CSLDCP | 这一句描述__的内容如下: |
| IFLYTEK | 这一句描述__的内容如下: |
第一个prompt模板的微调实验结果:
The **fine-tuning** results for prompt template 1:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 55.02 | 57.37 | 51.34 |
| Macbert-large | 55.77 | 58.99 | 50.31 |
| Erlangshen-1.3B | 57.36 | 62.35 | 53.23 |
| TCBert-base<sub>110M-Classification-Chinese | 55.57 | 58.60 | 49.63 |
| TCBert-large<sub>330M-Classification-Chinese | 56.17 | 60.06 | 51.34 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 65.10 | 53.75 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 57.46 | 65.04 | 53.06 |
第一个prompt模板的句子相似度结果:
The **sentence similarity** results for prompt template 1:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 43.53 | 47.16 | 33.50 | 36.53 | 28.99 | 33.85 |
| Macbert-large | 46.17 | 49.35 | 37.65 | 39.38 | 32.36 | 35.33 |
| Erlangshen-1.3B | 45.72 | 49.60 | 40.56 | 44.26 | 29.33 | 36.48 |
| TCBert-base<sub>110M-Classification-Chinese | 48.61 | 51.99 | 43.31 | 45.15 | 33.45 | 37.28 |
| TCBert-large<sub>330M-Classification-Chinese | 50.50 | 52.79 | 52.89 | 53.89 | 34.93 | 38.31 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 50.80 | 51.59 | 51.93 | 54.12 | 33.96 | 38.08 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 45.82 | 47.06 | 42.91 | 43.87 | 33.28 | 34.76 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.10 | 50.90 | 53.78 | 53.33 | 37.62 | 36.94 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.70 | 53.48 | 52.66 | 54.40 | 36.88 | 38.48 |
第二个prompt模板:
For ***prompt template 2***:
| Dataset | Prompt template 2 |
|---------|:------------------------:|
| TNEWS | 接下来的新闻,是跟__相关的内容: |
| CSLDCP | 接下来的学科,是跟__相关: |
| IFLYTEK | 接下来的生活内容,是跟__相关: |
第二个prompt模板的微调结果:
The **fine-tuning** results for prompt template 2:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 54.78 | 58.38 | 50.83 |
| Macbert-large | 56.77 | 60.22 | 51.63 |
| Erlangshen-1.3B | 57.81 | 62.80 | 52.77 |
| TCBert-base<sub>110M-Classification-Chinese | 54.58 | 59.16 | 49.80 |
| TCBert-large<sub>330M-Classification-Chinese | 56.22 | 61.23 | 50.77 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 64.82 | 53.34 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 56.87 | 65.83 | 52.94 |
第二个prompt模板的句子相似度结果:
The **sentence similarity** results for prompt template 2:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 42.29 | 45.22 | 34.23 | 37.48 | 29.62 | 34.13 |
| Macbert-large | 46.22 | 49.60 | 40.11 | 44.26 | 32.36 | 35.16 |
| Erlangshen-1.3B | 46.17 | 49.10 | 40.45 | 45.88 | 30.36 | 36.88 |
| TCBert-base<sub>110M-Classification-Chinese | 48.31 | 51.34 | 43.42 | 45.27 | 33.10 | 36.19 |
| TCBert-large<sub>330M-Classification-Chinese | 51.19 | 51.69 | 52.55 | 53.28 | 34.31 | 37.45 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 52.14 | 52.39 | 51.71 | 53.89 | 33.62 | 38.14 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 46.72 | 48.86 | 43.19 | 43.53 | 34.08 | 35.79 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.65 | 51.94 | 53.84 | 53.67 | 37.74 | 36.65 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.75 | 54.78 | 51.43 | 54.34 | 36.48 | 38.36 |
更多关于TCBERTs的细节,请参考我们的技术报告。基于新的数据,我们会更新TCBERTs,请留意我们仓库的更新。
For more details about TCBERTs, please refer to our paper. We may regularly update TCBERTs upon new coming data, please keep an eye on the repo!
## 使用 Usage
### 使用示例 Usage Examples
```python
# Prompt-based MLM fine-tuning
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Classification-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Classification-Chinese")
# Prepare the data
inputs = tokenizer("下面是一则关于[MASK][MASK]的新闻:怎样的房子才算户型方正?", return_tensors="pt")
labels = tokenizer("下面是一则关于房产的新闻:怎样的房子才算户型方正?", return_tensors="pt")["input_ids"]
labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
# Output the loss
outputs = model(**inputs, labels=labels)
loss = outputs.loss
```
```python
# Prompt-based Sentence Similarity
# To extract sentence representations.
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Classification-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Classification-Chinese")
# Cosine similarity function
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8)
with torch.no_grad():
# To extract sentence representations for training data
training_input = tokenizer("怎样的房子才算户型方正?", return_tensors="pt")
training_output = BertForMaskedLM(**token_text, output_hidden_states=True)
training_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# To extract sentence representations for training data
test_input = tokenizer("下面是一则关于[MASK][MASK]的新闻:股票放量下趺,大资金出逃谁在接盘?", return_tensors="pt")
test_output = BertForMaskedLM(**token_text, output_hidden_states=True)
test_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# Calculate similarity scores
similarity_score = cos(training_representation, test_representation)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型,可以引用我们的[技术报告](https://arxiv.org/abs/2211.11304):
If you use for your work, please cite the following paper
```
@article{han2022tcbert,
title={TCBERT: A Technical Report for Chinese Topic Classification BERT},
author={Han, Ting and Pan, Kunhao and Chen, Xinyu and Song, Dingjie and Fan, Yuchen and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing},
journal={arXiv preprint arXiv:2211.11304},
year={2022}
}
```
如果您在您的工作中使用了我们的模型,可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
IDEA-CCNL/Erlangshen-TCBert-330M-Classification-Chinese
|
IDEA-CCNL
| 2023-06-21T15:04:40Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"classification",
"zh",
"arxiv:2211.11304",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-10-21T10:29:37Z |
---
language:
- zh
license: apache-2.0
tags:
- classification
inference: false
---
# Erlangshen-TCBert-330M-Classification-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
330M参数的Topic Classification BERT (TCBert)。
The TCBert with 330M parameters is pre-trained for, not limited to, Chinese topic classification tasks.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | TCBert | 330M | Chinese |
## 模型信息 Model Information
为了提高模型在话题分类上的效果,我们收集了大量话题分类数据进行基于prompts的预训练。
To improve the model performance on the topic classification task, we collected numerous topic classification datasets for pre-training based on general prompts.
### 下游效果 Performance
我们为每个数据集设计了两个prompt模板。
We customize two prompts templates for each dataset.
第一个prompt模板:
For ***prompt template 1***:
| Dataset | Prompt template 1 |
|---------|:------------------------:|
| TNEWS | 下面是一则关于__的新闻: |
| CSLDCP | 这一句描述__的内容如下: |
| IFLYTEK | 这一句描述__的内容如下: |
第一个prompt模板的微调实验结果:
The **fine-tuning** results for prompt template 1:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 55.02 | 57.37 | 51.34 |
| Macbert-large | 55.77 | 58.99 | 50.31 |
| Erlangshen-1.3B | 57.36 | 62.35 | 53.23 |
| TCBert-base<sub>110M-Classification-Chinese | 55.57 | 58.60 | 49.63 |
| TCBert-large<sub>330M-Classification-Chinese | 56.17 | 60.06 | 51.34 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 65.10 | 53.75 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 57.46 | 65.04 | 53.06 |
第一个prompt模板的句子相似度结果:
The **sentence similarity** results for prompt template 1:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 43.53 | 47.16 | 33.50 | 36.53 | 28.99 | 33.85 |
| Macbert-large | 46.17 | 49.35 | 37.65 | 39.38 | 32.36 | 35.33 |
| Erlangshen-1.3B | 45.72 | 49.60 | 40.56 | 44.26 | 29.33 | 36.48 |
| TCBert-base<sub>110M-Classification-Chinese | 48.61 | 51.99 | 43.31 | 45.15 | 33.45 | 37.28 |
| TCBert-large<sub>330M-Classification-Chinese | 50.50 | 52.79 | 52.89 | 53.89 | 34.93 | 38.31 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 50.80 | 51.59 | 51.93 | 54.12 | 33.96 | 38.08 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 45.82 | 47.06 | 42.91 | 43.87 | 33.28 | 34.76 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.10 | 50.90 | 53.78 | 53.33 | 37.62 | 36.94 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.70 | 53.48 | 52.66 | 54.40 | 36.88 | 38.48 |
第二个prompt模板:
For ***prompt template 2***:
| Dataset | Prompt template 2 |
|---------|:------------------------:|
| TNEWS | 接下来的新闻,是跟__相关的内容: |
| CSLDCP | 接下来的学科,是跟__相关: |
| IFLYTEK | 接下来的生活内容,是跟__相关: |
第二个prompt模板的微调结果:
The **fine-tuning** results for prompt template 2:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 54.78 | 58.38 | 50.83 |
| Macbert-large | 56.77 | 60.22 | 51.63 |
| Erlangshen-1.3B | 57.81 | 62.80 | 52.77 |
| TCBert-base<sub>110M-Classification-Chinese | 54.58 | 59.16 | 49.80 |
| TCBert-large<sub>330M-Classification-Chinese | 56.22 | 61.23 | 50.77 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 64.82 | 53.34 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 56.87 | 65.83 | 52.94 |
第二个prompt模板的句子相似度结果:
The **sentence similarity** results for prompt template 2:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 42.29 | 45.22 | 34.23 | 37.48 | 29.62 | 34.13 |
| Macbert-large | 46.22 | 49.60 | 40.11 | 44.26 | 32.36 | 35.16 |
| Erlangshen-1.3B | 46.17 | 49.10 | 40.45 | 45.88 | 30.36 | 36.88 |
| TCBert-base<sub>110M-Classification-Chinese | 48.31 | 51.34 | 43.42 | 45.27 | 33.10 | 36.19 |
| TCBert-large<sub>330M-Classification-Chinese | 51.19 | 51.69 | 52.55 | 53.28 | 34.31 | 37.45 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 52.14 | 52.39 | 51.71 | 53.89 | 33.62 | 38.14 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 46.72 | 48.86 | 43.19 | 43.53 | 34.08 | 35.79 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.65 | 51.94 | 53.84 | 53.67 | 37.74 | 36.65 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.75 | 54.78 | 51.43 | 54.34 | 36.48 | 38.36 |
更多关于TCBERTs的细节,请参考我们的技术报告。基于新的数据,我们会更新TCBERTs,请留意我们仓库的更新。
For more details about TCBERTs, please refer to our paper. We may regularly update TCBERTs upon new coming data, please keep an eye on the repo!
## 使用 Usage
### 使用示例 Usage Examples
```python
# Prompt-based MLM fine-tuning
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Classification-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Classification-Chinese")
# Prepare the data
inputs = tokenizer("下面是一则关于[MASK][MASK]的新闻:怎样的房子才算户型方正?", return_tensors="pt")
labels = tokenizer("下面是一则关于房产的新闻:怎样的房子才算户型方正?", return_tensors="pt")["input_ids"]
labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
# Output the loss
outputs = model(**inputs, labels=labels)
loss = outputs.loss
```
```python
# Prompt-based Sentence Similarity
# To extract sentence representations.
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Classification-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Classification-Chinese")
# Cosine similarity function
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8)
with torch.no_grad():
# To extract sentence representations for training data
training_input = tokenizer("怎样的房子才算户型方正?", return_tensors="pt")
training_output = BertForMaskedLM(**token_text, output_hidden_states=True)
training_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# To extract sentence representations for training data
test_input = tokenizer("下面是一则关于[MASK][MASK]的新闻:股票放量下趺,大资金出逃谁在接盘?", return_tensors="pt")
test_output = BertForMaskedLM(**token_text, output_hidden_states=True)
test_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# Calculate similarity scores
similarity_score = cos(training_representation, test_representation)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型,可以引用我们的[技术报告](https://arxiv.org/abs/2211.11304):
If you use for your work, please cite the following paper
```
@article{han2022tcbert,
title={TCBERT: A Technical Report for Chinese Topic Classification BERT},
author={Han, Ting and Pan, Kunhao and Chen, Xinyu and Song, Dingjie and Fan, Yuchen and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing},
journal={arXiv preprint arXiv:2211.11304},
year={2022}
}
```
如果您在您的工作中使用了我们的模型,可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese
|
IDEA-CCNL
| 2023-06-21T15:03:22Z | 47 | 5 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"classification",
"zh",
"arxiv:2211.11304",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-10-21T10:27:40Z |
---
language:
- zh
license: apache-2.0
tags:
- classification
inference: false
---
# IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
110M参数的句子表征Topic Classification BERT (TCBert)。
The TCBert with 110M parameters is pre-trained for sentence representation for Chinese topic classification tasks.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 句子表征 | 二郎神 Erlangshen | TCBert (sentence representation) | 110M | Chinese |
## 模型信息 Model Information
为了提高模型在话题分类上句子表征效果,我们收集了大量话题分类数据进行基于prompts的对比学习预训练。
To improve the model performance on sentence representation for the topic classification task, we collected numerous topic classification datasets for contrastive pre-training based on general prompts.
### 下游效果 Performance
我们为每个数据集设计了两个prompt模板。
We customize two prompts templates for each dataset.
第一个prompt模板:
For ***prompt template 1***:
| Dataset | Prompt template 1 |
|---------|:------------------------:|
| TNEWS | 下面是一则关于__的新闻: |
| CSLDCP | 这一句描述__的内容如下: |
| IFLYTEK | 这一句描述__的内容如下: |
第一个prompt模板的微调实验结果:
The **fine-tuning** results for prompt template 1:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 55.02 | 57.37 | 51.34 |
| Macbert-large | 55.77 | 58.99 | 50.31 |
| Erlangshen-1.3B | 57.36 | 62.35 | 53.23 |
| TCBert-base<sub>110M-Classification-Chinese | 55.57 | 58.60 | 49.63 |
| TCBert-large<sub>330M-Classification-Chinese | 56.17 | 60.06 | 51.34 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 65.10 | 53.75 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 57.46 | 65.04 | 53.06 |
第一个prompt模板的句子相似度结果:
The **sentence similarity** results for prompt template 1:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 43.53 | 47.16 | 33.50 | 36.53 | 28.99 | 33.85 |
| Macbert-large | 46.17 | 49.35 | 37.65 | 39.38 | 32.36 | 35.33 |
| Erlangshen-1.3B | 45.72 | 49.60 | 40.56 | 44.26 | 29.33 | 36.48 |
| TCBert-base<sub>110M-Classification-Chinese | 48.61 | 51.99 | 43.31 | 45.15 | 33.45 | 37.28 |
| TCBert-large<sub>330M-Classification-Chinese | 50.50 | 52.79 | 52.89 | 53.89 | 34.93 | 38.31 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 50.80 | 51.59 | 51.93 | 54.12 | 33.96 | 38.08 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 45.82 | 47.06 | 42.91 | 43.87 | 33.28 | 34.76 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.10 | 50.90 | 53.78 | 53.33 | 37.62 | 36.94 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.70 | 53.48 | 52.66 | 54.40 | 36.88 | 38.48 |
第二个prompt模板:
For ***prompt template 2***:
| Dataset | Prompt template 2 |
|---------|:------------------------:|
| TNEWS | 接下来的新闻,是跟__相关的内容: |
| CSLDCP | 接下来的学科,是跟__相关: |
| IFLYTEK | 接下来的生活内容,是跟__相关: |
第二个prompt模板的微调结果:
The **fine-tuning** results for prompt template 2:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 54.78 | 58.38 | 50.83 |
| Macbert-large | 56.77 | 60.22 | 51.63 |
| Erlangshen-1.3B | 57.81 | 62.80 | 52.77 |
| TCBert-base<sub>110M-Classification-Chinese | 54.58 | 59.16 | 49.80 |
| TCBert-large<sub>330M-Classification-Chinese | 56.22 | 61.23 | 50.77 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 64.82 | 53.34 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 56.87 | 65.83 | 52.94 |
第二个prompt模板的句子相似度结果:
The **sentence similarity** results for prompt template 2:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 42.29 | 45.22 | 34.23 | 37.48 | 29.62 | 34.13 |
| Macbert-large | 46.22 | 49.60 | 40.11 | 44.26 | 32.36 | 35.16 |
| Erlangshen-1.3B | 46.17 | 49.10 | 40.45 | 45.88 | 30.36 | 36.88 |
| TCBert-base<sub>110M-Classification-Chinese | 48.31 | 51.34 | 43.42 | 45.27 | 33.10 | 36.19 |
| TCBert-large<sub>330M-Classification-Chinese | 51.19 | 51.69 | 52.55 | 53.28 | 34.31 | 37.45 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 52.14 | 52.39 | 51.71 | 53.89 | 33.62 | 38.14 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 46.72 | 48.86 | 43.19 | 43.53 | 34.08 | 35.79 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.65 | 51.94 | 53.84 | 53.67 | 37.74 | 36.65 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.75 | 54.78 | 51.43 | 54.34 | 36.48 | 38.36 |
更多关于TCBERTs的细节,请参考我们的技术报告。基于新的数据,我们会更新TCBERTs,请留意我们仓库的更新。
For more details about TCBERTs, please refer to our paper. We may regularly update TCBERTs upon new coming data, please keep an eye on the repo!
## 使用 Usage
### 使用示例 Usage Examples
```python
# Prompt-based MLM fine-tuning
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese")
# Prepare the data
inputs = tokenizer("下面是一则关于[MASK][MASK]的新闻:怎样的房子才算户型方正?", return_tensors="pt")
labels = tokenizer("下面是一则关于房产的新闻:怎样的房子才算户型方正?", return_tensors="pt")["input_ids"]
labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
# Output the loss
outputs = model(**inputs, labels=labels)
loss = outputs.loss
```
```python
# Prompt-based Sentence Similarity
# To extract sentence representations.
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese")
# Cosine similarity function
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8)
with torch.no_grad():
# To extract sentence representations for training data
training_input = tokenizer("怎样的房子才算户型方正?", return_tensors="pt")
training_output = BertForMaskedLM(**token_text, output_hidden_states=True)
training_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# To extract sentence representations for training data
test_input = tokenizer("下面是一则关于[MASK][MASK]的新闻:股票放量下趺,大资金出逃谁在接盘?", return_tensors="pt")
test_output = BertForMaskedLM(**token_text, output_hidden_states=True)
test_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# Calculate similarity scores
similarity_score = cos(training_representation, test_representation)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型,可以引用我们的[技术报告](https://arxiv.org/abs/2211.11304):
If you use for your work, please cite the following paper
```
@article{han2022tcbert,
title={TCBERT: A Technical Report for Chinese Topic Classification BERT},
author={Han, Ting and Pan, Kunhao and Chen, Xinyu and Song, Dingjie and Fan, Yuchen and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing},
journal={arXiv preprint arXiv:2211.11304},
year={2022}
}
```
如果您在您的工作中使用了我们的模型,可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese
|
IDEA-CCNL
| 2023-06-21T15:01:26Z | 344 | 9 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"classification",
"zh",
"arxiv:2211.11304",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-10-22T05:47:52Z |
---
language:
- zh
license: apache-2.0
tags:
- classification
inference: false
---
# IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
330M参数的句子表征Topic Classification BERT (TCBert)。
The TCBert with 330M parameters is pre-trained for sentence representation for Chinese topic classification tasks.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 句子表征 | 二郎神 Erlangshen | TCBert (sentence representation) | 330M | Chinese |
## 模型信息 Model Information
为了提高模型在话题分类上句子表征效果,我们收集了大量话题分类数据进行基于prompts的对比学习预训练。
To improve the model performance on sentence representation for the topic classification task, we collected numerous topic classification datasets for contrastive pre-training based on general prompts.
### 下游效果 Performance
我们为每个数据集设计了两个prompt模板。
We customize two prompts templates for each dataset.
第一个prompt模板:
For ***prompt template 1***:
| Dataset | Prompt template 1 |
|---------|:------------------------:|
| TNEWS | 下面是一则关于__的新闻: |
| CSLDCP | 这一句描述__的内容如下: |
| IFLYTEK | 这一句描述__的内容如下: |
第一个prompt模板的微调实验结果:
The **fine-tuning** results for prompt template 1:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 55.02 | 57.37 | 51.34 |
| Macbert-large | 55.77 | 58.99 | 50.31 |
| Erlangshen-1.3B | 57.36 | 62.35 | 53.23 |
| TCBert-base<sub>110M-Classification-Chinese | 55.57 | 58.60 | 49.63 |
| TCBert-large<sub>330M-Classification-Chinese | 56.17 | 60.06 | 51.34 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 65.10 | 53.75 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 57.46 | 65.04 | 53.06 |
第一个prompt模板的句子相似度结果:
The **sentence similarity** results for prompt template 1:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 43.53 | 47.16 | 33.50 | 36.53 | 28.99 | 33.85 |
| Macbert-large | 46.17 | 49.35 | 37.65 | 39.38 | 32.36 | 35.33 |
| Erlangshen-1.3B | 45.72 | 49.60 | 40.56 | 44.26 | 29.33 | 36.48 |
| TCBert-base<sub>110M-Classification-Chinese | 48.61 | 51.99 | 43.31 | 45.15 | 33.45 | 37.28 |
| TCBert-large<sub>330M-Classification-Chinese | 50.50 | 52.79 | 52.89 | 53.89 | 34.93 | 38.31 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 50.80 | 51.59 | 51.93 | 54.12 | 33.96 | 38.08 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 45.82 | 47.06 | 42.91 | 43.87 | 33.28 | 34.76 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.10 | 50.90 | 53.78 | 53.33 | 37.62 | 36.94 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.70 | 53.48 | 52.66 | 54.40 | 36.88 | 38.48 |
第二个prompt模板:
For ***prompt template 2***:
| Dataset | Prompt template 2 |
|---------|:------------------------:|
| TNEWS | 接下来的新闻,是跟__相关的内容: |
| CSLDCP | 接下来的学科,是跟__相关: |
| IFLYTEK | 接下来的生活内容,是跟__相关: |
第二个prompt模板的微调结果:
The **fine-tuning** results for prompt template 2:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 54.78 | 58.38 | 50.83 |
| Macbert-large | 56.77 | 60.22 | 51.63 |
| Erlangshen-1.3B | 57.81 | 62.80 | 52.77 |
| TCBert-base<sub>110M-Classification-Chinese | 54.58 | 59.16 | 49.80 |
| TCBert-large<sub>330M-Classification-Chinese | 56.22 | 61.23 | 50.77 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 64.82 | 53.34 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 56.87 | 65.83 | 52.94 |
第二个prompt模板的句子相似度结果:
The **sentence similarity** results for prompt template 2:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 42.29 | 45.22 | 34.23 | 37.48 | 29.62 | 34.13 |
| Macbert-large | 46.22 | 49.60 | 40.11 | 44.26 | 32.36 | 35.16 |
| Erlangshen-1.3B | 46.17 | 49.10 | 40.45 | 45.88 | 30.36 | 36.88 |
| TCBert-base<sub>110M-Classification-Chinese | 48.31 | 51.34 | 43.42 | 45.27 | 33.10 | 36.19 |
| TCBert-large<sub>330M-Classification-Chinese | 51.19 | 51.69 | 52.55 | 53.28 | 34.31 | 37.45 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 52.14 | 52.39 | 51.71 | 53.89 | 33.62 | 38.14 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 46.72 | 48.86 | 43.19 | 43.53 | 34.08 | 35.79 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.65 | 51.94 | 53.84 | 53.67 | 37.74 | 36.65 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.75 | 54.78 | 51.43 | 54.34 | 36.48 | 38.36 |
更多关于TCBERTs的细节,请参考我们的技术报告。基于新的数据,我们会更新TCBERTs,请留意我们仓库的更新。
For more details about TCBERTs, please refer to our paper. We may regularly update TCBERTs upon new coming data, please keep an eye on the repo!
## 使用 Usage
### 使用示例 Usage Examples
```python
# Prompt-based MLM fine-tuning
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese")
# Prepare the data
inputs = tokenizer("下面是一则关于[MASK][MASK]的新闻:怎样的房子才算户型方正?", return_tensors="pt")
labels = tokenizer("下面是一则关于房产的新闻:怎样的房子才算户型方正?", return_tensors="pt")["input_ids"]
labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
# Output the loss
outputs = model(**inputs, labels=labels)
loss = outputs.loss
```
```python
# Prompt-based Sentence Similarity
# To extract sentence representations.
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese")
# Cosine similarity function
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8)
with torch.no_grad():
# To extract sentence representations for training data
training_input = tokenizer("怎样的房子才算户型方正?", return_tensors="pt")
training_output = BertForMaskedLM(**token_text, output_hidden_states=True)
training_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# To extract sentence representations for training data
test_input = tokenizer("下面是一则关于[MASK][MASK]的新闻:股票放量下趺,大资金出逃谁在接盘?", return_tensors="pt")
test_output = BertForMaskedLM(**token_text, output_hidden_states=True)
test_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# Calculate similarity scores
similarity_score = cos(training_representation, test_representation)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型,可以引用我们的[技术报告](https://arxiv.org/abs/2211.11304):
If you use for your work, please cite the following paper
```
@article{han2022tcbert,
title={TCBERT: A Technical Report for Chinese Topic Classification BERT},
author={Han, Ting and Pan, Kunhao and Chen, Xinyu and Song, Dingjie and Fan, Yuchen and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing},
journal={arXiv preprint arXiv:2211.11304},
year={2022}
}
```
如果您在您的工作中使用了我们的模型,可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
SRDdev/QABERT-small
|
SRDdev
| 2023-06-21T15:00:00Z | 70 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"distilbert",
"question-answering",
"en",
"dataset:squad_v2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-08T12:40:31Z |
---
datasets:
- squad_v2
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: question-answering
tags:
- question-answering
---
# QA-BERT
QA-BERT is a Question Answering Model. This model is a lighter version of any of the question-answering models out there.
## Dataset
The Stanford Question Answering Dataset (SQuAD) is a widely used benchmark dataset for the task of machine reading comprehension. It consists of over 100,000 question-answer pairs based on a set of Wikipedia articles. The goal is to train models that can answer questions based on their understanding of the given text passages. SQuAD has played a significant role in advancing the state-of-the-art in this field and remains a popular choice for researchers and practitioners alike.
Due to GPU limitations, this version is trained on `30k samples` from the Stanford Question Answering Dataset.
<details>
<summary><i>Structure of the Data Dictonary</i></summary>
<!--All you need is a blank line-->
{
"data":[
{
"title":"Article Title",
"paragraphs":[
{
"context":"The context text of the paragraph",
"qas":[
{
"question":"The question asked about the context",
"id":"A unique identifier for the question",
"answers":[
{
"text":"The answer to the question",
"answer_start":"The starting index of the answer in the context"
}
]
}
]
}
]
}
],
"version":"The version of the SQuAD dataset"
}
</details>
## Model
BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based model for natural language processing tasks such as question answering. BERT is fine-tuned for question answering by adding a linear layer on top of the pre-trained BERT representations to predict the start and end of the answer in the input context. BERT has achieved state-of-the-art results on multiple benchmark datasets, including the Stanford Question Answering Dataset (SQuAD). The fine-tuning process allows BERT to effectively capture the relationships between questions and answers and generate accurate answers.
<img src="https://imgs.search.brave.com/F8m-nwp6EIG5vq--OmJLrCDpIkuX6tEQ_kyFKQjlUTs/rs:fit:1200:1200:1/g:ce/aHR0cHM6Ly9ibG9n/LmdyaWRkeW5hbWlj/cy5jb20vY29udGVu/dC9pbWFnZXMvMjAy/MC8xMC9TbGljZS0x/OC5wbmc">
For more detail about this read [Understanding QABERT](https://github.com/SRDdev/AnswerMind)
## Inference
_Load model_
```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
QAtokenizer = AutoTokenizer.from_pretrained("SRDdev/QABERT-small")
QAmodel = AutoModelForQuestionAnswering.from_pretrained("SRDdev/QABERT-small")
```
_context_
```text
Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
question-answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.
```
_Build Pipeline_
```python
from transformers import pipeline
ask = pipeline("question-answering", model= QAmodel , tokenizer = QAtokenizer)
result = ask(question="What is a good example of a question answering dataset?", context=context)
print(f"Answer: '{result['answer']}'")
```
## Contributing
Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.
Please make sure to update tests as appropriate.
## Citations
```
@citation{ QA-BERT-small,
author = {Shreyas Dixit},
year = {2023},
url = {https://huggingface.co/SRDdev/QA-BERT-small}
}
```
|
helenpy/distilbert-base-uncased-finetuned-tass
|
helenpy
| 2023-06-21T14:44:11Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-21T14:41:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-tass
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. -->
# distilbert-base-uncased-finetuned-tass
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9866
- Accuracy: 0.5170
## 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: 12
- eval_batch_size: 12
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9944 | 1.0 | 401 | 1.0210 | 0.4761 |
| 0.8994 | 2.0 | 802 | 0.9866 | 0.5170 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
KoRiF/codeparrot-ds
|
KoRiF
| 2023-06-21T14:43:58Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T13:33:13Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
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. -->
# 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
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
tux/LunarLanderV2_ppo_from_scratch
|
tux
| 2023-06-21T14:36:38Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T14:32:40Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -151.29 +/- 39.57
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
EleutherAI/gpt-j-6b
|
EleutherAI
| 2023-06-21T14:33:36Z | 256,005 | 1,477 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gptj",
"text-generation",
"causal-lm",
"en",
"dataset:EleutherAI/pile",
"arxiv:2104.09864",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language:
- en
tags:
- pytorch
- causal-lm
license: apache-2.0
datasets:
- EleutherAI/pile
---
# GPT-J 6B
## Model Description
GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
<figure>
| Hyperparameter | Value |
|----------------------|------------|
| \\(n_{parameters}\\) | 6053381344 |
| \\(n_{layers}\\) | 28* |
| \\(d_{model}\\) | 4096 |
| \\(d_{ff}\\) | 16384 |
| \\(n_{heads}\\) | 16 |
| \\(d_{head}\\) | 256 |
| \\(n_{ctx}\\) | 2048 |
| \\(n_{vocab}\\) | 50257/50400† (same tokenizer as GPT-2/3) |
| Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) |
| RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
<figcaption><p><strong>*</strong> Each layer consists of one feedforward block and one self attention block.</p>
<p><strong>†</strong> Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer.</p></figcaption></figure>
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64
dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
GPT-2/GPT-3.
## Intended Use and Limitations
GPT-J learns an inner representation of the English language that can be used to
extract features useful for downstream tasks. The model is best at what it was
pretrained for however, which is generating text from a prompt.
### Out-of-scope use
GPT-J-6B is **not** intended for deployment without fine-tuning, supervision,
and/or moderation. It is not a in itself a product and cannot be used for
human-facing interactions. For example, the model may generate harmful or
offensive text. Please evaluate the risks associated with your particular use case.
GPT-J-6B was trained on an English-language only dataset, and is thus **not**
suitable for translation or generating text in other languages.
GPT-J-6B has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means GPT-J-6B will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and Biases
The core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon GPT-J to produce factually accurate output.
GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See [Sections 5 and 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
### How to use
This model can be easily loaded using the `AutoModelForCausalLM` functionality:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
```
## Training data
GPT-J 6B was trained on [the Pile](https://pile.eleuther.ai), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai).
## Training procedure
This model was trained for 402 billion tokens over 383,500 steps on TPU v3-256 pod. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly.
## Evaluation results
<figure>
| Model | Public | Training FLOPs | LAMBADA PPL ↓ | LAMBADA Acc ↑ | Winogrande ↑ | Hellaswag ↑ | PIQA ↑ | Dataset Size (GB) |
|--------------------------|-------------|----------------|--- |--- |--- |--- |--- |-------------------|
| Random Chance | ✓ | 0 | ~a lot | ~0% | 50% | 25% | 25% | 0 |
| GPT-3 Ada‡ | ✗ | ----- | 9.95 | 51.6% | 52.9% | 43.4% | 70.5% | ----- |
| GPT-2 1.5B | ✓ | ----- | 10.63 | 51.21% | 59.4% | 50.9% | 70.8% | 40 |
| GPT-Neo 1.3B‡ | ✓ | 3.0e21 | 7.50 | 57.2% | 55.0% | 48.9% | 71.1% | 825 |
| Megatron-2.5B* | ✗ | 2.4e21 | ----- | 61.7% | ----- | ----- | ----- | 174 |
| GPT-Neo 2.7B‡ | ✓ | 6.8e21 | 5.63 | 62.2% | 56.5% | 55.8% | 73.0% | 825 |
| GPT-3 1.3B*‡ | ✗ | 2.4e21 | 5.44 | 63.6% | 58.7% | 54.7% | 75.1% | ~800 |
| GPT-3 Babbage‡ | ✗ | ----- | 5.58 | 62.4% | 59.0% | 54.5% | 75.5% | ----- |
| Megatron-8.3B* | ✗ | 7.8e21 | ----- | 66.5% | ----- | ----- | ----- | 174 |
| GPT-3 2.7B*‡ | ✗ | 4.8e21 | 4.60 | 67.1% | 62.3% | 62.8% | 75.6% | ~800 |
| Megatron-11B† | ✓ | 1.0e22 | ----- | ----- | ----- | ----- | ----- | 161 |
| **GPT-J 6B‡** | **✓** | **1.5e22** | **3.99** | **69.7%** | **65.3%** | **66.1%** | **76.5%** | **825** |
| GPT-3 6.7B*‡ | ✗ | 1.2e22 | 4.00 | 70.3% | 64.5% | 67.4% | 78.0% | ~800 |
| GPT-3 Curie‡ | ✗ | ----- | 4.00 | 69.3% | 65.6% | 68.5% | 77.9% | ----- |
| GPT-3 13B*‡ | ✗ | 2.3e22 | 3.56 | 72.5% | 67.9% | 70.9% | 78.5% | ~800 |
| GPT-3 175B*‡ | ✗ | 3.1e23 | 3.00 | 76.2% | 70.2% | 78.9% | 81.0% | ~800 |
| GPT-3 Davinci‡ | ✗ | ----- | 3.0 | 75% | 72% | 78% | 80% | ----- |
<figcaption><p>Models roughly sorted by performance, or by FLOPs if not available.</p>
<p><strong>*</strong> Evaluation numbers reported by their respective authors. All other numbers are provided by
running <a href="https://github.com/EleutherAI/lm-evaluation-harness/"><code>lm-evaluation-harness</code></a> either with released
weights or with API access. Due to subtle implementation differences as well as different zero shot task framing, these
might not be directly comparable. See <a href="https://blog.eleuther.ai/gpt3-model-sizes/">this blog post</a> for more
details.</p>
<p><strong>†</strong> Megatron-11B provides no comparable metrics, and several implementations using the released weights do not
reproduce the generation quality and evaluations. (see <a href="https://github.com/huggingface/transformers/pull/10301">1</a>
<a href="https://github.com/pytorch/fairseq/issues/2358">2</a> <a href="https://github.com/pytorch/fairseq/issues/2719">3</a>)
Thus, evaluation was not attempted.</p>
<p><strong>‡</strong> These models have been trained with data which contains possible test set contamination. The OpenAI GPT-3 models
failed to deduplicate training data for certain test sets, while the GPT-Neo models as well as this one is
trained on the Pile, which has not been deduplicated against any test sets.</p></figcaption></figure>
## Citation and Related Information
### BibTeX entry
To cite this model:
```bibtex
@misc{gpt-j,
author = {Wang, Ben and Komatsuzaki, Aran},
title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
year = 2021,
month = May
}
```
To cite the codebase that trained this model:
```bibtex
@misc{mesh-transformer-jax,
author = {Wang, Ben},
title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}},
howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
year = 2021,
month = May
}
```
If you use this model, we would love to hear about it! Reach out on [GitHub](https://github.com/kingoflolz/mesh-transformer-jax), Discord, or shoot Ben an email.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms) Alpha.
Thanks to everyone who have helped out one way or another (listed alphabetically):
- [James Bradbury](https://twitter.com/jekbradbury) for valuable assistance with debugging JAX issues.
- [Stella Biderman](https://www.stellabiderman.com), [Eric Hallahan](https://twitter.com/erichallahan), [Kurumuz](https://github.com/kurumuz/), and [Finetune](https://github.com/finetuneanon/) for converting the model to be compatible with the `transformers` package.
- [Leo Gao](https://twitter.com/nabla_theta) for running zero shot evaluations for the baseline models for the table.
- [Laurence Golding](https://github.com/researcher2/) for adding some features to the web demo.
- [Aran Komatsuzaki](https://twitter.com/arankomatsuzaki) for advice with experiment design and writing the blog posts.
- [Janko Prester](https://github.com/jprester/) for creating the web demo frontend.
|
VarunD/ppo-LunarLander-v2
|
VarunD
| 2023-06-21T14:23:50Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T14:23:28Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 270.22 +/- 12.31
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
orkg/orkgnlp-research-fields-classification
|
orkg
| 2023-06-21T14:20:50Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-06-07T13:53:34Z |
---
license: mit
---
This Repository includes the files required to run the `Research Fields Classification` ORKG-NLP service.
Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service.
This model is converted into a [TorchScript](https://pytorch.org/docs/stable/jit.html) (ScriptModule) using [torch.jit.trace](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html).
|
gsarti/opus-mt-tc-base-en-ja
|
gsarti
| 2023-06-21T14:12:24Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"ja",
"multilingual",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-09-09T21:54:27Z |
---
language:
- en
- ja
- multilingual
license: cc-by-4.0
tags:
- translation
- opus-mt-tc
model-index:
- name: opus-mt-tc-base-en-ja
results:
- task:
type: translation
name: Translation eng-jpg
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-jpg
metrics:
- type: bleu
value: 15.2
name: BLEU
---
# Opus Tatoeba English-Japanese
*This model was obtained by running the script [convert_marian_to_pytorch.py](https://github.com/huggingface/transformers/blob/master/src/transformers/models/marian/convert_marian_to_pytorch.py). The original models were trained by [J�rg Tiedemann](https://blogs.helsinki.fi/tiedeman/) using the [MarianNMT](https://marian-nmt.github.io/) library. See all available `MarianMTModel` models on the profile of the [Helsinki NLP](https://huggingface.co/Helsinki-NLP) group.*
* dataset: opus+bt
* model: transformer-align
* source language(s): eng
* target language(s): jpn
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download: [opus+bt-2021-04-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-jpn/opus+bt-2021-04-10.zip)
* test set translations: [opus+bt-2021-04-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-jpn/opus+bt-2021-04-10.test.txt)
* test set scores: [opus+bt-2021-04-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-jpn/opus+bt-2021-04-10.eval.txt)
## Benchmarks
| testset | BLEU | chr-F | #sent | #words | BP |
|---------|-------|-------|-------|--------|----|
| Tatoeba-test.eng-jpn | 15.2 | 0.258 | 10000 | 99206 | 1.000 |
|
Voryoji/Shuichi
|
Voryoji
| 2023-06-21T14:05:05Z | 2 | 0 |
fairseq
|
[
"fairseq",
"deberta-v2",
"art",
"audio-to-audio",
"jv",
"ja",
"zh",
"dataset:QingyiSi/Alpaca-CoT",
"doi:10.57967/hf/0791",
"license:creativeml-openrail-m",
"region:us"
] |
audio-to-audio
| 2023-06-21T12:30:26Z |
---
license: creativeml-openrail-m
datasets:
- QingyiSi/Alpaca-CoT
language:
- jv
- ja
- zh
metrics:
- bleurt
library_name: fairseq
pipeline_tag: audio-to-audio
tags:
- art
---
|
surajp/albert-base-sanskrit
|
surajp
| 2023-06-21T13:56:27Z | 12 | 4 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"albert",
"feature-extraction",
"sa",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: sa
---
# ALBERT-base-Sanskrit
Explaination Notebook Colab: [SanskritALBERT.ipynb](https://colab.research.google.com/github/parmarsuraj99/suraj-parmar/blob/master/_notebooks/2020-05-02-SanskritALBERT.ipynb)
Size of the model is **46MB**
Example of usage:
```
tokenizer = AutoTokenizer.from_pretrained("surajp/albert-base-sanskrit")
model = AutoModel.from_pretrained("surajp/albert-base-sanskrit")
enc=tokenizer.encode("ॐ सर्वे भवन्तु सुखिनः सर्वे सन्तु निरामयाः । सर्वे भद्राणि पश्यन्तु मा कश्चिद्दुःखभाग्भवेत् । ॐ शान्तिः शान्तिः शान्तिः ॥")
print(tokenizer.decode(enc))
ps = model(torch.tensor(enc).unsqueeze(1))
print(ps[0].shape)
```
```
'''
Output:
--------
[CLS] ॐ सर्वे भवन्तु सुखिनः सर्वे सन्तु निरामयाः । सर्वे भद्राणि पश्यन्तु मा कश्चिद्दुःखभाग्भवेत् । ॐ शान्तिः शान्तिः शान्तिः ॥[SEP]
torch.Size([28, 1, 768])
```
> Created by [Suraj Parmar/@parmarsuraj99](https://twitter.com/parmarsuraj99)
> Made with <span style="color: #e25555;">♥</span> in India
|
surajp/RoBERTa-hindi-guj-san
|
surajp
| 2023-06-21T13:56:15Z | 63 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"Indic",
"hi",
"sa",
"gu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- hi
- sa
- gu
tags:
- Indic
license: mit
datasets:
- Wikipedia (Hindi, Sanskrit, Gujarati)
metrics:
- perplexity
---
# RoBERTa-hindi-guj-san
## Model description
Multillingual RoBERTa like model trained on Wikipedia articles of Hindi, Sanskrit, Gujarati languages. The tokenizer was trained on combined text.
However, Hindi text was used to pre-train the model and then it was fine-tuned on Sanskrit and Gujarati Text combined hoping that pre-training with Hindi
will help the model learn similar languages.
### Configuration
| Parameter | Value |
|---|---|
| `hidden_size` | 768 |
| `num_attention_heads` | 12 |
| `num_hidden_layers` | 6 |
| `vocab_size` | 30522 |
|`model_type`|`roberta`|
## Intended uses & limitations
#### How to use
```python
# Example usage
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrained("surajp/RoBERTa-hindi-guj-san")
model = AutoModelWithLMHead.from_pretrained("surajp/RoBERTa-hindi-guj-san")
fill_mask = pipeline(
"fill-mask",
model=model,
tokenizer=tokenizer
)
# Sanskrit: इयं भाषा न केवलं भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।
# Hindi: अगर आप अब अभ्यास नहीं करते हो तो आप अपने परीक्षा में मूर्खतापूर्ण गलतियाँ करोगे।
# Gujarati: ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો <mask> હતો.
fill_mask("ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો <mask> હતો.")
'''
Output:
--------
[
{'score': 0.07849744707345963, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો જ હતો.</s>', 'token': 390},
{'score': 0.06273336708545685, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો ન હતો.</s>', 'token': 478},
{'score': 0.05160355195403099, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો થઇ હતો.</s>', 'token': 2075},
{'score': 0.04751499369740486, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો એક હતો.</s>', 'token': 600},
{'score': 0.03788900747895241, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો પણ હતો.</s>', 'token': 840}
]
```
## Training data
Cleaned wikipedia articles in Hindi, Sanskrit and Gujarati on Kaggle. It contains training as well as evaluation text.
Used in [iNLTK](https://github.com/goru001/inltk)
- [Hindi](https://www.kaggle.com/disisbig/hindi-wikipedia-articles-172k)
- [Gujarati](https://www.kaggle.com/disisbig/gujarati-wikipedia-articles)
- [Sanskrit](https://www.kaggle.com/disisbig/sanskrit-wikipedia-articles)
## Training procedure
- On TPU (using `xla_spawn.py`)
- For language modelling
- Iteratively increasing `--block_size` from 128 to 256 over epochs
- Tokenizer trained on combined text
- Pre-training with Hindi and fine-tuning on Sanskrit and Gujarati texts
```
--model_type distillroberta-base \
--model_name_or_path "/content/SanHiGujBERTa" \
--mlm_probability 0.20 \
--line_by_line \
--save_total_limit 2 \
--per_device_train_batch_size 128 \
--per_device_eval_batch_size 128 \
--num_train_epochs 5 \
--block_size 256 \
--seed 108 \
--overwrite_output_dir \
```
## Eval results
perplexity = 2.920005983224673
> Created by [Suraj Parmar/@parmarsuraj99](https://twitter.com/parmarsuraj99) | [LinkedIn](https://www.linkedin.com/in/parmarsuraj99/)
> Made with <span style="color: #e25555;">♥</span> in India
|
arcane-impact/gpt_bigcode-santacoder-ggml
|
arcane-impact
| 2023-06-21T13:50:23Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-06-21T13:40:47Z |
---
license: openrail
---
GGML format of [bigcode/gpt_bigcode-santacoder](https://huggingface.co/bigcode/gpt_bigcode-santacoder)
|
reinforceYrWay/ppo-Huggy
|
reinforceYrWay
| 2023-06-21T13:49:25Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-21T13:49:21Z |
---
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: reinforceYrWay/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
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