modelId
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81
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list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
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BigeS/DialoGPT-small-Rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 10 | 2022-11-24T23:16:16Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-1-parent
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8468253968253968
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4919786096256685
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.49554896142433236
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6203446359088383
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.794
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3991228070175439
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4675925925925926
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8946813319270754
- name: F1 (macro)
type: f1_macro
value: 0.8881909354487081
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8211267605633803
- name: F1 (macro)
type: f1_macro
value: 0.6170555844268196
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6338028169014085
- name: F1 (macro)
type: f1_macro
value: 0.6222691959704637
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9571537872991583
- name: F1 (macro)
type: f1_macro
value: 0.8677239073477255
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.878094641178314
- name: F1 (macro)
type: f1_macro
value: 0.8771653942410037
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-1-parent
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-1-parent/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4919786096256685
- Accuracy on SAT: 0.49554896142433236
- Accuracy on BATS: 0.6203446359088383
- Accuracy on U2: 0.3991228070175439
- Accuracy on U4: 0.4675925925925926
- Accuracy on Google: 0.794
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-1-parent/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8946813319270754
- Micro F1 score on CogALexV: 0.8211267605633803
- Micro F1 score on EVALution: 0.6338028169014085
- Micro F1 score on K&H+N: 0.9571537872991583
- Micro F1 score on ROOT09: 0.878094641178314
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-1-parent/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8468253968253968
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-1-parent")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 5
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: parent
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-1-parent/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
BinksSachary/ShaxxBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 9 | 2022-11-24T23:27:16Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: distilcamembert-cae-no-behavior
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. -->
# distilcamembert-cae-no-behavior
This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7115
- Precision: 0.8033
- Recall: 0.7975
- F1: 0.7966
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 1.1648 | 1.0 | 40 | 0.9164 | 0.5427 | 0.6076 | 0.5502 |
| 0.785 | 2.0 | 80 | 0.6939 | 0.7223 | 0.7089 | 0.6976 |
| 0.4211 | 3.0 | 120 | 0.7189 | 0.8007 | 0.7722 | 0.7823 |
| 0.2326 | 4.0 | 160 | 0.6878 | 0.7843 | 0.7595 | 0.7640 |
| 0.1357 | 5.0 | 200 | 0.7115 | 0.8033 | 0.7975 | 0.7966 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Blabla/Pipipopo | []
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} | 0 | 2022-11-24T23:43:44Z | ---
license: openrail++
language:
- en
tags:
- stable-diffusion
- text-to-image
- diffusers
thumbnail: "https://huggingface.co/nitrosocke/Future-Diffusion/resolve/main/images/future-diffusion-thumbnail-2.jpg"
inference: false
---
### Future Diffusion
This is the fine-tuned Stable Diffusion 2.0 model trained on high quality 3D images with a futuristic Sci-Fi theme.
Use the tokens
`future style`
in your prompts for the effect.
Trained on Stability.ai's [Stable Diffusion 2.0 Base](https://huggingface.co/stabilityai/stable-diffusion-2-base) with 512x512 resolution.
**If you enjoy my work and want to test new models before release, please consider supporting me**
[](https://patreon.com/user?u=79196446)
**Disclaimer: The SD 2.0 model is just over 24h old at this point and we still need to figure out how it works exactly. Please view this as an early prototype and experiment with the model.**
**Characters rendered with the model:**
**Cars and Animals rendered with the model:**
**Landscapes rendered with the model:**
#### Prompt and settings for the Characters:
**future style [subject] Negative Prompt: duplicate heads bad anatomy**
_Steps: 20, Sampler: Euler a, CFG scale: 7, Size: 512x704_
#### Prompt and settings for the Landscapes:
**future style city market street level at night Negative Prompt: blurry fog soft**
_Steps: 20, Sampler: Euler a, CFG scale: 7, Size: 1024x576_
This model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the _train-text-encoder_ flag in 7.000 steps.
## License
This model is open access and available to all, with a CreativeML Open RAIL++-M License further specifying rights and usage.
[Please read the full license here](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) |
Blackmist786/DialoGPt-small-transformers4 | [
"pytorch"
]
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} | 4 | 2022-11-24T23:45:37Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: distilcamembert-cae-territory
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. -->
# distilcamembert-cae-territory
This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7346
- Precision: 0.7139
- Recall: 0.6835
- F1: 0.6887
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 1.1749 | 1.0 | 40 | 1.0498 | 0.1963 | 0.4430 | 0.2720 |
| 0.9833 | 2.0 | 80 | 0.8853 | 0.7288 | 0.6709 | 0.6625 |
| 0.6263 | 3.0 | 120 | 0.7503 | 0.7237 | 0.6709 | 0.6689 |
| 0.3563 | 4.0 | 160 | 0.7346 | 0.7139 | 0.6835 | 0.6887 |
| 0.2253 | 5.0 | 200 | 0.7303 | 0.7139 | 0.6835 | 0.6887 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Blerrrry/Kkk | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: distilcamembert-cae-component
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. -->
# distilcamembert-cae-component
This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3683
- Precision: 0.9317
- Recall: 0.9303
- F1: 0.9306
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.6221 | 1.0 | 309 | 0.3860 | 0.9007 | 0.8720 | 0.8761 |
| 0.1723 | 2.0 | 618 | 0.3505 | 0.9233 | 0.9157 | 0.9168 |
| 0.0604 | 3.0 | 927 | 0.3683 | 0.9317 | 0.9303 | 0.9306 |
| 0.0117 | 4.0 | 1236 | 0.4214 | 0.9311 | 0.9303 | 0.9304 |
| 0.0061 | 5.0 | 1545 | 0.4232 | 0.9317 | 0.9303 | 0.9305 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
BlightZz/DialoGPT-medium-Kurisu | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 19 | 2022-11-25T00:12:29Z | ---
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: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9281121187139324
- name: Recall
type: recall
value: 0.9473241332884551
- name: F1
type: f1
value: 0.9376197218289332
- name: Accuracy
type: accuracy
value: 0.9862689115205746
---
<!-- 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: 0.0610
- Precision: 0.9281
- Recall: 0.9473
- F1: 0.9376
- Accuracy: 0.9863
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0859 | 1.0 | 1756 | 0.0663 | 0.9088 | 0.9312 | 0.9199 | 0.9822 |
| 0.0331 | 2.0 | 3512 | 0.0622 | 0.9270 | 0.9461 | 0.9365 | 0.9856 |
| 0.016 | 3.0 | 5268 | 0.0610 | 0.9281 | 0.9473 | 0.9376 | 0.9863 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
BogdanKuloren/continual-learning-paper-embeddings-model | [
"pytorch",
"mpnet",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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}
}
} | 11 | 2022-11-25T00:26:15Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.743095238095238
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4839572192513369
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4896142433234421
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6375764313507504
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.862
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4868421052631579
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5046296296296297
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8952840138616845
- name: F1 (macro)
type: f1_macro
value: 0.8885186450216263
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.819718309859155
- name: F1 (macro)
type: f1_macro
value: 0.6131261712437293
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6159263271939328
- name: F1 (macro)
type: f1_macro
value: 0.6066052358250007
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9501982332892815
- name: F1 (macro)
type: f1_macro
value: 0.865577305932915
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8755875900971483
- name: F1 (macro)
type: f1_macro
value: 0.8756061231187074
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4839572192513369
- Accuracy on SAT: 0.4896142433234421
- Accuracy on BATS: 0.6375764313507504
- Accuracy on U2: 0.4868421052631579
- Accuracy on U4: 0.5046296296296297
- Accuracy on Google: 0.862
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8952840138616845
- Micro F1 score on CogALexV: 0.819718309859155
- Micro F1 score on EVALution: 0.6159263271939328
- Micro F1 score on K&H+N: 0.9501982332892815
- Micro F1 score on ROOT09: 0.8755875900971483
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.743095238095238
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 5
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
BonjinKim/dst_kor_bert | [
"pytorch",
"jax",
"bert",
"pretraining",
"transformers"
]
| null | {
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}
}
} | 5 | null | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.763452380952381
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4358288770053476
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.44510385756676557
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6453585325180656
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.764
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.41228070175438597
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4305555555555556
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.893174627090553
- name: F1 (macro)
type: f1_macro
value: 0.88253444204685
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7779342723004695
- name: F1 (macro)
type: f1_macro
value: 0.5467947875271857
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6013001083423619
- name: F1 (macro)
type: f1_macro
value: 0.5766482049475778
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9545802323155039
- name: F1 (macro)
type: f1_macro
value: 0.8630198238087204
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8608586649952994
- name: F1 (macro)
type: f1_macro
value: 0.8593411038565139
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4358288770053476
- Accuracy on SAT: 0.44510385756676557
- Accuracy on BATS: 0.6453585325180656
- Accuracy on U2: 0.41228070175438597
- Accuracy on U4: 0.4305555555555556
- Accuracy on Google: 0.764
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.893174627090553
- Micro F1 score on CogALexV: 0.7779342723004695
- Micro F1 score on EVALution: 0.6013001083423619
- Micro F1 score on K&H+N: 0.9545802323155039
- Micro F1 score on ROOT09: 0.8608586649952994
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.763452380952381
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Boondong/Wandee | []
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}
} | 0 | 2022-11-25T00:29:42Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7553174603174603
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3877005347593583
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3887240356083086
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6158977209560867
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.622
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.38596491228070173
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.39814814814814814
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8958866957962935
- name: F1 (macro)
type: f1_macro
value: 0.8899187556569595
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7953051643192488
- name: F1 (macro)
type: f1_macro
value: 0.5661036014906222
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.5839653304442037
- name: F1 (macro)
type: f1_macro
value: 0.5694962586836211
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9565277874382695
- name: F1 (macro)
type: f1_macro
value: 0.8719904519356941
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8718270134753996
- name: F1 (macro)
type: f1_macro
value: 0.8712363187552654
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.3877005347593583
- Accuracy on SAT: 0.3887240356083086
- Accuracy on BATS: 0.6158977209560867
- Accuracy on U2: 0.38596491228070173
- Accuracy on U4: 0.39814814814814814
- Accuracy on Google: 0.622
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8958866957962935
- Micro F1 score on CogALexV: 0.7953051643192488
- Micro F1 score on EVALution: 0.5839653304442037
- Micro F1 score on K&H+N: 0.9565277874382695
- Micro F1 score on ROOT09: 0.8718270134753996
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7553174603174603
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 3
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
BossLee/t5-gec | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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"prefix": "translate English to Romanian: "
}
}
} | 6 | 2022-11-25T00:31:41Z | ---
## AloeVera's SimpMaker2000 Mix
This model is a highly versatile and balanced mix that will allow you to generate stunning pictures in a wide range of styles and settings.
# Scroll Down for Samples
-language: en
-license: Unknown
## This Model was merged using the following models:
- SameDoesArts Ultmerge
- Anything V3.0
- Zeipher F222
- SXD 8.0
- Hassans Blend v2
- Zeipher F111
## Prompting
To make this model work I highly recommend you use CLIP SKIP 1, and the VAE Stable Diffusion 1.5 - 840'000 steps.
All the image below have the prompt data etc, you can use https://www.metadata2go.com/ to retrieve it, to guide you
with the prompting of the model. With the right words.. pretty much EVERYTHING can be achieved with this model.
I used NMKD's GUI 1.7.1 to generate the sampled images
Sampler: DPM++2 A
## DISCLAIMER
I do not presume to OWN any form of copyrights related to the models used in this merge, I only mean to share this balanced mix that in my opinion produces
excellent pictures in a wide variety of styles ranging from SFW, to fantasy, to NSFW, to horror, to photographs, anime, etc...
AloeVera
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669340090307-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669340092012-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669340092013-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669340091729-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669340090733-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669341162944-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669341151859-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669341162937-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669341163224-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669341162936-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669341162085-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669341693883-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669341699128-63800c8c96f995a21717050c.png">
<img width="768px" src="https://s3.amazonaws.com/moonup/production/uploads/1669341694334-63800c8c96f995a21717050c.png">
|
Botjallu/DialoGPT-small-harrypotter | []
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} | 0 | null | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.6083928571428572
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.2967914438502674
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.29080118694362017
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5525291828793775
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.626
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.40350877192982454
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.40046296296296297
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9013108332077746
- name: F1 (macro)
type: f1_macro
value: 0.8942952749488488
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7812206572769953
- name: F1 (macro)
type: f1_macro
value: 0.5351011171746861
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.59534127843987
- name: F1 (macro)
type: f1_macro
value: 0.578313715405001
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9534673436739236
- name: F1 (macro)
type: f1_macro
value: 0.8633473853913686
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8420557818865559
- name: F1 (macro)
type: f1_macro
value: 0.8473749954042976
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.2967914438502674
- Accuracy on SAT: 0.29080118694362017
- Accuracy on BATS: 0.5525291828793775
- Accuracy on U2: 0.40350877192982454
- Accuracy on U4: 0.40046296296296297
- Accuracy on Google: 0.626
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9013108332077746
- Micro F1 score on CogALexV: 0.7812206572769953
- Micro F1 score on EVALution: 0.59534127843987
- Micro F1 score on K&H+N: 0.9534673436739236
- Micro F1 score on ROOT09: 0.8420557818865559
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.6083928571428572
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Botslity/Bot | []
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} | 0 | 2022-11-25T00:36:22Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-1-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7961111111111111
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5668449197860963
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5637982195845698
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7087270705947749
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.862
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5185185185185185
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9094470393249963
- name: F1 (macro)
type: f1_macro
value: 0.9071978262208379
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8481220657276995
- name: F1 (macro)
type: f1_macro
value: 0.6770534875484192
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6663055254604551
- name: F1 (macro)
type: f1_macro
value: 0.6477804074680519
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9549975655560965
- name: F1 (macro)
type: f1_macro
value: 0.8718545305555775
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8994045753682232
- name: F1 (macro)
type: f1_macro
value: 0.8985019443558045
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-1-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-1-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5668449197860963
- Accuracy on SAT: 0.5637982195845698
- Accuracy on BATS: 0.7087270705947749
- Accuracy on U2: 0.5
- Accuracy on U4: 0.5185185185185185
- Accuracy on Google: 0.862
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-1-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9094470393249963
- Micro F1 score on CogALexV: 0.8481220657276995
- Micro F1 score on EVALution: 0.6663055254604551
- Micro F1 score on K&H+N: 0.9549975655560965
- Micro F1 score on ROOT09: 0.8994045753682232
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-1-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7961111111111111
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-1-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 5
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-1-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
BotterHax/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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"GPT2LMHeadModel"
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} | 8 | null | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-2-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7766666666666666
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5106951871657754
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5192878338278932
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6336853807670928
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.836
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4956140350877193
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4675925925925926
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9070363115865602
- name: F1 (macro)
type: f1_macro
value: 0.9020957402698154
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8448356807511737
- name: F1 (macro)
type: f1_macro
value: 0.656354139071707
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6630552546045504
- name: F1 (macro)
type: f1_macro
value: 0.6446451538634405
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9517980107115531
- name: F1 (macro)
type: f1_macro
value: 0.8705494994159542
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8799749294891883
- name: F1 (macro)
type: f1_macro
value: 0.8791801333500958
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-2-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-2-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5106951871657754
- Accuracy on SAT: 0.5192878338278932
- Accuracy on BATS: 0.6336853807670928
- Accuracy on U2: 0.4956140350877193
- Accuracy on U4: 0.4675925925925926
- Accuracy on Google: 0.836
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-2-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9070363115865602
- Micro F1 score on CogALexV: 0.8448356807511737
- Micro F1 score on EVALution: 0.6630552546045504
- Micro F1 score on K&H+N: 0.9517980107115531
- Micro F1 score on ROOT09: 0.8799749294891883
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-2-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7766666666666666
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-2-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-nce-2-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
BritishLibraryLabs/bl-books-genre | [
"pytorch",
"distilbert",
"text-classification",
"multilingual",
"dataset:blbooksgenre",
"transformers",
"genre",
"books",
"library",
"historic",
"glam ",
"lam",
"license:mit",
"has_space"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 76 | 2022-11-25T00:44:33Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-0-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7946825396825397
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.47593582887700536
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.47774480712166173
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7026125625347415
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.766
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.41228070175438597
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.47453703703703703
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9061322886846467
- name: F1 (macro)
type: f1_macro
value: 0.9005307658810957
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8345070422535211
- name: F1 (macro)
type: f1_macro
value: 0.6390904639654723
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6570964247020585
- name: F1 (macro)
type: f1_macro
value: 0.6350934679864825
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9510328997704667
- name: F1 (macro)
type: f1_macro
value: 0.8702203249298888
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8953306173613286
- name: F1 (macro)
type: f1_macro
value: 0.8939575406867228
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-0-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-0-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.47593582887700536
- Accuracy on SAT: 0.47774480712166173
- Accuracy on BATS: 0.7026125625347415
- Accuracy on U2: 0.41228070175438597
- Accuracy on U4: 0.47453703703703703
- Accuracy on Google: 0.766
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-0-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9061322886846467
- Micro F1 score on CogALexV: 0.8345070422535211
- Micro F1 score on EVALution: 0.6570964247020585
- Micro F1 score on K&H+N: 0.9510328997704667
- Micro F1 score on ROOT09: 0.8953306173613286
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-0-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7946825396825397
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-0-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 8
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-0-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Broadus20/DialoGPT-small-joshua | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 12 | 2022-11-25T00:47:57Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-2-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.6354563492063492
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.31016042780748665
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.32344213649851633
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3085047248471373
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.328
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3157894736842105
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.2986111111111111
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9019135151423836
- name: F1 (macro)
type: f1_macro
value: 0.8910141280520246
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7758215962441315
- name: F1 (macro)
type: f1_macro
value: 0.5011043573121808
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.5677139761646804
- name: F1 (macro)
type: f1_macro
value: 0.5566709002001424
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9569451206788621
- name: F1 (macro)
type: f1_macro
value: 0.8723408257004264
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8621121905358822
- name: F1 (macro)
type: f1_macro
value: 0.8575846242198137
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-2-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-2-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.31016042780748665
- Accuracy on SAT: 0.32344213649851633
- Accuracy on BATS: 0.3085047248471373
- Accuracy on U2: 0.3157894736842105
- Accuracy on U4: 0.2986111111111111
- Accuracy on Google: 0.328
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-2-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9019135151423836
- Micro F1 score on CogALexV: 0.7758215962441315
- Micro F1 score on EVALution: 0.5677139761646804
- Micro F1 score on K&H+N: 0.9569451206788621
- Micro F1 score on ROOT09: 0.8621121905358822
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-2-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.6354563492063492
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-2-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 5
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-nce-2-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Brokette/projetCS | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
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}
}
} | 4 | 2022-11-25T00:49:33Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-0-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7938293650793651
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5374331550802139
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5370919881305638
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.688715953307393
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.896
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5570175438596491
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5509259259259259
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9100497212596053
- name: F1 (macro)
type: f1_macro
value: 0.9059343922634877
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8443661971830986
- name: F1 (macro)
type: f1_macro
value: 0.6626426898012254
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6728060671722643
- name: F1 (macro)
type: f1_macro
value: 0.6655534986615009
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9536064547541212
- name: F1 (macro)
type: f1_macro
value: 0.8662723101451977
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.89157004073958
- name: F1 (macro)
type: f1_macro
value: 0.8897680037027316
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-0-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-0-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5374331550802139
- Accuracy on SAT: 0.5370919881305638
- Accuracy on BATS: 0.688715953307393
- Accuracy on U2: 0.5570175438596491
- Accuracy on U4: 0.5509259259259259
- Accuracy on Google: 0.896
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-0-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9100497212596053
- Micro F1 score on CogALexV: 0.8443661971830986
- Micro F1 score on EVALution: 0.6728060671722643
- Micro F1 score on K&H+N: 0.9536064547541212
- Micro F1 score on ROOT09: 0.89157004073958
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-0-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7938293650793651
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-0-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 8
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-0-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Brona/poc_de | []
| null | {
"architectures": null,
"model_type": null,
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | 2022-11-25T00:51:26Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-1-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8196825396825397
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.56951871657754
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5667655786350149
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7048360200111173
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.928
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5219298245614035
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5254629629629629
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9231580533373512
- name: F1 (macro)
type: f1_macro
value: 0.9194572369237081
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.85962441314554
- name: F1 (macro)
type: f1_macro
value: 0.6907772192655386
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6630552546045504
- name: F1 (macro)
type: f1_macro
value: 0.6453703655988703
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9586144536412325
- name: F1 (macro)
type: f1_macro
value: 0.8827948686405107
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8906298965841429
- name: F1 (macro)
type: f1_macro
value: 0.8863329325808406
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-1-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-1-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.56951871657754
- Accuracy on SAT: 0.5667655786350149
- Accuracy on BATS: 0.7048360200111173
- Accuracy on U2: 0.5219298245614035
- Accuracy on U4: 0.5254629629629629
- Accuracy on Google: 0.928
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-1-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9231580533373512
- Micro F1 score on CogALexV: 0.85962441314554
- Micro F1 score on EVALution: 0.6630552546045504
- Micro F1 score on K&H+N: 0.9586144536412325
- Micro F1 score on ROOT09: 0.8906298965841429
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-1-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8196825396825397
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-1-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-1-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
BrunoNogueira/DialoGPT-kungfupanda | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 10 | 2022-11-25T00:53:04Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-2-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7883531746031746
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.553475935828877
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5459940652818991
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.725958866036687
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.9
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.49122807017543857
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5185185185185185
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9047762543317764
- name: F1 (macro)
type: f1_macro
value: 0.9016293933408125
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8410798122065728
- name: F1 (macro)
type: f1_macro
value: 0.6595900253618268
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.652762730227519
- name: F1 (macro)
type: f1_macro
value: 0.6495593725859232
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9506155665298741
- name: F1 (macro)
type: f1_macro
value: 0.8601599227538594
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8896897524287057
- name: F1 (macro)
type: f1_macro
value: 0.889792052107688
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-2-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-2-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.553475935828877
- Accuracy on SAT: 0.5459940652818991
- Accuracy on BATS: 0.725958866036687
- Accuracy on U2: 0.49122807017543857
- Accuracy on U4: 0.5185185185185185
- Accuracy on Google: 0.9
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-2-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9047762543317764
- Micro F1 score on CogALexV: 0.8410798122065728
- Micro F1 score on EVALution: 0.652762730227519
- Micro F1 score on K&H+N: 0.9506155665298741
- Micro F1 score on ROOT09: 0.8896897524287057
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-2-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7883531746031746
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-2-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-nce-2-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Brunomezenga/NN | []
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} | 0 | 2022-11-25T00:54:41Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-nce-0-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7786111111111111
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.44385026737967914
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4391691394658754
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4952751528627015
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.71
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.39035087719298245
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4444444444444444
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9159258701220431
- name: F1 (macro)
type: f1_macro
value: 0.9126316000488791
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8164319248826291
- name: F1 (macro)
type: f1_macro
value: 0.6068522121067965
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6321776814734561
- name: F1 (macro)
type: f1_macro
value: 0.6239199306624651
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9479029004660221
- name: F1 (macro)
type: f1_macro
value: 0.8642589837552072
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8755875900971483
- name: F1 (macro)
type: f1_macro
value: 0.8762091857973789
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-nce-0-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-nce-0-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.44385026737967914
- Accuracy on SAT: 0.4391691394658754
- Accuracy on BATS: 0.4952751528627015
- Accuracy on U2: 0.39035087719298245
- Accuracy on U4: 0.4444444444444444
- Accuracy on Google: 0.71
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-nce-0-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9159258701220431
- Micro F1 score on CogALexV: 0.8164319248826291
- Micro F1 score on EVALution: 0.6321776814734561
- Micro F1 score on K&H+N: 0.9479029004660221
- Micro F1 score on ROOT09: 0.8755875900971483
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-nce-0-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7786111111111111
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-nce-0-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 6
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-nce-0-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
BumBelDumBel/ZORK_AI_FANTASY | []
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}
}
} | 0 | null | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-parent
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.743095238095238
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4839572192513369
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4896142433234421
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6375764313507504
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.862
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4868421052631579
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5046296296296297
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8952840138616845
- name: F1 (macro)
type: f1_macro
value: 0.8885186450216263
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.819718309859155
- name: F1 (macro)
type: f1_macro
value: 0.6131261712437293
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6159263271939328
- name: F1 (macro)
type: f1_macro
value: 0.6066052358250007
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9501982332892815
- name: F1 (macro)
type: f1_macro
value: 0.865577305932915
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8755875900971483
- name: F1 (macro)
type: f1_macro
value: 0.8756061231187074
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-parent
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-parent/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4839572192513369
- Accuracy on SAT: 0.4896142433234421
- Accuracy on BATS: 0.6375764313507504
- Accuracy on U2: 0.4868421052631579
- Accuracy on U4: 0.5046296296296297
- Accuracy on Google: 0.862
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-parent/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8952840138616845
- Micro F1 score on CogALexV: 0.819718309859155
- Micro F1 score on EVALution: 0.6159263271939328
- Micro F1 score on K&H+N: 0.9501982332892815
- Micro F1 score on ROOT09: 0.8755875900971483
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-parent/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.743095238095238
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-parent")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 5
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: parent
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-1-parent/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
BumBelDumBel/ZORK_AI_SCIFI | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
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} | 14 | null | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-parent
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7425793650793651
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.39037433155080214
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3827893175074184
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.471372984991662
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.682
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.39473684210526316
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4513888888888889
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8625885189091457
- name: F1 (macro)
type: f1_macro
value: 0.8579789924146027
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7875586854460094
- name: F1 (macro)
type: f1_macro
value: 0.5559978680629714
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.5991332611050921
- name: F1 (macro)
type: f1_macro
value: 0.5789568668572579
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9440077902204911
- name: F1 (macro)
type: f1_macro
value: 0.851850221872151
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8539642745220932
- name: F1 (macro)
type: f1_macro
value: 0.8572408527947681
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-parent
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-parent/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.39037433155080214
- Accuracy on SAT: 0.3827893175074184
- Accuracy on BATS: 0.471372984991662
- Accuracy on U2: 0.39473684210526316
- Accuracy on U4: 0.4513888888888889
- Accuracy on Google: 0.682
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-parent/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8625885189091457
- Micro F1 score on CogALexV: 0.7875586854460094
- Micro F1 score on EVALution: 0.5991332611050921
- Micro F1 score on K&H+N: 0.9440077902204911
- Micro F1 score on ROOT09: 0.8539642745220932
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-parent/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7425793650793651
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-parent")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 10
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: parent
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-nce-2-parent/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
BunakovD/sd | []
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} | 0 | 2022-11-25T01:19:18Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-parent
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7812301587301588
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.45989304812834225
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4629080118694362
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.565869927737632
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.744
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.41228070175438597
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4699074074074074
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8999547988549043
- name: F1 (macro)
type: f1_macro
value: 0.8962816660157421
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8105633802816902
- name: F1 (macro)
type: f1_macro
value: 0.5902758892120937
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6164680390032503
- name: F1 (macro)
type: f1_macro
value: 0.6103988006466425
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9581971204006399
- name: F1 (macro)
type: f1_macro
value: 0.8785923815023278
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8834221247257913
- name: F1 (macro)
type: f1_macro
value: 0.8841984389956332
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-parent
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-parent/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.45989304812834225
- Accuracy on SAT: 0.4629080118694362
- Accuracy on BATS: 0.565869927737632
- Accuracy on U2: 0.41228070175438597
- Accuracy on U4: 0.4699074074074074
- Accuracy on Google: 0.744
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-parent/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8999547988549043
- Micro F1 score on CogALexV: 0.8105633802816902
- Micro F1 score on EVALution: 0.6164680390032503
- Micro F1 score on K&H+N: 0.9581971204006399
- Micro F1 score on ROOT09: 0.8834221247257913
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-parent/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7812301587301588
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-parent")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 10
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: parent
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-0-parent/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Buntan/xlm-roberta-base-finetuned-marc-en | []
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} | 0 | null | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-parent
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.6670436507936508
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3770053475935829
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.37388724035608306
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4802668148971651
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.558
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.33771929824561403
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.34953703703703703
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8898598764502034
- name: F1 (macro)
type: f1_macro
value: 0.881086615114024
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7859154929577464
- name: F1 (macro)
type: f1_macro
value: 0.5266767261561511
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.566088840736728
- name: F1 (macro)
type: f1_macro
value: 0.5668961837542391
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9492940112679975
- name: F1 (macro)
type: f1_macro
value: 0.8577064786656643
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8480100282043247
- name: F1 (macro)
type: f1_macro
value: 0.8474938401923806
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-parent
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-parent/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.3770053475935829
- Accuracy on SAT: 0.37388724035608306
- Accuracy on BATS: 0.4802668148971651
- Accuracy on U2: 0.33771929824561403
- Accuracy on U4: 0.34953703703703703
- Accuracy on Google: 0.558
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-parent/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8898598764502034
- Micro F1 score on CogALexV: 0.7859154929577464
- Micro F1 score on EVALution: 0.566088840736728
- Micro F1 score on K&H+N: 0.9492940112679975
- Micro F1 score on ROOT09: 0.8480100282043247
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-parent/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.6670436507936508
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-parent")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 5
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: parent
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-parent/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Bwehfuk/Ron | []
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} | 0 | null | ---
license: mit
inference: false
tags:
- music
---
# Introduction to our series work
The development log of our Music Audio Pre-training (m-a-p) model family:
- 17/03/2023: we release two advanced music understanding models, [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M) and [MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks.
- 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset [MERT-v0-public](https://huggingface.co/m-a-p/MERT-v0-public)
- 29/12/2022: a music understanding model [MERT-v0](https://huggingface.co/m-a-p/MERT-v0) trained with **MLM** paradigm, which performs better at downstream tasks.
- 29/10/2022: a pre-trained MIR model [music2vec](https://huggingface.co/m-a-p/music2vec-v1) trained with **BYOL** paradigm.
Here is a table for quick model pick-up:
| Name | Pre-train Paradigm | Training Data (hour) | Pre-train Context (second) | Model Size | Transformer Layer-Dimension | Feature Rate | Sample Rate | Release Date |
| ------------------------------------------------------------ | ------------------ | -------------------- | ---------------------------- | ---------- | --------------------------- | ------------ | ----------- | ------------ |
| [MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) | MLM | 160K | 5 | 330M | 24-1024 | 75 Hz | 24K Hz | 17/03/2023 |
| [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M) | MLM | 20K | 5 | 95M | 12-768 | 75 Hz | 24K Hz | 17/03/2023 |
| [MERT-v0-public](https://huggingface.co/m-a-p/MERT-v0-public) | MLM | 900 | 5 | 95M | 12-768 | 50 Hz | 16K Hz | 14/03/2023 |
| [MERT-v0](https://huggingface.co/m-a-p/MERT-v0) | MLM | 1000 | 5 | 95 M | 12-768 | 50 Hz | 16K Hz | 29/12/2022 |
| [music2vec-v1](https://huggingface.co/m-a-p/music2vec-v1) | BYOL | 1000 | 30 | 95 M | 12-768 | 50 Hz | 16K Hz | 30/10/2022 |
## Explanation
The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using:
- **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware.
- **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**.
- **Feature Rate**: Given a 1-second audio input, the number of features output by the model.
- **Sample Rate**: The frequency of audio that the model is trained with.
# Introduction to Music2Vec
**Music2Vec** is accepted as 2-page abstract in Late Breaking Demos (LBD) at the ISMIR 2022.
It is a completely unsupervised model trained on 1000 hour music audios.
We release the **crop5s** version base model as music2vec-v1.
Our base model is SOTA-comparable on multiple MIR tasks even under probing settings, while keeping fine-tunable on a single 2080Ti.
Larger models trained with more data are on the way~
For a more recent pretrained model with better performance, please refer to [m-a-p/MERT-v0](https://huggingface.co/m-a-p/MERT-v0).
# Model Architecture
Music2Vec Framework. During pre-training, the student model aims to
reconstruct the masked music audio by taking the contextualized representations provided by the teacher model as prediction targets.
# Performance Comparison
With 95M parameters and relatively small training data (1k hr), our base Music2Vec representation achieves comparable performance to the SOTA Jukebox-5B representation.
Note that our base model size is **<2%** of Jukebox-5B.
# Model Usage
```python
from transformers import Wav2Vec2Processor, Data2VecAudioModel
import torch
from torch import nn
from datasets import load_dataset
# load demo audio and set processor
dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
dataset = dataset.sort("id")
sampling_rate = dataset.features["audio"].sampling_rate
processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h")
# loading our model weights
model = Data2VecAudioModel.from_pretrained("m-a-p/music2vec-v1")
# audio file is decoded on the fly
inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs, output_hidden_states=True)
# take a look at the output shape, there are 13 layers of representation
# each layer performs differently in different downstream tasks, you should choose empirically
all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze()
print(all_layer_hidden_states.shape) # [13 layer, 292 timestep, 768 feature_dim]
# for utterance level classification tasks, you can simply reduce the representation in time
time_reduced_hidden_states = all_layer_hidden_states.mean(-2)
print(time_reduced_hidden_states.shape) # [13, 768]
# you can even use a learnable weighted average representation
aggregator = nn.Conv1d(in_channels=13, out_channels=1, kernel_size=1)
weighted_avg_hidden_states = aggregator(time_reduced_hidden_states).squeeze()
print(weighted_avg_hidden_states.shape) # [768]
```
Our model is based on the [data2vec audio model](https://huggingface.co/docs/transformers/model_doc/data2vec#transformers.Data2VecAudioModel).
# Citation
The paper can be found at [ISMIR](https://ismir2022program.ismir.net/lbd_410.html).
```shell
@article{li2022map,
title={MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning},
author={Li, Yizhi and Yuan, Ruibin and Zhang, Ge and Ma, Yinghao and Lin, Chenghua and Chen, Xingran and Ragni, Anton and Yin, Hanzhi and Hu, Zhijie and He, Haoyu and others},
journal={arXiv preprint arXiv:2212.02508},
year={2022}
}
``` |
CALM/CALM | []
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} | 0 | null | ---
license: creativeml-openrail-m
---
# 矢车菊：基于StableDiffusion的虚拟画师
**If you are an English user, please scrool to the bottom of the article to see the English version of the introduction.**
**这是什么？**
矢车菊是一种基于StableDiffusion，利用特定风格的插图训练得到的绘画风格，理论上来说和现实的人类画师多少有一些区分度。
目前提供私炉版 (Embedding + HyperNetwork) 和大模型版 (ckpt单文件)两个版本。
<ul>
<li>
私炉版(YaguruMagiku-v3)：当前的v3版本是在v2版本的基础上换装了Anything3.0模型并进行微调得来的，所以首先需要Anything3.0模型才可使用。使用时，将两个文件分别放到对应的位置，运行以后在prompt中输入'art by yaguru magiku'以激活画风，采样器建议使用DPM fast。
</li>
<li>
大模型版(YaguruMagiku-v3-Anybased)：利用DreamBooth在Anything3.0模型的基础上训练私炉版生成的大量图片得到的整合模型，可以像各类模型一样直接使用。运行以后在prompt中输入'yagumagi style'以激活画风，采样器建议使用LMS。
</li>
</ul>
**两个版本有什么区别？**
由于训练过程不同，两种版本的图像风格会有一部分差异，主要体现在大模型版有着更接近水彩画的风格，而私炉版对人物的描绘更为淡雅。
下图为同一参数下不同模型的输出图片比较，左为大模型版，右为私炉版。
**模型效果如何？**
在不同版本的目录下面都有展示图可供查看。
**如何安装？**
互联网上有许多有关ckpt、Embedding和Hypernetwork文件的安装方法，这里不展开赘述。如果你仍有疑问，可以在bilibili上联系我。
**有没有适合搭配的prompt？**
以下这段是我经常用的，不过可能会过于光污染，你可以根据自己的喜好酌情调整。
> art by yaguru magiku, Anime Style, Super Quality, Vaporwave, Synth Wave, weirdcore, dreamcore, dreamy, full body, ((masterpiece, best quality,best quality,highres,Amazing, beautiful detailed eyes,finely detail,Depth of field,extremely detailed CG unity 8k wallpaper, masterpiece, straight-on))
<br>#将你想要的内容放在这里<br>
[[red, blue, green, yellow, black, white, pink, purple, cowboy shot, ((((colorful)))), ink and wash painting, [white hair], delicate and beautiful girl, ((illustration)), ((floating hair)), ((chromatic aberration)), ((caustic)), lens flare, dynamic angle, ((portrait)), ((glass strips)), ((floating glass fragments)), ((colorful refraction)), (stained glass), ((dark intense shadows)), ((sharp focus))]]
Visual effects:movie lights, theatrical point of view, ((masterpiece)), best quality, high resolution illustrations, particle effect, ((very detailed CG)), ((masterpiece))absurd, intricate details, ((8k_wallpaper))
如果你使用大模型版，记得把开头换了。
如果你想加重水彩效果，可以在最后加上适当权重的watercolor。
# Yaguru Magiku: A virtual artist based on StableDiffusion
**What is this?**
Cornflower is a painting style based on StableDiffusion that uses specific styles of illustration training, and theoretically distinguishes it from real-life human painters.
Two versions are currently available: Embedding + HyperNetwork and Large Model (ckpt single file).
<ul>
<li>
YaguruMagiku-v3: The current v3 version is based on the v2 version with the Anything 3.0 model and fine-tuned, so the Anything 3.0 model is required first. When using, put the two files in the corresponding location, and after running, enter 'art by yaguru magiku' in the prompt to activate the art style, the sampler recommends using Euler a.
</li>
<li>
Large Model Edition (YaguruMagiku-v3-Anybased): An integrated model obtained by using DreamBooth to train a large number of images generated by YaguruMagiku-v3 based on the Anything 3.0 model, which can be used directly like various models. After running, enter 'yagumagi style' in the prompt to activate the art style, and the sampler recommends using LMS.
</li>
</ul>
**What is the difference between the two versions?**
Due to the different training process, there will be some differences in the image style of the two versions, mainly reflected in the fact that the large model version has a style closer to watercolor painting, while YaguruMagiku-v3 depicts the characters more elegantly.
The following figure compares the output pictures of different models under the same parameters, with the large model version on the left and the YaguruMagiku-v3 on the right.
**How does the model work?**
There are display images under the different versions of the catalog.
**How to install?**
There are many ways to install ckpt, Embedding, and Hypernetwork files on the Internet, which I won't go into here. If you still have questions, you can contact me on bilibili.
**Is there a good prompt to match?**
The following paragraph is what I use often, but it may be too light pollution, you can adjust it according to your preferences.
> art by yaguru magiku, Anime Style, Super Quality, Vaporwave, Synth Wave, weirdcore, dreamcore, dreamy, full body, ((masterpiece, best quality,best quality,highres,Amazing, beautiful detailed eyes,finely detail,Depth of field,extremely detailed CG unity 8k wallpaper, masterpiece, straight-on))
<br>#put your own contents there<br>
[[red, blue, green, yellow, black, white, pink, purple, cowboy shot, ((((colorful)))), ink and wash painting, [white hair], delicate and beautiful girl, ((illustration)), ((floating hair)), ((chromatic aberration)), ((caustic)), lens flare, dynamic angle, ((portrait)), ((glass strips)), ((floating glass fragments)), ((colorful refraction)), (stained glass), ((dark intense shadows)), ((sharp focus))]]
Visual effects:movie lights, theatrical point of view, ((masterpiece)), best quality, high resolution illustrations, particle effect, ((very detailed CG)), ((masterpiece))absurd, intricate details, ((8k_wallpaper))
If you use the large model version, remember to change the first sphere.
If you want to accentuate the watercolor effect, you can add the appropriate weight of 'watercolor' at the end.
|
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
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} | 37 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: DistilBert-finetuned-Hackaton
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-finetuned-Hackaton
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1456
- Accuracy: 0.4283
- F1: 0.4344
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.3155 | 1.0 | 338 | 2.6640 | 0.33 | 0.3161 |
| 2.2064 | 2.0 | 676 | 2.5991 | 0.3283 | 0.3094 |
| 2.0703 | 3.0 | 1014 | 2.5172 | 0.3467 | 0.3347 |
| 2.0222 | 4.0 | 1352 | 2.4497 | 0.3567 | 0.3434 |
| 1.9197 | 5.0 | 1690 | 2.3951 | 0.375 | 0.3639 |
| 1.8334 | 6.0 | 2028 | 2.3398 | 0.375 | 0.3646 |
| 1.7327 | 7.0 | 2366 | 2.3231 | 0.3833 | 0.3749 |
| 1.6621 | 8.0 | 2704 | 2.3040 | 0.3867 | 0.3787 |
| 1.5902 | 9.0 | 3042 | 2.2702 | 0.3883 | 0.3809 |
| 1.5554 | 10.0 | 3380 | 2.2230 | 0.4167 | 0.4143 |
| 1.5008 | 11.0 | 3718 | 2.2277 | 0.4067 | 0.3999 |
| 1.4451 | 12.0 | 4056 | 2.2023 | 0.4033 | 0.4025 |
| 1.3788 | 13.0 | 4394 | 2.1953 | 0.41 | 0.4066 |
| 1.3418 | 14.0 | 4732 | 2.1774 | 0.4083 | 0.4036 |
| 1.2689 | 15.0 | 5070 | 2.1798 | 0.41 | 0.4123 |
| 1.2495 | 16.0 | 5408 | 2.1700 | 0.4233 | 0.4228 |
| 1.1946 | 17.0 | 5746 | 2.1653 | 0.42 | 0.4241 |
| 1.1652 | 18.0 | 6084 | 2.1672 | 0.4283 | 0.4279 |
| 1.1428 | 19.0 | 6422 | 2.1631 | 0.4217 | 0.4259 |
| 1.1027 | 20.0 | 6760 | 2.1501 | 0.4133 | 0.4189 |
| 1.063 | 21.0 | 7098 | 2.1522 | 0.4183 | 0.4244 |
| 1.0621 | 22.0 | 7436 | 2.1480 | 0.42 | 0.4258 |
| 1.0412 | 23.0 | 7774 | 2.1491 | 0.4217 | 0.4285 |
| 1.0311 | 24.0 | 8112 | 2.1493 | 0.4267 | 0.4333 |
| 1.0195 | 25.0 | 8450 | 2.1456 | 0.4283 | 0.4344 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy | [
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"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 32 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/screenmix/1669345167844/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1396778415269761024/KhPw6ZzX_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Screen Mix</div>
<div style="text-align: center; font-size: 14px;">@screenmix</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Screen Mix.
| Data | Screen Mix |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 340 |
| Short tweets | 52 |
| Tweets kept | 2858 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/loq13m25/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @screenmix's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/20974c1b) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/20974c1b/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/screenmix')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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}
} | 54 | null | ---
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
datasets:
- ai_light_dance
metrics:
- wer
model-index:
- name: ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-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. -->
# ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-2
This model is a fine-tuned version of [gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-2](https://huggingface.co/gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-2) on the GARY109/AI_LIGHT_DANCE - ONSET-IDMT-2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2794
- Wer: 0.2733
## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 9 | 0.3089 | 0.2856 |
| 0.2871 | 2.0 | 18 | 0.3208 | 0.28 |
| 0.2997 | 3.0 | 27 | 0.3948 | 0.2878 |
| 0.299 | 4.0 | 36 | 0.3137 | 0.3011 |
| 0.3462 | 5.0 | 45 | 0.3067 | 0.2689 |
| 0.3098 | 6.0 | 54 | 0.3271 | 0.2811 |
| 0.2812 | 7.0 | 63 | 0.4907 | 0.26 |
| 0.3151 | 8.0 | 72 | 0.5852 | 0.2778 |
| 0.3038 | 9.0 | 81 | 0.2981 | 0.2767 |
| 0.3248 | 10.0 | 90 | 0.3129 | 0.2811 |
| 0.3248 | 11.0 | 99 | 0.4090 | 0.2767 |
| 0.3106 | 12.0 | 108 | 0.5354 | 0.3 |
| 0.2702 | 13.0 | 117 | 0.5543 | 0.3 |
| 0.3021 | 14.0 | 126 | 0.5437 | 0.2689 |
| 0.2622 | 15.0 | 135 | 0.5898 | 0.2778 |
| 0.2465 | 16.0 | 144 | 0.2900 | 0.2722 |
| 0.3077 | 17.0 | 153 | 0.4407 | 0.2544 |
| 0.2959 | 18.0 | 162 | 0.4079 | 0.2944 |
| 0.2843 | 19.0 | 171 | 0.5042 | 0.2722 |
| 0.254 | 20.0 | 180 | 0.3851 | 0.2878 |
| 0.254 | 21.0 | 189 | 0.3912 | 0.2678 |
| 0.2532 | 22.0 | 198 | 0.4699 | 0.2578 |
| 0.3011 | 23.0 | 207 | 0.7466 | 0.2744 |
| 0.2601 | 24.0 | 216 | 0.4238 | 0.28 |
| 0.2873 | 25.0 | 225 | 0.3817 | 0.2456 |
| 0.2791 | 26.0 | 234 | 0.3488 | 0.2489 |
| 0.2399 | 27.0 | 243 | 0.2980 | 0.2611 |
| 0.2592 | 28.0 | 252 | 0.2942 | 0.27 |
| 0.2191 | 29.0 | 261 | 0.2921 | 0.2833 |
| 0.2285 | 30.0 | 270 | 0.2851 | 0.2744 |
| 0.2285 | 31.0 | 279 | 0.2794 | 0.2733 |
| 0.2489 | 32.0 | 288 | 0.3036 | 0.2678 |
| 0.2445 | 33.0 | 297 | 0.2851 | 0.2678 |
| 0.2261 | 34.0 | 306 | 0.2864 | 0.2733 |
| 0.2391 | 35.0 | 315 | 0.3055 | 0.2611 |
| 0.3939 | 36.0 | 324 | 0.2927 | 0.26 |
| 0.2521 | 37.0 | 333 | 0.3470 | 0.2578 |
| 0.2378 | 38.0 | 342 | 0.2841 | 0.2656 |
| 0.2653 | 39.0 | 351 | 0.2889 | 0.2389 |
| 0.2235 | 40.0 | 360 | 0.3176 | 0.25 |
| 0.2235 | 41.0 | 369 | 0.3188 | 0.2667 |
| 0.2474 | 42.0 | 378 | 0.3782 | 0.2633 |
| 0.222 | 43.0 | 387 | 0.3201 | 0.2767 |
| 0.2411 | 44.0 | 396 | 0.3416 | 0.2722 |
| 0.2561 | 45.0 | 405 | 0.3050 | 0.2711 |
| 0.2169 | 46.0 | 414 | 0.3968 | 0.2511 |
| 0.2296 | 47.0 | 423 | 0.3721 | 0.2567 |
| 0.1989 | 48.0 | 432 | 0.3205 | 0.2667 |
| 0.2408 | 49.0 | 441 | 0.4524 | 0.2489 |
| 0.2163 | 50.0 | 450 | 0.4850 | 0.2567 |
| 0.2163 | 51.0 | 459 | 0.3777 | 0.2711 |
| 0.2001 | 52.0 | 468 | 0.5526 | 0.2644 |
| 0.2373 | 53.0 | 477 | 0.5141 | 0.2589 |
| 0.2132 | 54.0 | 486 | 0.5408 | 0.2611 |
| 0.2687 | 55.0 | 495 | 0.5389 | 0.2678 |
| 0.2244 | 56.0 | 504 | 0.5729 | 0.2578 |
| 0.2102 | 57.0 | 513 | 0.6249 | 0.2489 |
| 0.2076 | 58.0 | 522 | 0.5538 | 0.25 |
| 0.208 | 59.0 | 531 | 0.5499 | 0.2467 |
| 0.2167 | 60.0 | 540 | 0.6481 | 0.2433 |
| 0.2167 | 61.0 | 549 | 0.6797 | 0.2589 |
| 0.2218 | 62.0 | 558 | 0.5401 | 0.2656 |
| 0.2102 | 63.0 | 567 | 0.5152 | 0.26 |
| 0.2176 | 64.0 | 576 | 0.5581 | 0.26 |
| 0.2068 | 65.0 | 585 | 0.7225 | 0.2533 |
| 0.2123 | 66.0 | 594 | 0.6330 | 0.2633 |
| 0.2212 | 67.0 | 603 | 0.5943 | 0.2589 |
| 0.2013 | 68.0 | 612 | 0.7557 | 0.25 |
| 0.2304 | 69.0 | 621 | 0.9144 | 0.2467 |
| 0.209 | 70.0 | 630 | 0.7790 | 0.24 |
| 0.209 | 71.0 | 639 | 0.6203 | 0.2411 |
| 0.191 | 72.0 | 648 | 0.6280 | 0.2322 |
| 0.2313 | 73.0 | 657 | 0.5491 | 0.2378 |
| 0.1869 | 74.0 | 666 | 0.4653 | 0.2411 |
| 0.2313 | 75.0 | 675 | 0.6016 | 0.2489 |
| 0.1806 | 76.0 | 684 | 0.6492 | 0.2478 |
| 0.1934 | 77.0 | 693 | 0.6185 | 0.2478 |
| 0.1954 | 78.0 | 702 | 0.5618 | 0.2489 |
| 0.2077 | 79.0 | 711 | 0.5760 | 0.2522 |
| 0.2052 | 80.0 | 720 | 0.6172 | 0.25 |
| 0.2052 | 81.0 | 729 | 0.6859 | 0.2467 |
| 0.1804 | 82.0 | 738 | 0.7643 | 0.2422 |
| 0.1995 | 83.0 | 747 | 0.8360 | 0.2367 |
| 0.1869 | 84.0 | 756 | 0.6984 | 0.2489 |
| 0.2135 | 85.0 | 765 | 0.6759 | 0.2422 |
| 0.178 | 86.0 | 774 | 0.6791 | 0.2444 |
| 0.1734 | 87.0 | 783 | 0.7284 | 0.2411 |
| 0.1881 | 88.0 | 792 | 0.8172 | 0.2344 |
| 0.1625 | 89.0 | 801 | 0.8061 | 0.2356 |
| 0.181 | 90.0 | 810 | 0.7644 | 0.2389 |
| 0.181 | 91.0 | 819 | 0.7413 | 0.24 |
| 0.1942 | 92.0 | 828 | 0.6439 | 0.2433 |
| 0.1806 | 93.0 | 837 | 0.6250 | 0.2467 |
| 0.1651 | 94.0 | 846 | 0.6517 | 0.2433 |
| 0.1833 | 95.0 | 855 | 0.6628 | 0.2389 |
| 0.1873 | 96.0 | 864 | 0.6582 | 0.2378 |
| 0.1672 | 97.0 | 873 | 0.6548 | 0.2389 |
| 0.1871 | 98.0 | 882 | 0.6655 | 0.24 |
| 0.2429 | 99.0 | 891 | 0.6695 | 0.24 |
| 0.1832 | 100.0 | 900 | 0.6700 | 0.2389 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.8.1+cu111
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter | [
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"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
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} | 12 | null | ---
language:
- en
license: apache-2.0
tags:
- t5-small
- text2text-generation
- natural language generation
- conversational system
- task-oriented dialog
datasets:
- ConvLab/tm1
- ConvLab/tm2
- ConvLab/tm3
metrics:
- Slot Error Rate
- sacrebleu
model-index:
- name: t5-small-nlg-tm1_tm2_tm3
results:
- task:
type: text2text-generation
name: natural language generation
dataset:
type: ConvLab/tm1, ConvLab/tm2, ConvLab/tm3
name: TM1+TM2+TM3
split: test
metrics:
- type: Slot Error Rate
value: 2.1
name: SER
- type: sacrebleu
value: 51.5
name: BLEU
widget:
- text: "tm1: [inform][pizza_ordering]([name.store][Domino's])\n\nsystem: "
- text: "tm2: [inform][restaurant-search]([name.restaurant][Via 313, the Violet Crown Social Club],[price_range][$8 per slice])\n\nsystem: "
- text: "tm3: [inform][movie]([name.movie][Star Wars],[name.movie][The Grudge])\n\nsystem: "
inference:
parameters:
max_length: 100
---
# t5-small-nlg-tm1_tm2_tm3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [Taskmaster-1](https://huggingface.co/datasets/ConvLab/tm1), [Taskmaster-2](https://huggingface.co/datasets/ConvLab/tm2), and [Taskmaster-3](https://huggingface.co/datasets/ConvLab/tm3).
Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
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} | 0 | null | ---
language:
- en
license: apache-2.0
tags:
- t5-small
- text2text-generation
- natural language understanding
- conversational system
- task-oriented dialog
datasets:
- ConvLab/multiwoz21
metrics:
- Dialog acts Accuracy
- Dialog acts F1
model-index:
- name: t5-small-nlu-multiwoz21
results:
- task:
type: text2text-generation
name: natural language understanding
dataset:
type: ConvLab/multiwoz21
name: MultiWOZ 2.1
split: test
revision: 5f55375edbfe0270c20bcf770751ad982c0e6614
metrics:
- type: Dialog acts Accuracy
value: 77.8
name: Accuracy
- type: Dialog acts F1
value: 86.5
name: F1
widget:
- text: "user: I would like a taxi from Saint John's college to Pizza Hut Fen Ditton."
- text: "user: we are staying 6 people for 4 nights starting from Tuesday. i need the reference number"
inference:
parameters:
max_length: 100
---
# t5-small-nlu-multiwoz21
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21).
Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
CLAck/en-km | [
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"marian",
"text2text-generation",
"transformers",
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"autotrain_compatible"
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} | 12 | null | ---
language:
- en
license: apache-2.0
tags:
- t5-small
- text2text-generation
- natural language understanding
- conversational system
- task-oriented dialog
datasets:
- ConvLab/sgd
metrics:
- Dialog acts Accuracy
- Dialog acts F1
model-index:
- name: t5-small-nlu-sgd
results:
- task:
type: text2text-generation
name: natural language understanding
dataset:
type: ConvLab/sgd
name: SGD
split: test
revision: 6e8c79b888b21cc658cf9c0ce128d263241cf70f
metrics:
- type: Dialog acts Accuracy
value: 45.0
name: Accuracy
- type: Dialog acts F1
value: 58.6
name: F1
widget:
- text: "user: Could you get me a reservation at P.f. Chang's in Corte Madera at afternoon 12?"
- text: "user: Sure, may I know if they have vegetarian options and how expensive is their food?"
inference:
parameters:
max_length: 100
---
# t5-small-nlu-sgd
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd).
Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
CLAck/en-vi | [
"pytorch",
"marian",
"text2text-generation",
"en",
"vi",
"dataset:ALT",
"transformers",
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"license:apache-2.0",
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-multilingual-uncased-en-de-fr-nov-24-epoch-1
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-multilingual-uncased-en-de-fr-nov-24-epoch-1
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0337
- F1: 0.9421
## 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: 4e-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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0557 | 1.0 | 6468 | 0.0337 | 0.9421 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CLAck/indo-mixed | [
"pytorch",
"marian",
"text2text-generation",
"en",
"id",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
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} | 15 | null | ---
language:
- en
license: apache-2.0
tags:
- t5-small
- text2text-generation
- natural language understanding
- conversational system
- task-oriented dialog
datasets:
- ConvLab/tm1
- ConvLab/tm2
- ConvLab/tm3
metrics:
- Dialog acts Accuracy
- Dialog acts F1
model-index:
- name: t5-small-nlu-tm1_tm2_tm3
results:
- task:
type: text2text-generation
name: natural language understanding
dataset:
type: ConvLab/tm1, ConvLab/tm2, ConvLab/tm3
name: TM1+TM2+TM3
split: test
metrics:
- type: Dialog acts Accuracy
value: 81.8
name: Accuracy
- type: Dialog acts F1
value: 73.0
name: F1
widget:
- text: "tm1: user: I would like to order a pizza from Domino's."
- text: "tm2: user: I would like help getting a flight from LA to Amsterdam."
- text: "tm3: user: Well, I need a kids friendly movie. I was thinking about seeing Mulan."
inference:
parameters:
max_length: 100
---
# t5-small-nlu-tm1_tm2_tm3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [Taskmaster-1](https://huggingface.co/datasets/ConvLab/tm1), [Taskmaster-2](https://huggingface.co/datasets/ConvLab/tm2), and [Taskmaster-3](https://huggingface.co/datasets/ConvLab/tm3).
Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
CLAck/indo-pure | [
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"text2text-generation",
"en",
"id",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 4 | null | ---
language:
- en
license: apache-2.0
tags:
- t5-small
- text2text-generation
- natural language understanding
- conversational system
- task-oriented dialog
datasets:
- ConvLab/multiwoz21
- ConvLab/sgd
- ConvLab/tm1
- ConvLab/tm2
- ConvLab/tm3
metrics:
- Slot Error Rate
- sacrebleu
model-index:
- name: t5-small-nlu-multiwoz21_sgd_tm1_tm2_tm3
results:
- task:
type: text2text-generation
name: natural language understanding
dataset:
type: ConvLab/multiwoz21
name: MultiWOZ 2.1
split: test
revision: 5f55375edbfe0270c20bcf770751ad982c0e6614
metrics:
- type: Dialog acts Accuracy
value: 77.5
name: Accuracy
- type: Dialog acts F1
value: 86.4
name: F1
- task:
type: text2text-generation
name: natural language understanding
dataset:
type: ConvLab/sgd
name: SGD
split: test
revision: 6e8c79b888b21cc658cf9c0ce128d263241cf70f
metrics:
- type: Dialog acts Accuracy
value: 45.2
name: Accuracy
- type: Dialog acts F1
value: 58.6
name: F1
- task:
type: text2text-generation
name: natural language understanding
dataset:
type: ConvLab/tm1, ConvLab/tm2, ConvLab/tm3
name: TM1+TM2+TM3
split: test
metrics:
- type: Dialog acts Accuracy
value: 81.8
name: Accuracy
- type: Dialog acts F1
value: 73.0
name: F1
widget:
- text: "multiwoz21: user: I would like a taxi from Saint John's college to Pizza Hut Fen Ditton."
example_title: "MultiWOZ 2.1"
- text: "sgd: user: Could you get me a reservation at P.f. Chang's in Corte Madera at afternoon 12?"
example_title: "Schema-Guided Dialog"
- text: "tm1: user: I would like to order a pizza from Domino's."
example_title: "Taskmaster-1"
- text: "tm2: user: I would like help getting a flight from LA to Amsterdam."
example_title: "Taskmaster-2"
- text: "tm3: user: Well, I need a kids friendly movie. I was thinking about seeing Mulan."
example_title: "Taskmaster-3"
inference:
parameters:
max_length: 100
---
# t5-small-nlu-multiwoz21_sgd_tm1_tm2_tm3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21), [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd), [Taskmaster-1](https://huggingface.co/datasets/ConvLab/tm1), [Taskmaster-2](https://huggingface.co/datasets/ConvLab/tm2), and [Taskmaster-3](https://huggingface.co/datasets/ConvLab/tm3).
Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
CLEE/CLEE | []
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} | 0 | 2022-11-25T04:56:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-1b-korean-sample2
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. -->
# wav2vec2-large-xls-r-1b-korean-sample2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1283
- Cer: 0.0294
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3415 | 1.0 | 11471 | 0.2666 | 0.0750 |
| 0.1997 | 2.0 | 22942 | 0.1617 | 0.0415 |
| 0.1153 | 3.0 | 34413 | 0.1283 | 0.0294 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.11.0
|
CLTL/MedRoBERTa.nl | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"transformers",
"license:mit",
"autotrain_compatible"
]
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} | 2,988 | null | ---
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
datasets:
- ai_light_dance
metrics:
- wer
model-index:
- name: ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-mdb-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. -->
# ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-mdb-2
This model is a fine-tuned version of [gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-mdb-2](https://huggingface.co/gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-mdb-2) on the GARY109/AI_LIGHT_DANCE - ONSET-IDMT-MDB-2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4204
- Wer: 0.1844
## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2599 | 0.98 | 11 | 0.4281 | 0.2198 |
| 0.2491 | 1.98 | 22 | 0.4891 | 0.1947 |
| 0.2619 | 2.98 | 33 | 0.5496 | 0.2183 |
| 0.3354 | 3.98 | 44 | 0.5202 | 0.2094 |
| 0.277 | 4.98 | 55 | 0.4574 | 0.2080 |
| 0.3065 | 5.98 | 66 | 0.4749 | 0.2080 |
| 0.2669 | 6.98 | 77 | 0.5902 | 0.2183 |
| 0.2829 | 7.98 | 88 | 0.8560 | 0.2050 |
| 0.2509 | 8.98 | 99 | 0.6190 | 0.2035 |
| 0.2728 | 9.98 | 110 | 0.6562 | 0.2109 |
| 0.2615 | 10.98 | 121 | 0.6291 | 0.2065 |
| 0.2586 | 11.98 | 132 | 0.6167 | 0.1844 |
| 0.2441 | 12.98 | 143 | 0.6736 | 0.1962 |
| 0.233 | 13.98 | 154 | 0.5727 | 0.2050 |
| 0.2567 | 14.98 | 165 | 0.6165 | 0.1873 |
| 0.2264 | 15.98 | 176 | 0.7506 | 0.2080 |
| 0.2346 | 16.98 | 187 | 0.7017 | 0.1888 |
| 0.2343 | 17.98 | 198 | 0.5930 | 0.2094 |
| 0.2638 | 18.98 | 209 | 0.5730 | 0.2006 |
| 0.2543 | 19.98 | 220 | 0.4991 | 0.2198 |
| 0.2476 | 20.98 | 231 | 0.6364 | 0.2065 |
| 0.2777 | 21.98 | 242 | 0.6247 | 0.1844 |
| 0.2661 | 22.98 | 253 | 0.5589 | 0.2006 |
| 0.2094 | 23.98 | 264 | 0.5316 | 0.2080 |
| 0.2496 | 24.98 | 275 | 0.8821 | 0.1844 |
| 0.2302 | 25.98 | 286 | 0.5408 | 0.1814 |
| 0.2651 | 26.98 | 297 | 0.6479 | 0.2094 |
| 0.2119 | 27.98 | 308 | 0.5875 | 0.1814 |
| 0.2468 | 28.98 | 319 | 0.7614 | 0.1976 |
| 0.2239 | 29.98 | 330 | 0.4908 | 0.1903 |
| 0.2514 | 30.98 | 341 | 0.5196 | 0.2035 |
| 0.2244 | 31.98 | 352 | 0.5580 | 0.1991 |
| 0.2524 | 32.98 | 363 | 0.5342 | 0.2021 |
| 0.2516 | 33.98 | 374 | 0.4204 | 0.1844 |
| 0.2515 | 34.98 | 385 | 0.5135 | 0.2124 |
| 0.2542 | 35.98 | 396 | 0.8150 | 0.1962 |
| 0.2269 | 36.98 | 407 | 0.8833 | 0.2094 |
| 0.212 | 37.98 | 418 | 1.3235 | 0.2183 |
| 0.2119 | 38.98 | 429 | 0.6919 | 0.2021 |
| 0.2228 | 39.98 | 440 | 0.6712 | 0.2021 |
| 0.2127 | 40.98 | 451 | 0.7557 | 0.1976 |
| 0.2064 | 41.98 | 462 | 0.5918 | 0.1947 |
| 0.2147 | 42.98 | 473 | 0.8049 | 0.1962 |
| 0.193 | 43.98 | 484 | 0.7117 | 0.1976 |
| 0.2063 | 44.98 | 495 | 0.5544 | 0.1962 |
| 0.1989 | 45.98 | 506 | 0.5782 | 0.1888 |
| 0.2193 | 46.98 | 517 | 0.5216 | 0.1947 |
| 0.2012 | 47.98 | 528 | 0.5269 | 0.1917 |
| 0.2187 | 48.98 | 539 | 0.4636 | 0.1844 |
| 0.2128 | 49.98 | 550 | 0.4968 | 0.1888 |
| 0.2041 | 50.98 | 561 | 0.4784 | 0.1888 |
| 0.1993 | 51.98 | 572 | 0.5592 | 0.1755 |
| 0.1981 | 52.98 | 583 | 0.4871 | 0.1785 |
| 0.1808 | 53.98 | 594 | 0.4771 | 0.1740 |
| 0.2317 | 54.98 | 605 | 0.5285 | 0.1814 |
| 0.1906 | 55.98 | 616 | 0.5485 | 0.1844 |
| 0.1924 | 56.98 | 627 | 0.5615 | 0.1814 |
| 0.1761 | 57.98 | 638 | 0.4604 | 0.1799 |
| 0.2047 | 58.98 | 649 | 0.4223 | 0.1829 |
| 0.1992 | 59.98 | 660 | 0.4706 | 0.1873 |
| 0.1949 | 60.98 | 671 | 0.4633 | 0.1844 |
| 0.2034 | 61.98 | 682 | 0.4854 | 0.1814 |
| 0.2147 | 62.98 | 693 | 0.4489 | 0.1844 |
| 0.2135 | 63.98 | 704 | 0.4874 | 0.1726 |
| 0.2021 | 64.98 | 715 | 0.4635 | 0.1814 |
| 0.1822 | 65.98 | 726 | 0.4813 | 0.1785 |
| 0.1882 | 66.98 | 737 | 0.5076 | 0.1799 |
| 0.2014 | 67.98 | 748 | 0.5183 | 0.1888 |
| 0.1869 | 68.98 | 759 | 0.5035 | 0.1799 |
| 0.1914 | 69.98 | 770 | 0.4694 | 0.1844 |
| 0.1972 | 70.98 | 781 | 0.4485 | 0.1844 |
| 0.1724 | 71.98 | 792 | 0.4579 | 0.1829 |
| 0.195 | 72.98 | 803 | 0.5178 | 0.1814 |
| 0.2017 | 73.98 | 814 | 0.4978 | 0.1829 |
| 0.1874 | 74.98 | 825 | 0.5035 | 0.1873 |
| 0.1925 | 75.98 | 836 | 0.5495 | 0.1829 |
| 0.1845 | 76.98 | 847 | 0.5394 | 0.1799 |
| 0.1718 | 77.98 | 858 | 0.5070 | 0.1711 |
| 0.1824 | 78.98 | 869 | 0.4912 | 0.1770 |
| 0.1702 | 79.98 | 880 | 0.4632 | 0.1726 |
| 0.1563 | 80.98 | 891 | 0.4412 | 0.1726 |
| 0.1858 | 81.98 | 902 | 0.4635 | 0.1667 |
| 0.1701 | 82.98 | 913 | 0.4838 | 0.1726 |
| 0.188 | 83.98 | 924 | 0.4775 | 0.1814 |
| 0.1789 | 84.98 | 935 | 0.4801 | 0.1740 |
| 0.2134 | 85.98 | 946 | 0.4542 | 0.1785 |
| 0.2141 | 86.98 | 957 | 0.4499 | 0.1785 |
| 0.1599 | 87.98 | 968 | 0.4595 | 0.1770 |
| 0.1927 | 88.98 | 979 | 0.4772 | 0.1755 |
| 0.1709 | 89.98 | 990 | 0.4588 | 0.1770 |
| 0.1588 | 90.98 | 1001 | 0.4607 | 0.1785 |
| 0.1702 | 91.98 | 1012 | 0.4656 | 0.1829 |
| 0.1646 | 92.98 | 1023 | 0.4631 | 0.1829 |
| 0.1867 | 93.98 | 1034 | 0.4758 | 0.1814 |
| 0.1799 | 94.98 | 1045 | 0.4820 | 0.1755 |
| 0.1611 | 95.98 | 1056 | 0.4846 | 0.1785 |
| 0.1685 | 96.98 | 1067 | 0.4816 | 0.1770 |
| 0.19 | 97.98 | 1078 | 0.4781 | 0.1770 |
| 0.1953 | 98.98 | 1089 | 0.4767 | 0.1770 |
| 0.188 | 99.98 | 1100 | 0.4774 | 0.1770 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.8.1+cu111
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
CM-CA/Cartman | []
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} | 0 | 2022-11-25T06:06:18Z | ---
license: creativeml-openrail-m
language:
- en
tags:
- stable-diffusion
- text-to-image
- diffusers
---
# Inkpunk Diffusion
Finetuned Stable Diffusion model trained on dreambooth. Vaguely inspired by Gorillaz, FLCL, and Yoji Shinkawa. Use **_nvinkpunk_** in your prompts.
# Gradio
We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Inkpunk-Diffusion:
[](https://huggingface.co/spaces/akhaliq/Inkpunk-Diffusion)
# Sample images
 |
CNT-UPenn/RoBERTa_for_seizureFrequency_QA | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-xsum-rahul2
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. -->
# t5-small-finetuned-xsum-rahul2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 63 | 1.3966 | 24.7113 | 17.3364 | 22.3967 | 24.026 | 19.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Calamarii/calamari | []
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} | 0 | null | ---
license: mit
---
This is the https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K model converted to a simple CLIPTokenizer model for use with https://huggingface.co/stabilityai/stable-diffusion-2
I produced this tokenizer when trying to update the ckpt converter in Diffusers to work with Stable Diffusion V2, and I personally think it often gives better results than the stock tokenizer.
|
CallumRai/HansardGPT2 | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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} | 14 | 2022-11-25T06:28:56Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.4864864864864865
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3826
- Accuracy: 0.4865
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.9 | 7 | 1.4323 | 0.4865 |
| 1.5843 | 1.9 | 14 | 1.3999 | 0.4865 |
| 1.5007 | 2.9 | 21 | 1.3826 | 0.4865 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Canadiancaleb/DialoGPT-small-walter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 13 | null | ---
language:
- en
license: apache-2.0
tags:
- t5-small
- text2text-generation
- dialog state tracking
- conversational system
- task-oriented dialog
datasets:
- ConvLab/sgd
metrics:
- Joint Goal Accuracy
- Slot F1
model-index:
- name: t5-small-dst-sgd
results:
- task:
type: text2text-generation
name: dialog state tracking
dataset:
type: ConvLab/sgd
name: SGD
split: test
revision: 6e8c79b888b21cc658cf9c0ce128d263241cf70f
metrics:
- type: Joint Goal Accuracy
value: 20.1
name: JGA
- type: Slot F1
value: 58.5
name: Slot F1
widget:
- text: "user: Hi, could you get me a restaurant booking on the 8th please?\nsystem: Any preference on the restaurant, location and time?\nuser: Could you get me a reservation at P.f. Chang's in Corte Madera at afternoon 12?"
- text: "user: I need to book a dinner reservation for a date. Help me reserve a table at a restaurant.\nsystem: What time and location do you have in mind?\nuser: Something around 8 in the night should be fine. Oh, and look in the San Jose area."
inference:
parameters:
max_length: 100
---
# t5-small-dst-sgd
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd).
Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
CarlosPR/mt5-spanish-memmories-analysis | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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} | 7 | null | Access to model inferenceendpoints/gated-model is restricted and you are not in the authorized list. Visit https://huggingface.co/inferenceendpoints/gated-model to ask for access. |
CasualHomie/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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}
} | 11 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc
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. -->
# xlm-roberta-base-finetuned-marc
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9725
- Mae: 0.5221
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1615 | 1.0 | 308 | 1.0893 | 0.6106 |
| 0.9994 | 2.0 | 616 | 0.9725 | 0.5221 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
dccuchile/albert-tiny-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 5 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlr-roberta-base-finetuned-panx-de-fr
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. -->
# xlr-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1608
- F1: 0.8593
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 |
| 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 |
| 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
dccuchile/albert-tiny-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"AlbertForQuestionAnswering"
],
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-sumups
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. -->
# bert-finetuned-ner-sumups
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9498
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.2605
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 2 | 2.0593 | 0.0 | 0.0 | 0.0 | 0.2347 |
| No log | 2.0 | 4 | 1.9693 | 0.0 | 0.0 | 0.0 | 0.2632 |
| No log | 3.0 | 6 | 1.9498 | 0.0 | 0.0 | 0.0 | 0.2605 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
dccuchile/albert-tiny-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
} | 31 | null | ---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whispertestlocal
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. -->
# whispertestlocal
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4481
- Wer: 46.1754
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1886 | 1.12 | 100 | 0.4481 | 46.1754 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.2
|
dccuchile/albert-xlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
} | 26 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- kejian/codeparrot-train-more-filter-3.3b-cleaned
model-index:
- name: kejian/final-cond-10-0.01
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. -->
# kejian/final-cond-10-0.01
This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0},
'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'],
'is_split_by_sentences': True},
'generation': {'batch_size': 64,
'metrics_configs': [{}, {'n': 1}, {}],
'scenario_configs': [{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 704,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 512,
'prefix': '<|aligned|>',
'use_prompt_for_scoring': False},
{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 272,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'functions',
'num_samples': 512,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/functions_csnet.jsonl',
'use_prompt_for_scoring': True}],
'scorer_config': {}},
'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'codeparrot/codeparrot-small'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/final-cond-10-0.01',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0008,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000.0,
'output_dir': 'training_output',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 5000,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/1wgqepja |
dccuchile/albert-xlarge-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
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"AlbertForSequenceClassification"
],
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} | 29 | null | ---
language: pl
tags:
- text-classification
- financial-sentiment-analysis
- sentiment-analysis
datasets:
- datasets/financial_phrasebank
metrics:
- f1
- accuracy
- precision
- recall
widget:
- text: "Sprzedaż netto wzrosła o 30% do 36 mln EUR."
example_title: "Example 1"
- text: "Rusza Black Friday. Lista promocji w sklepach."
example_title: "Example 2"
- text: "Akcje CDPROJEKT zanotowały największy spadek wśród spółek notowanych na GPW."
example_title: "Example 3"
---
# Finance Sentiment PL (fast)
Finance Sentiment PL (fast) is a [distiluse](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1)-based model for analyzing sentiment of Polish financial news. It was trained on the translated version of [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (20014) for 10 epochs on single RTX3090 gpu.
The model will give you a three labels: positive, negative and neutral.
## How to use
You can use this model directly with a pipeline for sentiment-analysis:
```python
from transformers import pipeline
nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-pl-fast")
nlp("Sprzedaż netto wzrosła o 30% do 36 mln EUR.")
```
```bash
[{'label': 'positive', 'score': 0.9999998807907104}]
```
## Performance
| Metric | Value |
| --- | ----------- |
| f1 macro | 0.933 |
| precision macro | 0.950 |
| recall macro | 0.918 |
| accuracy | 0.944 |
| samples per second | 268.1 |
(The performance was evaluated on RTX 3090 gpu)
## Changelog
- 2022-12-01: Rename the model to finance-sentiment-pl-base
- 2022-11-15: Initial release
## About bards.ai
At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/)
Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected]
|
dccuchile/albert-xxlarge-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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}
} | 28 | null | ---
license: apache-2.0
---
## PlanTL Project's Spanish-Catalan machine translation model
## Table of Contents
- [Model Description](#model-description)
- [Intended Uses and Limitations](#intended-use)
- [How to Use](#how-to-use)
- [Training](#training)
- [Training data](#training-data)
- [Training procedure](#training-procedure)
- [Data Preparation](#data-preparation)
- [Tokenization](#tokenization)
- [Hyperparameters](#hyperparameters)
- [Evaluation](#evaluation)
- [Variable and Metrics](#variable-and-metrics)
- [Evaluation Results](#evaluation-results)
- [Additional Information](#additional-information)
- [Author](#author)
- [Contact Information](#contact-information)
- [Copyright](#copyright)
- [Licensing Information](#licensing-information)
- [Funding](#funding)
- [Disclaimer](#disclaimer)
## Model description
This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Spanish-Catalan datasets, up to 92 million sentences. Additionally, the model is evaluated on several public datasecomprising 5 different domains (general, adminstrative, technology, biomedical, and news).
## Intended uses and limitations
You can use this model for machine translation from Spanish to Catalan.
## How to use
### Usage
Required libraries:
```bash
pip install ctranslate2 pyonmttok
```
Translate a sentence using python
```python
import ctranslate2
import pyonmttok
from huggingface_hub import snapshot_download
model_dir = snapshot_download(repo_id="PlanTL-GOB-ES/mt-plantl-es-ca", revision="main")
tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
tokenized=tokenizer.tokenize("Bienvenido al Proyecto PlanTL!")
translator = ctranslate2.Translator(model_dir)
translated = translator.translate_batch([tokenized[0]])
print(tokenizer.detokenize(translated[0][0]['tokens']))
```
## Training
### Training data
The model was trained on a combination of the following datasets:
| Dataset | Sentences | Tokens |
|-------------------|----------------|-------------------|
| DOCG v2 | 8.472.786 | 188.929.206 |
| El Periodico | 6.483.106 | 145.591.906 |
| EuroParl | 1.876.669 | 49.212.670 |
| WikiMatrix | 1.421.077 | 34.902.039 |
| Wikimedia | 335.955 | 8.682.025 |
| QED | 71.867 | 1.079.705 |
| TED2020 v1 | 52.177 | 836.882 |
| CCMatrix v1 | 56.103.820 | 1.064.182.320 |
| MultiCCAligned v1 | 2.433.418 | 48.294.144 |
| ParaCrawl | 15.327.808 | 334.199.408 |
| **Total** | **92.578.683** | **1.875.910.305** |
### Training procedure
### Data preparation
All datasets are concatenated and filtered using the [mBERT Gencata parallel filter](https://huggingface.co/projecte-aina/mbert-base-gencata) and cleaned using the clean-corpus-n.pl script from [moses](https://github.com/moses-smt/mosesdecoder), allowing sentences between 5 and 150 words.
Before training, the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py)
#### Tokenization
All data is tokenized using sentencepiece, with 50 thousand token sentencepiece model learned from the combination of all filtered training data. This model is included.
#### Hyperparameters
The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf)
The following hyperparamenters were set on the Fairseq toolkit:
| Hyperparameter | Value |
|------------------------------------|----------------------------------|
| Architecture | transformer_vaswani_wmt_en_de_bi |
| Embedding size | 1024 |
| Feedforward size | 4096 |
| Number of heads | 16 |
| Encoder layers | 24 |
| Decoder layers | 6 |
| Normalize before attention | True |
| --share-decoder-input-output-embed | True |
| --share-all-embeddings | True |
| Effective batch size | 96.000 |
| Optimizer | adam |
| Adam betas | (0.9, 0.980) |
| Clip norm | 0.0 |
| Learning rate | 1e-3 |
| Lr. schedurer | inverse sqrt |
| Warmup updates | 4000 |
| Dropout | 0.1 |
| Label smoothing | 0.1 |
The model was trained using shards of 10 million sentences, for a total of 8.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 6 checkpoints.
## Evaluation
### Variable and metrics
We use the BLEU score for evaluation on test sets: [Flores-101](https://github.com/facebookresearch/flores), [TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/), [United Nations](https://zenodo.org/record/3888414#.Y33-_tLMIW0), [Cybersecurity](https://elrc-share.eu/repository/browse/cyber-mt-test-set/2bd93faab98c11ec9c1a00155d026706b96a490ed3e140f0a29a80a08c46e91e/), [wmt19 biomedical test set](), [wmt13 news test set](https://elrc-share.eu/repository/browse/catalan-wmt2013-machine-translation-shared-task-test-set/84a96139b98611ec9c1a00155d0267061a0aa1b62e2248e89aab4952f3c230fc/)
### Evaluation results
Below are the evaluation results on the machine translation from Spanish to Catalan compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es):
| Test set | SoftCatalà | Google Translate |mt-plantl-es-ca|
|----------------------|------------|------------------|---------------|
| Spanish Constitution | **63,6** | 61,7 | 63,0 |
| United Nations | 73,8 | 74,8 | **74,9** |
| Flores 101 dev | 22 | **23,1** | 22,5 |
| Flores 101 devtest | 22,7 | **23,6** | 23,1 |
| Cybersecurity | 61,4 | **69,5** | 67,3 |
| wmt 19 biomedical | 60,2 | 59,7 | **60,6** |
| wmt 13 news | 21,3 | **22,4** | 22,0 |
| Average | 46,4 | **47,8** | 47,6 |
## Additional information
### Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
### Contact information
For further information, send an email to <[email protected]>
### Copyright
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Licensing information
This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SE
### Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
</details>
|
dccuchile/albert-base-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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} | 586 | 2022-11-25T09:38:49Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- kejian/codeparrot-train-more-filter-3.3b-cleaned
model-index:
- name: kejian/final-awr
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. -->
# kejian/final-awr
This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.001
- 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: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 12588
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'],
'is_split_by_sentences': True},
'generation': {'batch_size': 64,
'metrics_configs': [{}, {'n': 1}, {}],
'scenario_configs': [{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 640,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 512},
{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 272,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'functions',
'num_samples': 512,
'prompts_path': 'resources/functions_csnet.jsonl',
'use_prompt_for_scoring': True}],
'scorer_config': {}},
'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'},
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'value_head_config': {'is_detached': False}},
'path_or_name': 'codeparrot/codeparrot-small'},
'objective': {'alpha': 0.05, 'beta': 1, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 256,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/final-awr',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000.0,
'output_dir': 'training_output',
'per_device_eval_batch_size': 16,
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 5000,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/3a8ddwg4 |
dccuchile/albert-large-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
"architectures": [
"AlbertForPreTraining"
],
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},
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},
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},
"translation_en_to_fr": {
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}
} | 75 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- kejian/codeparrot-train-more-filter-3.3b-cleaned
model-index:
- name: kejian/final-ul
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. -->
# kejian/final-ul
This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'],
'is_split_by_sentences': True},
'generation': {'batch_size': 64,
'metrics_configs': [{}, {'n': 1}, {}],
'scenario_configs': [{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 640,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 512},
{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 272,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'functions',
'num_samples': 512,
'prompts_path': 'resources/functions_csnet.jsonl',
'use_prompt_for_scoring': True}],
'scorer_config': {}},
'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'},
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'path_or_name': 'codeparrot/codeparrot-small'},
'objective': {'alpha': 0.01, 'name': 'Unlikelihood', 'score_threshold': 0},
'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/final-ul',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0008,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000.0,
'output_dir': 'training_output',
'per_device_eval_batch_size': 16,
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 5000,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/e4ldamat |
dccuchile/albert-xlarge-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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"AlbertForPreTraining"
],
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},
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},
"translation_en_to_fr": {
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}
}
} | 91 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.fr
split: train
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8346456692913387
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2763
- F1: 0.8346
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5779 | 1.0 | 191 | 0.3701 | 0.7701 |
| 0.2735 | 2.0 | 382 | 0.2908 | 0.8254 |
| 0.1769 | 3.0 | 573 | 0.2763 | 0.8346 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
dccuchile/albert-xxlarge-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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"AlbertForPreTraining"
],
"model_type": "albert",
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},
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},
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}
}
} | 42 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: gpt2-Georges-sand
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. -->
# gpt2-Georges-sand
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 10.8530
- Validation Loss: 10.7406
- 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -993, '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: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.9815 | 10.9683 | 0 |
| 10.9422 | 10.8815 | 1 |
| 10.8530 | 10.7406 | 2 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.10.0
- Datasets 2.7.0
- Tokenizers 0.13.2
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-ner | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 81 | 2022-11-25T10:18:57Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: wonderful_keller
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. -->
# wonderful_keller
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'filter_threshold': 0.00078,
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'wonderful_keller',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/354p6jl4 |
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 25 | 2022-11-25T10:18:57Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: hungry_saha
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. -->
# hungry_saha
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.00056},
'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048,
'prefix': '<|aligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'hungry_saha',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/22upd61h |
dccuchile/bert-base-spanish-wwm-cased-finetuned-pos | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1 | 2022-11-25T10:19:02Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: goofy_pasteur
results: []
---
# goofy_pasteur
- **Repository: https://github.com/tomekkorbak/aligned-pretraining-objectives**
- **Paper: Arxiv link to be added**
## Model description
This model was trained using [pile-detoxify](https://huggingface.co/datasets/tomekkorbak/pile-detoxify), which is data from [The Pile](https://huggingface.co/datasets/the_pile), annotated based on toxicity detected by [Detoxify](https://github.com/unitaryai/detoxify).
## Intended uses & limitations
This model has been trained to generate text that receives a low score for toxicity from [Detoxify](https://github.com/unitaryai/detoxify).
While we have promising results with the methods used to avoid toxic text, we cannot guarantee that it will output text that is fully aligned with non-toxicity in every situation.
This model and its associated datasets are intended for research purposes only and should not be deployed anywhere.
Please take care to avoid misusing the datasets used to train this model (where toxicity and personal identifiable information are annotated) or putting anybody in danger by publicizing their information.
## Training and evaluation data
This model was trained using [pile-detoxify](https://huggingface.co/datasets/tomekkorbak/pile-detoxify).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'goofy_pasteur',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/20d87pk8 |
dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 5 | 2022-11-25T10:19:02Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: nifty_banach
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. -->
# nifty_banach
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'path_or_name': 'gpt2'},
'objective': {'alpha': 1, 'name': 'Unlikelihood', 'score_threshold': 0.00078},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'nifty_banach',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1adikmkl |
dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 28 | 2022-11-25T10:19:12Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: gallant_thompson
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. -->
# gallant_thompson
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## 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: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 3147
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'value_head_config': {'is_detached': False}},
'path_or_name': 'gpt2'},
'objective': {'alpha': 0.5, 'beta': 10, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 1024,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'gallant_thompson',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1fjxzr1j |
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 24 | 2022-11-25T10:28:04Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: frosty_lamport
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. -->
# frosty_lamport
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 3147
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'value_head_config': {'is_detached': False}},
'path_or_name': 'gpt2'},
'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 1024,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'frosty_lamport',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/3qaspcth |
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pos | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 5 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
### huggingface2 Dreambooth model trained by osanseviero with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training)
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
(use that on your prompt)
(use that on your prompt)
(use that on your prompt)
(use that on your prompt)
(use that on your prompt)
(use that on your prompt)
(use that on your prompt)
(use that on your prompt)
(use that on your prompt)
(use that on your prompt)
huggingface emoji (use that on your prompt)
|
Chalponkey/DialoGPT-small-Barry | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 11 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: train
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8124233755619126
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2630
- F1: 0.8124
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8193 | 1.0 | 70 | 0.3200 | 0.7356 |
| 0.2773 | 2.0 | 140 | 0.2841 | 0.7882 |
| 0.1807 | 3.0 | 210 | 0.2630 | 0.8124 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Cheatham/xlm-roberta-large-finetuned | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
]
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} | 20 | null | ---
license: other
---
Psychedelia Diffusion Model, and maybe others to come.
Tips for psychedelicmerger.ckpt:
High step count, ancestral samplers seem to give the best results. Using words that imply any form of psychedelia in the prompt should help to get it's style out, but may not be necessary.
if you want to try the training tokens, don't expect great results:
sdpsydiffsyle and sdpsydiffstylev2,
this model is a merge between two different training sets.
Also works nicely with pop art, phunkadelic, surreal etc.
Can't offer much advice on what CFG scale setting will work best typically, it seems pretty dependent on the prompt
the clip aesthetic/stylepile in webui seems to play nicely with this too, worth experimenting.
Have fun! |
Cheatham/xlm-roberta-large-finetuned3 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
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} | 22 | null | ---
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
datasets:
- ai_light_dance
metrics:
- wer
model-index:
- name: ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-2_8k
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. -->
# ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-2_8k
This model is a fine-tuned version of [gary109/ai-light-dance_drums_pretrain_wav2vec2-base-new](https://huggingface.co/gary109/ai-light-dance_drums_pretrain_wav2vec2-base-new) on the GARY109/AI_LIGHT_DANCE - ONSET-IDMT-2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5029
- Wer: 0.3178
## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 9 | 101.8046 | 0.98 |
| 17.4958 | 2.0 | 18 | 82.4920 | 1.0 |
| 16.2087 | 3.0 | 27 | 36.1388 | 1.0 |
| 6.2942 | 4.0 | 36 | 8.3267 | 1.0 |
| 2.0411 | 5.0 | 45 | 6.8215 | 1.0 |
| 1.554 | 6.0 | 54 | 5.3847 | 1.0 |
| 1.6215 | 7.0 | 63 | 4.4645 | 1.0 |
| 1.4962 | 8.0 | 72 | 3.2211 | 1.0 |
| 1.3825 | 9.0 | 81 | 2.5513 | 1.0 |
| 1.3443 | 10.0 | 90 | 2.8582 | 1.0 |
| 1.3443 | 11.0 | 99 | 2.5446 | 1.0 |
| 1.3096 | 12.0 | 108 | 2.0211 | 0.9956 |
| 1.3361 | 13.0 | 117 | 1.8110 | 0.9944 |
| 1.2862 | 14.0 | 126 | 1.7796 | 0.9933 |
| 1.2556 | 15.0 | 135 | 1.7301 | 0.9922 |
| 1.1959 | 16.0 | 144 | 1.4245 | 0.9989 |
| 1.1161 | 17.0 | 153 | 1.1932 | 0.5678 |
| 0.8853 | 18.0 | 162 | 1.2726 | 0.4922 |
| 0.7996 | 19.0 | 171 | 1.0841 | 0.5511 |
| 0.8165 | 20.0 | 180 | 1.4062 | 0.4411 |
| 0.8165 | 21.0 | 189 | 1.4219 | 0.3367 |
| 0.6807 | 22.0 | 198 | 1.2107 | 0.3344 |
| 0.7315 | 23.0 | 207 | 1.1420 | 0.3189 |
| 0.6203 | 24.0 | 216 | 1.0770 | 0.3778 |
| 0.6552 | 25.0 | 225 | 1.1095 | 0.3789 |
| 0.5618 | 26.0 | 234 | 1.0004 | 0.3478 |
| 0.5311 | 27.0 | 243 | 0.8811 | 0.3311 |
| 0.5391 | 28.0 | 252 | 0.8163 | 0.3678 |
| 0.5275 | 29.0 | 261 | 1.0000 | 0.3311 |
| 0.4965 | 30.0 | 270 | 0.7320 | 0.37 |
| 0.4965 | 31.0 | 279 | 0.9643 | 0.3389 |
| 0.4909 | 32.0 | 288 | 0.7663 | 0.3589 |
| 0.5218 | 33.0 | 297 | 0.9004 | 0.3489 |
| 0.4991 | 34.0 | 306 | 0.7342 | 0.38 |
| 0.4883 | 35.0 | 315 | 0.7959 | 0.3389 |
| 0.4902 | 36.0 | 324 | 0.6892 | 0.3378 |
| 0.4447 | 37.0 | 333 | 0.6480 | 0.3333 |
| 0.4458 | 38.0 | 342 | 0.6198 | 0.3333 |
| 0.4607 | 39.0 | 351 | 0.6081 | 0.3111 |
| 0.4352 | 40.0 | 360 | 0.6748 | 0.3156 |
| 0.4352 | 41.0 | 369 | 0.6885 | 0.3256 |
| 0.4286 | 42.0 | 378 | 0.6806 | 0.3333 |
| 0.4314 | 43.0 | 387 | 0.7855 | 0.3222 |
| 0.4476 | 44.0 | 396 | 0.6569 | 0.3144 |
| 0.4815 | 45.0 | 405 | 0.5389 | 0.3033 |
| 0.36 | 46.0 | 414 | 0.5550 | 0.3011 |
| 0.4516 | 47.0 | 423 | 0.5924 | 0.3144 |
| 0.3682 | 48.0 | 432 | 0.7275 | 0.3056 |
| 0.4371 | 49.0 | 441 | 0.7051 | 0.3089 |
| 0.4004 | 50.0 | 450 | 0.5669 | 0.3078 |
| 0.4004 | 51.0 | 459 | 0.5029 | 0.3178 |
| 0.3298 | 52.0 | 468 | 0.6150 | 0.32 |
| 0.4083 | 53.0 | 477 | 0.5882 | 0.33 |
| 0.4022 | 54.0 | 486 | 0.7253 | 0.3144 |
| 0.4465 | 55.0 | 495 | 0.6808 | 0.3111 |
| 0.3955 | 56.0 | 504 | 0.6002 | 0.3133 |
| 0.3877 | 57.0 | 513 | 0.7593 | 0.3056 |
| 0.3486 | 58.0 | 522 | 0.6764 | 0.3189 |
| 0.3782 | 59.0 | 531 | 0.6772 | 0.3133 |
| 0.3599 | 60.0 | 540 | 0.8846 | 0.3111 |
| 0.3599 | 61.0 | 549 | 0.9458 | 0.3233 |
| 0.3424 | 62.0 | 558 | 0.8399 | 0.3233 |
| 0.3652 | 63.0 | 567 | 0.8266 | 0.3133 |
| 0.3327 | 64.0 | 576 | 0.7813 | 0.3078 |
| 0.3603 | 65.0 | 585 | 0.8066 | 0.3156 |
| 0.3401 | 66.0 | 594 | 0.7960 | 0.3067 |
| 0.3797 | 67.0 | 603 | 0.8513 | 0.2989 |
| 0.3353 | 68.0 | 612 | 0.8319 | 0.2722 |
| 0.3909 | 69.0 | 621 | 0.8244 | 0.2878 |
| 0.3263 | 70.0 | 630 | 0.9539 | 0.3022 |
| 0.3263 | 71.0 | 639 | 1.0030 | 0.2922 |
| 0.3102 | 72.0 | 648 | 0.9875 | 0.3044 |
| 0.3577 | 73.0 | 657 | 0.9030 | 0.2978 |
| 0.2953 | 74.0 | 666 | 0.9392 | 0.2889 |
| 0.3644 | 75.0 | 675 | 0.9089 | 0.2878 |
| 0.3231 | 76.0 | 684 | 0.9264 | 0.2844 |
| 0.3078 | 77.0 | 693 | 1.0536 | 0.2911 |
| 0.4503 | 78.0 | 702 | 0.9473 | 0.2967 |
| 0.3492 | 79.0 | 711 | 0.8909 | 0.3089 |
| 0.347 | 80.0 | 720 | 0.8532 | 0.3067 |
| 0.347 | 81.0 | 729 | 0.9553 | 0.2833 |
| 0.2949 | 82.0 | 738 | 1.0111 | 0.2867 |
| 0.3447 | 83.0 | 747 | 0.9160 | 0.3011 |
| 0.2878 | 84.0 | 756 | 0.8401 | 0.2989 |
| 0.3229 | 85.0 | 765 | 0.8815 | 0.2911 |
| 0.276 | 86.0 | 774 | 0.8802 | 0.2911 |
| 0.3469 | 87.0 | 783 | 0.9121 | 0.29 |
| 0.3044 | 88.0 | 792 | 0.8934 | 0.2933 |
| 0.2885 | 89.0 | 801 | 0.8806 | 0.2967 |
| 0.3365 | 90.0 | 810 | 0.9037 | 0.2844 |
| 0.3365 | 91.0 | 819 | 0.9218 | 0.2867 |
| 0.3239 | 92.0 | 828 | 0.9228 | 0.2844 |
| 0.3219 | 93.0 | 837 | 0.9167 | 0.2844 |
| 0.2736 | 94.0 | 846 | 0.9495 | 0.2878 |
| 0.3587 | 95.0 | 855 | 0.9997 | 0.2844 |
| 0.3386 | 96.0 | 864 | 0.9977 | 0.2856 |
| 0.2895 | 97.0 | 873 | 0.9964 | 0.2889 |
| 0.3496 | 98.0 | 882 | 0.9765 | 0.2889 |
| 0.2789 | 99.0 | 891 | 0.9713 | 0.2878 |
| 0.3284 | 100.0 | 900 | 0.9687 | 0.2889 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.8.1+cu111
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
Cheatham/xlm-roberta-large-finetuned4 | [
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"text-classification",
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} | 20 | null | ---
inference: true
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
---
# Openjourney is an open source Stable Diffusion fine tuned model on Midjourney images, by [PromptHero](https://prompthero.com/)
Use prompt: 'mdjrny-v4 style'
[](https://colab.research.google.com/drive/1vkuxKKeSYNYI2OLZm8mR-WqcokQtSURM?usp=sharing)
# Gradio
We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Openjourney:
[](https://huggingface.co/spaces/akhaliq/midjourney-v4-diffusion)
### 🧨 Diffusers
### Stable Diffusion v1.5 vs Openjourney
(Same parameters, just added "mdjrny-v4 style" at the beginning):
<img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587642-63265d019f9d19bfd4f45031.png" width="100%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587623-63265d019f9d19bfd4f45031.png" width="100%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587609-63265d019f9d19bfd4f45031.png" width="100%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587646-63265d019f9d19bfd4f45031.png" width="100%"/>
[Click here](https://prompthero.com/search?model=Midjourney+Diffusion&q=road+) for more Openjourney prompts and inspiration.
### 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "prompthero/openjourney"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "retro serie of different cars with different colors and shapes, mdjrny-v4 style"
image = pipe(prompt).images[0]
image.save("./retro_cars.png")
```
|
Check/vaw2tmp | [
"tensorboard"
]
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Nalisten-Likeness-1 Dreambooth model trained by nalisten1 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)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
download
.png)
|
Chiuchiyin/Donald | []
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} | 0 | null | ---
library_name: sklearn
tags:
- sklearn
- skops
- text-classification
---
# Model description
This is a logistic regression model trained with GPT-2 embeddings on imdb dataset.
The notebook to generate this model is in this repository and in this [kaggle link](https://www.kaggle.com/code/unofficialmerve/scikit-learn-with-transformers-with-skops/notebook).
## Intended uses & limitations
This model is trained for educational purposes.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|-------------------------------|-------------------------------------------------------------------------------------------------------------------|
| memory | |
| steps | [('embedding', HFTransformersLanguage(model_name_or_path='facebook/bart-base')), ('model', LogisticRegression())] |
| verbose | False |
| embedding | HFTransformersLanguage(model_name_or_path='facebook/bart-base') |
| model | LogisticRegression() |
| embedding__model_name_or_path | facebook/bart-base |
| model__C | 1.0 |
| model__class_weight | |
| model__dual | False |
| model__fit_intercept | True |
| model__intercept_scaling | 1 |
| model__l1_ratio | |
| model__max_iter | 100 |
| model__multi_class | auto |
| model__n_jobs | |
| model__penalty | l2 |
| model__random_state | |
| model__solver | lbfgs |
| model__tol | 0.0001 |
| model__verbose | 0 |
| model__warm_start | False |
</details>
### Model Plot
The model plot is below.
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-c17251a9-68a0-4b34-a80a-a89592893866 div.sk-text-repr-fallback {display: none;}</style><div id="sk-c17251a9-68a0-4b34-a80a-a89592893866" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('embedding',HFTransformersLanguage(model_name_or_path='facebook/bart-base')),('model', LogisticRegression())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="cae9454b-9d5c-424d-bbf8-8256c92c6733" type="checkbox" ><label for="cae9454b-9d5c-424d-bbf8-8256c92c6733" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('embedding',HFTransformersLanguage(model_name_or_path='facebook/bart-base')),('model', LogisticRegression())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="0e6ffcb1-dfbc-44ef-9a7c-c15d496369c7" type="checkbox" ><label for="0e6ffcb1-dfbc-44ef-9a7c-c15d496369c7" class="sk-toggleable__label sk-toggleable__label-arrow">HFTransformersLanguage</label><div class="sk-toggleable__content"><pre>HFTransformersLanguage(model_name_or_path='facebook/bart-base')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b8eb1956-cf67-40f5-96f2-c2f0a0a41704" type="checkbox" ><label for="b8eb1956-cf67-40f5-96f2-c2f0a0a41704" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|----------|----------|
| f1_score | 0.867535 |
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
[More Information Needed]
```
</details>
```
# Additional Content
## Confusion matrix
 |
ChoboAvenger/DialoGPT-small-joshua | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: akmmsr/marian-finetuned-kde4-en-to-fr_akm
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. -->
# akmmsr/marian-finetuned-kde4-en-to-fr_akm
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6861
- Validation Loss: 0.8049
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17736, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0625 | 0.8779 | 0 |
| 0.7985 | 0.8221 | 1 |
| 0.6861 | 0.8049 | 2 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Chun/DialoGPT-large-dailydialog | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 6 | null | ---
language: pl
tags:
- text-classification
- financial-sentiment-analysis
- sentiment-analysis
datasets:
- datasets/financial_phrasebank
metrics:
- f1
- accuracy
- precision
- recall
widget:
- text: "Sprzedaż netto wzrosła o 30% do 36 mln EUR."
example_title: "Example 1"
- text: "Rusza Black Friday. Lista promocji w sklepach."
example_title: "Example 2"
- text: "Akcje CDPROJEKT zanotowały największy spadek wśród spółek notowanych na GPW."
example_title: "Example 3"
---
# Finance Sentiment PL (base)
Finance Sentiment PL (base) is a model based on [herbert-base](https://huggingface.co/allegro/herbert-base-cased) for analyzing sentiment of Polish financial news. It was trained on the translated version of [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (20014) for 10 epochs on single RTX3090 gpu.
The model will give you a three labels: positive, negative and neutral.
## How to use
You can use this model directly with a pipeline for sentiment-analysis:
```python
from transformers import pipeline
nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-pl-base")
nlp("Sprzedaż netto wzrosła o 30% do 36 mln EUR.")
```
```bash
[{'label': 'positive', 'score': 0.9999998807907104}]
```
## Performance
| Metric | Value |
| --- | ----------- |
| f1 macro | 0.969 |
| precision macro | 0.971 |
| recall macro | 0.968 |
| accuracy | 0.976 |
| samples per second | 136.8 |
(The performance was evaluated on RTX 3090 gpu)
## Changelog
- 2022-12-01: Rename the model to finance-sentiment-pl-base
- 2022-11-15: Initial release
## About bards.ai
At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/)
Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected]
|
ClaudeCOULOMBE/RickBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 9 | 2022-11-25T17:44:11Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: meningioma
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# cancer_diffusion_model_meningioma
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `meningioma` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/emendes3/cancer_diffusion_model_meningioma/tensorboard?#scalars)
|
ClydeWasTaken/DialoGPT-small-joshua | [
"conversational"
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} | 0 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- kejian/codeparrot-train-more-filter-3.3b-cleaned
model-index:
- name: kejian/final-cond-10-0.01-again
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. -->
# kejian/final-cond-10-0.01-again
This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0},
'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'],
'is_split_by_sentences': True},
'generation': {'batch_size': 64,
'metrics_configs': [{}, {'n': 1}, {}],
'scenario_configs': [{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 704,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 512,
'prefix': '<|aligned|>',
'use_prompt_for_scoring': False},
{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 272,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'functions',
'num_samples': 512,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/functions_csnet.jsonl',
'use_prompt_for_scoring': True}],
'scorer_config': {}},
'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'codeparrot/codeparrot-small'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/final-cond-10-0.01-again',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0008,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000.0,
'output_dir': 'training_output',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 5000,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/umjqnwtk |
CoachCarter/distilbert-base-uncased | []
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} | 0 | 2022-11-25T18:33:06Z |
## whisper-small-ar
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset (language=Arabic).
|
CodeDanCode/CartmenBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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"max_length": 1000
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} | 14 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- kejian/codeparrot-train-more-filter-3.3b-cleaned
model-index:
- name: kejian/final-cond-10-0.05
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. -->
# kejian/final-cond-10-0.05
This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.05,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0},
'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'],
'is_split_by_sentences': True},
'generation': {'batch_size': 64,
'metrics_configs': [{}, {'n': 1}, {}],
'scenario_configs': [{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 704,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 512,
'prefix': '<|aligned|>',
'use_prompt_for_scoring': False},
{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 272,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'functions',
'num_samples': 512,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/functions_csnet.jsonl',
'use_prompt_for_scoring': True}],
'scorer_config': {}},
'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'codeparrot/codeparrot-small'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/final-cond-10-0.05',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0008,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000.0,
'output_dir': 'training_output',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 5000,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/3pfuv3vn |
ComCom/gpt2-large | [
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"GPT2Model"
],
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} | 1 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### GSwap Dreambooth model trained by mynameisai 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)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
SD
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.png)
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.png)
|
Connor-tech/bert_cn_finetuning | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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}
}
} | 27 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-kr-jw4169
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fleurs
type: fleurs
config: ko_kr
split: train
args: ko_kr
metrics:
- name: Wer
type: wer
value: 0.519593179778642
---
<!-- 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. -->
# wav2vec2-large-xls-r-300m-kr-jw4169
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9752
- Wer: 0.5196
## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 35.084 | 1.39 | 200 | 6.8536 | 1.0 |
| 4.853 | 2.78 | 400 | 4.6246 | 1.0 |
| 4.5491 | 4.17 | 600 | 4.3815 | 1.0 |
| 2.799 | 5.55 | 800 | 1.7402 | 0.8642 |
| 1.3872 | 6.94 | 1000 | 1.2019 | 0.7448 |
| 0.9599 | 8.33 | 1200 | 1.0594 | 0.7134 |
| 0.675 | 9.72 | 1400 | 0.9321 | 0.6404 |
| 0.4775 | 11.11 | 1600 | 0.9088 | 0.5911 |
| 0.3479 | 12.5 | 1800 | 0.9430 | 0.6010 |
| 0.2712 | 13.89 | 2000 | 0.8948 | 0.5854 |
| 0.2283 | 15.28 | 2200 | 0.9009 | 0.5495 |
| 0.1825 | 16.67 | 2400 | 0.9079 | 0.5501 |
| 0.161 | 18.06 | 2600 | 0.9518 | 0.5390 |
| 0.1394 | 19.44 | 2800 | 0.9529 | 0.5399 |
| 0.1266 | 20.83 | 3000 | 0.9505 | 0.5283 |
| 0.1102 | 22.22 | 3200 | 0.9748 | 0.5328 |
| 0.101 | 23.61 | 3400 | 0.9593 | 0.5316 |
| 0.0907 | 25.0 | 3600 | 0.9832 | 0.5292 |
| 0.0833 | 26.39 | 3800 | 0.9773 | 0.5181 |
| 0.0781 | 27.78 | 4000 | 0.9736 | 0.5163 |
| 0.0744 | 29.17 | 4200 | 0.9752 | 0.5196 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
ConstellationBoi/Oop | []
| null | {
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}
} | 0 | null | ---
license: unknown
---
Pretrained LM with MLM training object based on query text data provided by the organizer
|
Contrastive-Tension/BERT-Base-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
} | 5 | 2022-11-25T21:42:19Z | ---
language: en
thumbnail: http://www.huggingtweets.com/a_0_o_1-gentlest_alive/1672200305146/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1593308812085174279/OmoJcwIC_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1598158921088344065/e-Z1Pwep_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">angelicism antarctic 露天 & 𓆏 Johnni Jiro 𓆏🕊️◻️</div>
<div style="text-align: center; font-size: 14px;">@a_0_o_1-gentlest_alive</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from angelicism antarctic 露天 & 𓆏 Johnni Jiro 𓆏🕊️◻️.
| Data | angelicism antarctic 露天 | 𓆏 Johnni Jiro 𓆏🕊️◻️ |
| --- | --- | --- |
| Tweets downloaded | 443 | 488 |
| Retweets | 23 | 26 |
| Short tweets | 32 | 45 |
| Tweets kept | 388 | 417 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/w96ghz5f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @a_0_o_1-gentlest_alive's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/24gvhogy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/24gvhogy/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/a_0_o_1-gentlest_alive')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Contrastive-Tension/RoBerta-Large-CT-STSb | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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"translation_en_to_de": {
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}
} | 5 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-classifier-feedback-1024-pseudo
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. -->
# deberta-classifier-feedback-1024-pseudo
This model is a fine-tuned version of [TTian/deberta-classifier-feedback-1024](https://huggingface.co/TTian/deberta-classifier-feedback-1024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1018
## 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: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5566 | 0.01 | 10 | 0.8514 |
| 0.3244 | 0.03 | 20 | 0.8647 |
| 0.2631 | 0.04 | 30 | 0.6700 |
| 0.2237 | 0.06 | 40 | 0.8904 |
| 0.2103 | 0.07 | 50 | 0.7951 |
| 0.2576 | 0.08 | 60 | 0.8669 |
| 0.2519 | 0.1 | 70 | 0.9586 |
| 0.2419 | 0.11 | 80 | 0.7507 |
| 0.2088 | 0.13 | 90 | 1.1316 |
| 0.218 | 0.14 | 100 | 0.7750 |
| 0.1886 | 0.15 | 110 | 0.9505 |
| 0.1979 | 0.17 | 120 | 0.9668 |
| 0.2069 | 0.18 | 130 | 0.9559 |
| 0.2633 | 0.2 | 140 | 1.1578 |
| 0.2176 | 0.21 | 150 | 0.9224 |
| 0.2026 | 0.22 | 160 | 0.9700 |
| 0.2231 | 0.24 | 170 | 1.0094 |
| 0.2396 | 0.25 | 180 | 1.1268 |
| 0.2172 | 0.27 | 190 | 0.9728 |
| 0.2105 | 0.28 | 200 | 0.9813 |
| 0.2816 | 0.29 | 210 | 0.8179 |
| 0.1927 | 0.31 | 220 | 1.0210 |
| 0.1686 | 0.32 | 230 | 1.0608 |
| 0.1662 | 0.34 | 240 | 0.9698 |
| 0.1969 | 0.35 | 250 | 0.9445 |
| 0.2037 | 0.36 | 260 | 1.0223 |
| 0.1684 | 0.38 | 270 | 0.9921 |
| 0.1934 | 0.39 | 280 | 0.9738 |
| 0.1927 | 0.41 | 290 | 0.9370 |
| 0.1978 | 0.42 | 300 | 1.0144 |
| 0.1591 | 0.43 | 310 | 0.9222 |
| 0.1748 | 0.45 | 320 | 0.9433 |
| 0.2245 | 0.46 | 330 | 0.9773 |
| 0.2297 | 0.48 | 340 | 0.9884 |
| 0.1746 | 0.49 | 350 | 1.0024 |
| 0.152 | 0.5 | 360 | 0.9463 |
| 0.1514 | 0.52 | 370 | 1.0633 |
| 0.1898 | 0.53 | 380 | 1.1181 |
| 0.1438 | 0.55 | 390 | 1.0994 |
| 0.1426 | 0.56 | 400 | 1.0228 |
| 0.1545 | 0.58 | 410 | 1.1413 |
| 0.146 | 0.59 | 420 | 1.0416 |
| 0.1295 | 0.6 | 430 | 1.0037 |
| 0.1538 | 0.62 | 440 | 1.0532 |
| 0.1584 | 0.63 | 450 | 1.1754 |
| 0.1607 | 0.65 | 460 | 1.0540 |
| 0.1518 | 0.66 | 470 | 1.0318 |
| 0.1447 | 0.67 | 480 | 1.0777 |
| 0.1432 | 0.69 | 490 | 1.0318 |
| 0.1491 | 0.7 | 500 | 1.0717 |
| 0.1134 | 0.72 | 510 | 1.0512 |
| 0.1106 | 0.73 | 520 | 1.1904 |
| 0.1521 | 0.74 | 530 | 1.0705 |
| 0.1485 | 0.76 | 540 | 1.0390 |
| 0.1431 | 0.77 | 550 | 1.1089 |
| 0.1537 | 0.79 | 560 | 1.0316 |
| 0.1472 | 0.8 | 570 | 1.1694 |
| 0.129 | 0.81 | 580 | 1.1325 |
| 0.1286 | 0.83 | 590 | 1.0471 |
| 0.1338 | 0.84 | 600 | 1.1001 |
| 0.1285 | 0.86 | 610 | 1.0770 |
| 0.1379 | 0.87 | 620 | 1.1107 |
| 0.1299 | 0.88 | 630 | 1.0579 |
| 0.1151 | 0.9 | 640 | 1.0898 |
| 0.1119 | 0.91 | 650 | 1.1335 |
| 0.1297 | 0.93 | 660 | 1.1061 |
| 0.1218 | 0.94 | 670 | 1.1080 |
| 0.1038 | 0.95 | 680 | 1.0922 |
| 0.1286 | 0.97 | 690 | 1.1035 |
| 0.1263 | 0.98 | 700 | 1.1118 |
| 0.1182 | 1.0 | 710 | 1.1018 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Corvus/DialoGPT-medium-CaptainPrice | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
],
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"task_specific_params": {
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"max_length": 1000
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"prefix": null
},
"text-generation": {
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},
"translation_en_to_de": {
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},
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}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_trainer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3708
- Accuracy: 0.9033
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5061 | 1.0 | 1125 | 0.4126 | 0.8653 |
| 0.3081 | 2.0 | 2250 | 0.3708 | 0.9033 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CouchCat/ma_mlc_v7_distil | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"multi-label",
"license:mit"
]
| text-classification | {
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"DistilBertForSequenceClassification"
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} | 29 | 2022-11-25T23:33:09Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hasoc19-xlm-roberta-base-targinsult1
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. -->
# hasoc19-xlm-roberta-base-targinsult1
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7512
- Accuracy: 0.7096
- Precision: 0.6720
- Recall: 0.6675
- F1: 0.6695
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 263 | 0.5619 | 0.6996 | 0.6660 | 0.6717 | 0.6684 |
| 0.5931 | 2.0 | 526 | 0.5350 | 0.7239 | 0.6880 | 0.6576 | 0.6655 |
| 0.5931 | 3.0 | 789 | 0.5438 | 0.7239 | 0.6872 | 0.6644 | 0.6714 |
| 0.5101 | 4.0 | 1052 | 0.5595 | 0.7196 | 0.6866 | 0.6909 | 0.6886 |
| 0.5101 | 5.0 | 1315 | 0.5580 | 0.7186 | 0.6818 | 0.6743 | 0.6774 |
| 0.4313 | 6.0 | 1578 | 0.6000 | 0.7039 | 0.6679 | 0.6692 | 0.6686 |
| 0.4313 | 7.0 | 1841 | 0.6429 | 0.7082 | 0.6765 | 0.6841 | 0.6794 |
| 0.3591 | 8.0 | 2104 | 0.6626 | 0.7115 | 0.6772 | 0.6803 | 0.6786 |
| 0.3591 | 9.0 | 2367 | 0.7231 | 0.7139 | 0.6764 | 0.6700 | 0.6727 |
| 0.3016 | 10.0 | 2630 | 0.7512 | 0.7096 | 0.6720 | 0.6675 | 0.6695 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
CouchCat/ma_ner_v7_distil | [
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
]
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} | 13 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dicquiloan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
CouchCat/ma_sa_v7_distil | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"sentiment-analysis",
"license:mit"
]
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}
} | 38 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
widget:
- text: "woolitize "
---
### Jak's Woolitize Image Pack v.1.0 for Stable Diffusion 1.5 (2.0 coming soon)
Woolitize Image Pack brought to you by 117 training images through 8000 training steps, 20% Training text crafted by Jak_TheAI_Artist
### UPDATE: Woolitize v1.2 available [here](https://huggingface.co/plasmo/woolitize-768sd1-5)
Version history:
v.1.0 - original model
v.1.2 - improved detail and backgrounds using 768x768 resolution training images
Include Prompt trigger: "woolitize" to activate.
Woolitize v1.0 (SD1.5) download file "woolitize.ckpt"
Woolitize v1.2 (SD1.5) download file "woolitize768.ckpt"
Woolitize (SD2.0) version: (not compatible with Automatic111 yet)
Sample pictures of this concept:
woolitize
|
Coverage/sakurajimamai | []
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} | 0 | null | UPDATE: V2 is released as is much better, would recommend using instead https://huggingface.co/Metal079/SonicDiffusionV2
3 Dreambooth models based on AnythingV3 for the base model and training images from Evan Stanley's twitter.
evan5400 was trained on ~30 images for 5400 steps, use keyword 'sonic person' when prompting
mobianstrimmed6000 was trained on 100 images for 6000 steps, use keyword 'mobian person' when prompting
mobianstrimmed12000 was trained on 100 images for 12000 steps, use keyword 'mobian person' when prompting
Current testing shows that mobianstrimmed6000 and evan5400 produce the best quality images, I would recommend starting with one of those two and compare results.
Apologies for using different keywords between models, I wanted to fully switch over to 'mobian person' but unfortunetaly im not fully conviced even though I used
more than double the training images, that it is better so I included both models. 6000 seemed like the sweet spot as I have noticed mobianstrimmed12000
doesnt give as consistantly good images as the other two. |
Coyotl/DialoGPT-test3-arthurmorgan | [
"conversational"
]
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
### sksHikakinotonoderugomi Dreambooth model trained by Hirokusa 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)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
sksHikakinotonoderugomi
.png)
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc_2 | []
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} | 0 | 2022-11-26T02:51:28Z | ---
license: openrail
library_name: diffusers
tags:
- TPU
- JAX
- Flax
- stable-diffusion
- text-to-image
language:
- en
--- |
Culmenus/opus-mt-de-is-finetuned-de-to-is_ekkicc | []
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} | 0 | 2022-11-26T03:30:29Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-classifier-feedback-1024-pseudo-final
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. -->
# deberta-classifier-feedback-1024-pseudo-final
This model is a fine-tuned version of [TTian/deberta-classifier-feedback-1024-pseudo](https://huggingface.co/TTian/deberta-classifier-feedback-1024-pseudo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5263
## 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: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5814 | 0.04 | 10 | 0.5888 |
| 0.5521 | 0.08 | 20 | 0.5736 |
| 0.5685 | 0.13 | 30 | 0.5809 |
| 0.6052 | 0.17 | 40 | 0.5702 |
| 0.5532 | 0.21 | 50 | 0.5571 |
| 0.6177 | 0.25 | 60 | 0.5848 |
| 0.6196 | 0.3 | 70 | 0.5464 |
| 0.5772 | 0.34 | 80 | 0.5307 |
| 0.5805 | 0.38 | 90 | 0.5550 |
| 0.6453 | 0.42 | 100 | 0.5467 |
| 0.5756 | 0.47 | 110 | 0.5587 |
| 0.5901 | 0.51 | 120 | 0.5482 |
| 0.568 | 0.55 | 130 | 0.5263 |
| 0.5452 | 0.59 | 140 | 0.5698 |
| 0.5949 | 0.64 | 150 | 0.5484 |
| 0.5537 | 0.68 | 160 | 0.5783 |
| 0.5327 | 0.72 | 170 | 0.5202 |
| 0.5449 | 0.76 | 180 | 0.5272 |
| 0.5345 | 0.81 | 190 | 0.5621 |
| 0.5837 | 0.85 | 200 | 0.5501 |
| 0.5969 | 0.89 | 210 | 0.5470 |
| 0.5905 | 0.93 | 220 | 0.5924 |
| 0.5481 | 0.97 | 230 | 0.5415 |
| 0.5035 | 1.02 | 240 | 0.5321 |
| 0.4508 | 1.06 | 250 | 0.5371 |
| 0.4227 | 1.1 | 260 | 0.5276 |
| 0.4423 | 1.14 | 270 | 0.5324 |
| 0.432 | 1.19 | 280 | 0.5378 |
| 0.4317 | 1.23 | 290 | 0.5302 |
| 0.46 | 1.27 | 300 | 0.5302 |
| 0.435 | 1.31 | 310 | 0.5326 |
| 0.3813 | 1.36 | 320 | 0.5431 |
| 0.4422 | 1.4 | 330 | 0.5323 |
| 0.4298 | 1.44 | 340 | 0.5575 |
| 0.5068 | 1.48 | 350 | 0.5529 |
| 0.4619 | 1.53 | 360 | 0.5589 |
| 0.4852 | 1.57 | 370 | 0.5256 |
| 0.3888 | 1.61 | 380 | 0.5731 |
| 0.4319 | 1.65 | 390 | 0.5335 |
| 0.4422 | 1.69 | 400 | 0.5419 |
| 0.4522 | 1.74 | 410 | 0.5547 |
| 0.4276 | 1.78 | 420 | 0.5263 |
| 0.3988 | 1.82 | 430 | 0.5481 |
| 0.4063 | 1.86 | 440 | 0.5404 |
| 0.4141 | 1.91 | 450 | 0.5292 |
| 0.4149 | 1.95 | 460 | 0.5241 |
| 0.4104 | 1.99 | 470 | 0.5263 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Czapla/Rick | []
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} | 0 | 2022-11-26T04:38:49Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
# gLWoman
This is my second Stable Diffusion custom model that bring to you a generic woman generated with non-licenced images.
The magic word is: gLWoman
If you enjoy my work, please consider supporting me:
[](https://www.buymeacoffee.com/elrivx)
Examples:
<img src=https://imgur.com/tKpiVEE.png width=30% height=30%>
<img src=https://imgur.com/GAOJzps.png width=30% height=30%>
<img src=https://imgur.com/oxI9ZQv.png width=30% height=30%>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
D3xter1922/electra-base-discriminator-finetuned-mnli | []
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} | 0 | 2022-11-26T05:01:43Z | ---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dicquiloan/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
DSI/TweetBasedSA | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 29 | 2022-11-26T07:53:32Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: final_model_output_subreddit-wallstreetbets
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_model_output_subreddit-wallstreetbets
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5351
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.7979 | 1.25 | 5000 | 3.6293 |
| 3.4998 | 2.49 | 10000 | 3.5351 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
DSI/human-directed-sentiment | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 26 | 2022-11-26T12:13:12Z | ---
tags:
- generated_from_trainer
- summarization
- book summary
metrics:
- rouge
dataset:
- kmfoda/booksum
model-index:
- name: long-t5-tglobal-large-booksum-WIP
results:
- task:
type: summarization
name: Summarization
dataset:
name: kmfoda/booksum
type: kmfoda/booksum
config: kmfoda--booksum
split: test
metrics:
- type: rouge
value: 25.6136
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2E3ZWI5NjRiZGE3YTQ2YTg5MGNmNzI5NTdjN2U3OTNiNzhmMjBhMDVkZjcwZjg0MTEyMTM3MzQyZmI1NzNjYSIsInZlcnNpb24iOjF9.REYAFwePFucxAn1Twsh9BSov9KPsCML9nTjL9oIIWa3Hp8DwJ_syPmfNsYxGe2vvNVq5rzBKF9gsJW80pbo-Aw
- type: rouge
value: 2.8652
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzk5Mjg0ZmRjYzg1NjM4MGMwOWYyOTM0ZDU2OTM2ZGJlYmM0OTVjNTI2NzcyMzU0MGI0M2I0ZmE0ZmY2NmRlNSIsInZlcnNpb24iOjF9.MzKSIqRjIV6V5YMYlvbRt2ca_CR5WFZ8DqOrUvDbiSyh7qbdU6F2LdDjB6eL-wzIR_DMF10sTtoF7H7wXs2GDw
- type: rouge
value: 12.4913
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDMzMDZhYzg2N2Q0YTZiYWUzOGI2MTRjMmRlNGIzY2I0ZDU3YzQ1MWVkZDlkOTQzNDlhNjk1MWM2OWUwNDczYSIsInZlcnNpb24iOjF9.TysgYlvfe-4GJWDSFg8KQ97Bsu-kDX3VDamS6bi9q_60V3mBzIOz0M0slySuHXu5S4MJ8a0OCPWvskP0T4ZmCQ
- type: rouge
value: 23.1102
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzY3NmI2MDJkZTQ2MzMwMDg2NWZmM2Q5NjNmZTRkMTJiODViODZmODYyNTgwMzBkYzBmZDRmMWNjYjg5NjBkYSIsInZlcnNpb24iOjF9.XNvINLow-1mfiDbm_YcAM_l4c-gEV_V5oLKzBWh7Hdmi9gHP_Z86fqQn9Kj2nhOPFWcUOFUBIzx4Z0rjs162BA
- type: loss
value: 5.004334926605225
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODNjMzI5N2IwNDExOWQzMWYxMzE4YzkxYWYxZmRkNTA2NWQ1MmYzOTFjODJhNGUzODQxYmNkODBlZDA0MGNmZCIsInZlcnNpb24iOjF9.xGNlloXeHra0K5DTKXbsrrkyuAvFXZwjzkxOyjtpw2jWs0KPw4nQ1MKkJiX6juXtleJrvS2u1FQcwCbygUmLDQ
- type: gen_len
value: 89.4354
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjBlODBiMmEwN2UzYzE5NTE3ODBkNDVmMTgxMzhlYmVmZjgxMzJjYTBlYjBhMDgzNzhhMWQ0Mzc2MjdjN2E0ZiIsInZlcnNpb24iOjF9.Z9kytQDiNK-TCaHz-0YZeH8FCrW5D0SA-ji7Q86wqdhBC9jTDmJGnBll6mGFcHipERrRKZb12hYStKJanb3iBA
---
# tglobal-large-booksum-WIP
> this is a WIP checkpoint that has been fine-tuned from the vanilla (original) for 10ish epochs. It is **not ready to be used for inference**
This model is a fine-tuned version of [google/long-t5-tglobal-large](https://huggingface.co/google/long-t5-tglobal-large) on the `kmfoda/booksum` dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9519
- Rouge1: 21.8058
- Rouge2: 2.9343
- Rougel: 10.3717
- Rougelsum: 20.1537
- Gen Len: 106.055
## Model description
Testing fine-tuning only on booksum with 16384/1024 the whole time (vs. previous large WIP checkpoint I made that started from a partially-trained `pubmed` checkpoint)
## Intended uses & limitations
this is a WIP checkpoint that has been fine-tuned from the vanilla (original) for 10ish epochs. It is **not ready to be used for inference**
## Training and evaluation data
This is **only** fine-tuned on booksum (vs. previous large WIP checkpoint I made that started from a partially-trained `pubmed` checkpoint)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 1
- eval_batch_size: 1
- seed: 31060
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:-------:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 5.0389 | 0.99 | 37 | 219.03 | 5.1884 | 29.995 | 4.4045 | 12.8837 | 27.557 |
| 4.8986 | 1.0 | 75 | 5.1286 | 26.921 | 3.7193 | 11.3605| 25.3492 | 276.005 |
| 4.5928 | 2.0 | 150 | 4.9900 | 26.6667 | 3.7342 | 11.8223| 24.7087 | 178.775 |
| 4.6159 | 3.0 | 225 | 4.9519 | 21.8058 | 2.9343 | 10.3717| 20.1537 | 106.055 |
#### eval in bf16
```
***** eval metrics *****
epoch = 3.0
eval_gen_len = 103.075
eval_loss = 4.9501
eval_rouge1 = 21.6345
eval_rouge2 = 2.877
eval_rougeL = 10.386
eval_rougeLsum = 20.0148
eval_runtime = 0:06:02.75
eval_samples = 200
eval_samples_per_second = 0.551
eval_steps_per_second = 0.138
[INFO|trainer.py:2724] 2022-11-27 01:00:
```
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
|
alexandrainst/da-hatespeech-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
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} | 866 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: outputs
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. -->
# outputs
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0926
- Accuracy: 0.8780
- F1: 0.3881
- Precision: 0.5417
- Recall: 0.3023
## 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: 8e-05
- train_batch_size: 256
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 6 | 0.0874 | 0.8810 | 0.4118 | 0.56 | 0.3256 |
| No log | 2.0 | 12 | 0.0936 | 0.8839 | 0.4000 | 0.5909 | 0.3023 |
| No log | 3.0 | 18 | 0.0922 | 0.8780 | 0.3881 | 0.5417 | 0.3023 |
| No log | 4.0 | 24 | 0.0926 | 0.8780 | 0.3881 | 0.5417 | 0.3023 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Daivakai/DialoGPT-small-saitama | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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"GPT2LMHeadModel"
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} | 9 | 2022-11-26T10:22:43Z | ---
license: creativeml-openrail-m
---
DreamBooth fine-tuned Stable Diffusion v1.5 model.
## Style
Use token **_clone wars style_** for the overall style.
<details>
<summary>Style samples and prompts. Click to expand.</summary>
`a supercar, clone wars style`
Steps: 20, Sampler: Euler, CFG scale: 7, Seed: 3415266579, Size: 768x512
---
`Portrait of david bowie in clone wars style`
Steps: 30, Sampler: Euler a, CFG scale: 5, Seed: 3362890746, Size: 512x512
---
`ironman, clone wars style`
Steps: 30, Sampler: Euler, CFG scale: 5, Seed: 282860809, Size: 512x512
---
`A futuristic city, clone wars style`
Steps: 30, Sampler: Euler, CFG scale: 6, Seed: 1803498434, Size: 512x512
---
`a high speed train, clone wars style`
Steps: 30, Sampler: Euler, CFG scale: 6, Seed: 166188094, Size: 768x512
---
</details>
## Characters
The model was trained to recognize the following characters. Use character name as prompt token. You don't need to add style token if character token is used.
Click to expand and view prompt samples:
<details>
<summary>Ahsoka Tano</summary>
`ahsoka tano in glasses wearing dress sitting on a chair`
Steps: 30, Sampler: Euler, CFG scale: 5, Seed: 4066073909, Size: 512x512
---
`a realistic photo a woman as [ahsoka tano : 4]`
Negative prompt: `clone wars style, cartoon, 3d render, anime, 3d animation, 3d game`
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 1721324089, Size: 512x512
---
</details>
<details>
<summary>Anakin Skywalker</summary>
`portrait of smiling anakin skywalker in a cowboy hat with a beard`
Steps: 30, Sampler: Euler a, CFG scale: 3, Seed: 4147078052, Size: 512x512
---
`portrait of [[anakin skywalker]], (beautiful curly hair), high detail, illustration by greg rutkowski`
Steps: 30, Sampler: Euler, CFG scale: 3, Seed: 357046052, Size: 512x512
---
</details>
<details>
<summary>Bo-Katan</summary>
`[bo-katan : 4] laughing (in a top hat)`
Steps: 30, Sampler: Euler, CFG scale: 7, Seed: 1530576893, Size: 512x512
---
`[bo-katan : 9], smiling with pink hair and and in a blue dress`
Steps: 30, Sampler: Euler, CFG scale: 5, Seed: 3605723330, Size: 512x512
---
</details>
<details>
<summary>Captain Rex</summary>
`close up portrait of captain rex as batman!`
Steps: 20, Sampler: Euler, CFG scale: 4, Seed: 3055584516, Size: 512x512
---
`[captain rex] in a black tuxedo! as agent 47`
Steps: 30, Sampler: Euler a, CFG scale: 3.0, Seed: 2666206249, Size: 512x512
---
</details>
<details>
<summary>Count Dooku</summary>
`[count dooku] as american civil war general in a blue uniform, clone wars style`
Steps: 30, Sampler: Euler, CFG scale: 3.0, Seed: 1985172001, Size: 512x512
---
`painting, drawing, anime masterpiece by Studio Ghibli, mountains landscape on the background, deep bokeh, close-up photo of [count dooku]`
Negative prompt: `clone wars style, 3d render, 3d game`
Steps: 20, Sampler: Euler, CFG scale: 5, Seed: 2127464199, Size: 512x512
---
</details>
<details>
<summary>Darth Maul</summary>
`most wanted poster, reward poster of [darth maul] (in a cowboy hat and beard )`
Steps: 30, Sampler: Euler, CFG scale: 5, Seed: 2076969083, Size: 512x512
---
`buffed bodybuilder darth maul`
Steps: 30, Sampler: Euler a, CFG scale: 3, Seed: 2584885283, Size: 512x512
---
</details>
<details>
<summary>Mace Windu</summary>
`mace windu in a cowboy hat`
Steps: 30, Sampler: Euler a, CFG scale: 3, Seed: 3316198593, Size: 512x512
---
`military dictator in (aviator sunglasses) by [mace windu]`
Steps: 30, Sampler: Euler, CFG scale: 5, Seed: 3739984072, Size: 512x512
---
</details>
<details>
<summary>Obi-Wan Kenobi</summary>
`epic portrait of obi-wan kenobi on a horse in a cowboy hat!`
Steps: 30, Sampler: Euler, CFG scale: 4, Seed: 3422542133, Size: 512x512
---
`portrait of a (pirate captain : 100) [obi-wan kenobi] with hat`
Steps: 25, Sampler: Euler, CFG scale: 3, Seed: 2834183111, Size: 512x512
---
</details>
<details>
<summary>Padme Amidala</summary>
`anime masterpiece by Studio Ghibli, in a (top hat) and victorian dress, (padme amidala)`
Steps: 30, Sampler: Euler, CFG scale: 4, Seed: 2110649472, Size: 512x512
---
`padme amidala dollfie in black gothic dress`
Steps: 30, Sampler: Euler a, CFG scale: 3, Seed: 2062874506, Size: 512x512
---
</details>
<details>
<summary>Palpatine</summary>
`preacher in black outfit and a hat palpatine`
Steps: 20, Sampler: Euler, CFG scale: 4, Seed: 203950006, Size: 512x512
---
`kawaii palpatine with (big anime eyes and cat ears)`
Steps: 20, Sampler: Euler, CFG scale: 3, Seed: 2283800789, Size: 512x512
---
</details>
<details>
<summary>Yoda</summary>
`(in a cowboy hat), yoda`
Steps: 30, Sampler: Euler, CFG scale: 4, Seed: 362164702, Size: 512x512
---
`overweight and obese yoda`
Steps: 30, Sampler: Euler, CFG scale: 4, Seed: 2980052051, Size: 512x512
---
</details>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
Danih1502/t5-base-finetuned-en-to-de | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [rinna/japanese-gpt2-small](https://huggingface.co/rinna/japanese-gpt2-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4525
- Accuracy: 0.4155
## 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: 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: 10.0
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Danih1502/t5-small-finetuned-en-to-de | []
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} | 0 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: bart-base-finetuned-cnn_dailymail
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: train
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 0.35105989316705805
---
<!-- 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. -->
# bart-base-finetuned-cnn_dailymail
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5396
- Rouge1: 0.3511
- Rouge2: 0.1925
- Rougel: 0.3086
- Rougelsum: 0.3292
## 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: 5.6e-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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.9486 | 1.0 | 35890 | 1.5941 | 0.3498 | 0.1893 | 0.3063 | 0.3272 |
| 1.6706 | 2.0 | 71780 | 1.5601 | 0.3503 | 0.1916 | 0.3079 | 0.3279 |
| 1.4809 | 3.0 | 107670 | 1.5423 | 0.3520 | 0.1923 | 0.3086 | 0.3295 |
| 1.3293 | 4.0 | 143560 | 1.5396 | 0.3511 | 0.1925 | 0.3086 | 0.3292 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
DarkKibble/DialoGPT-medium-Tankman | []
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} | 0 | null | ---
tags:
- text
- stance
- text-classification
pipeline_tag: text-classification
language:
- en
widget:
- text: user Bolsonaro is the president of Brazil. He speaks for all brazilians. Greta is a climate activist. Their opinions do create a balance that the world needs now
example_title: example 1
- text: user The fact is that she still doesn’t change her ways and still stays non environmental friendly
example_title: example 2
- text: user The criteria for these awards dont seem to be very high.
example_title: example 3
model-index:
- name: Stance-Tw
results:
- task:
type: stance-classification # Required. Example: automatic-speech-recognition
name: Text Classification # Optional. Example: Speech Recognition
dataset:
type: stance # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: stance # Required. A pretty name for the dataset. Example: Common Voice (French)
metrics:
- type: f1
value: 75.8
- type: accuracy
value: 76.2
---
<!-- 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. -->
# Stance-Tw
This model is a fine-tuned version of [j-hartmann/sentiment-roberta-large-english-3-classes](https://huggingface.co/j-hartmann/sentiment-roberta-large-english-3-classes) to predict 3 categories of author stance (attack, support, neutral) towards an entity mentioned in the text.
- training procedure available in [Colab notebook](https://colab.research.google.com/drive/12DsO5dNaQI3kFO7ohOHZn4EWNewFy2jm?usp=sharing)
- result of a collaboration with [Laboratory of The New Ethos](https://newethos.org/laboratory/)
```
# Model usage
from transformers import pipeline
model_path = "eevvgg/Stance-Tw"
cls_task = pipeline(task = "text-classification", model = model_path, tokenizer = model_path)#, device=0
sequence = ['his rambling has no clear ideas behind it',
'That has nothing to do with medical care',
"Turns around and shows how qualified she is because of her political career.",
'She has very little to gain by speaking too much']
result = cls_task(sequence)
labels = [i['label'] for i in result]
labels # ['attack', 'neutral', 'support', 'attack']
```
## Intended uses & limitations
Model suited for classification of stance in short text. Fine-tuned on a manually-annotated corpus of size 3.2k.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 4e-5, 'decay': 0.01}
Trained for 3 epochs, mini-batch size of 8.
- loss: 0.719
## Evaluation data
It achieves the following results on the evaluation set:
- macro f1-score: 0.758
- weighted f1-score: 0.762
- accuracy: 0.762
## Citation
**BibTeX**: tba
|
Darkecho789/email-gen | []
| null | {
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} | 0 | null | ---
language:
- zh
tags:
- SongNet
- pytorch
- zh
- Text2Text-Generation
license: "apache-2.0"
widget:
- text: "丹枫江冷人初去"
---
# SongNet pretrain (songnet-base-chinese) Model
SongNet中文预训练模型
SongNet的网络结构：
## Usage
本项目开源在文本生成项目：[textgen](https://github.com/shibing624/textgen)，可支持SongNet模型。
模型文件组成：
```
songnet-base-chinese
├── pytorch_model.bin
└── vocab.txt
```
### 相关内容
- [SongNet paper](https://aclanthology.org/2020.acl-main.68/)
- [textgen](https://github.com/shibing624/textgen)
如果需要训练SongNet模型，请参考[https://github.com/shibing624/textgen/blob/main/examples/language_generation/training_zh_songnet_demo.py](https://github.com/shibing624/textgen/blob/main/examples/language_generation/training_zh_songnet_demo.py)
## Citation
```latex
@software{textgen,
author = {Xu Ming},
title = {textgen: Implementation of Text Generation models},
year = {2022},
url = {https://github.com/shibing624/textgen},
}
```
|
DarkestSky/distilbert-base-uncased-finetuned-ner | []
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}
} | 0 | null | ---
language:
- zh
tags:
- SongNet
- pytorch
- zh
- Text2Text-Generation
license: "apache-2.0"
widget:
- text: "严蕊<s1>如梦令<s2>道是梨花不是。</s>道是杏花不是。</s>白白与红红，别是东风情味。</s>曾记。</s>曾记。</s>人在武陵微醉。"
---
# SongNet for Chinese songci(songnet-base-chinese-songci) Model
SongNet中文宋词仿写模型
`songnet-base-chinese-songci` evaluate couplet test data：
The overall performance of SongNet on songci **test**:
|input_text|predict|
|:--- |:--- |
|道是梨花不是。</s>道是杏花不是。</s>白白与红红，别是东风情味。</s>曾记。</s>曾记。</s>人在武陵微醉。|<bos>风撼梧桐影乱。</s>雨洒梧桐影乱。</s>又是一番红，人与暮霞俱远。</s>凄断。</s>凄断。</s>人与暮霞俱远。</s>|
在宋词测试集上生成结果满足字数相同、词性对齐、词面对齐、形似要求，针对性的SongNet网络结构，在语义对仗工整和平仄合律上的效果明显优于T5和GPT2等模型。
SongNet的网络结构：
## Usage
本项目开源在文本生成项目：[textgen](https://github.com/shibing624/textgen)，可支持SongNet模型，通过如下命令调用：
Install package:
```shell
pip install -U textgen
```
```python
from textgen.language_modeling import SongNetModel
model = SongNetModel(model_type='songnet', model_name='shibing624/songnet-base-chinese-songci')
sentences = [
"严蕊<s1>如梦令<s2>道是梨花不是。</s>道是杏花不是。</s>白白与红红，别是东风情味。</s>曾记。</s>曾记。</s>人在武陵微醉。",
"张抡<s1>春光好<s2>烟澹澹，雨。</s>水溶溶。</s>帖水落花飞不起，小桥东。</s>翩翩怨蝶愁蜂。</s>绕芳丛。</s>恋馀红。</s>不恨无情桥下水，恨东风。"
]
print("inputs:", sentences)
print("outputs:", model.generate(sentences))
sentences = [
"秦湛<s1>卜算子<s2>_____，____到。_______，____俏。_____，____报。_______，____笑。",
"秦湛<s1>卜算子<s2>_雨___，____到。______冰，____俏。____春，__春_报。__山花___，____笑。"
]
print("inputs:", sentences)
print("outputs:", model.fill_mask(sentences))
```
output:
```shell
inputs: ['严蕊<s1>如梦令<s2>道是梨花不是。</s>道是杏花不是。</s>白白与红红，别是东风情味。</s>曾记。</s>曾记。</s>人在武陵微醉。', '张抡<s1>春光好<s2>烟澹澹，雨。</s>水溶溶。</s>帖水落花飞不起，小桥东。</s>翩翩怨蝶愁蜂。</s>绕芳丛。</s>恋馀红。</s>不恨无情桥下水，恨东风。']
outputs: ['<bos>风撼梧桐影乱。</s>雨洒梧桐影乱。</s>又是一番红，人与暮霞俱远。</s>凄断。</s>凄断。</s>人与暮霞俱远。</s>', '<bos>光阴速，还。</s>转飞残。</s>日向旧时檐下见，两三竿。</s>多少社寒垂涎。</s>玉人间。</s>恶循环。</s>不见旧时檐下见，两三竿。</s>']
inputs: ['秦湛<s1>卜算子<s2>_____，____到。_______，____俏。_____，____报。_______，____笑。', '秦湛<s1>卜算子<s2>_雨___，____到。______冰，____俏。____春，__春_报。__山花___，____笑。']
outputs: ['<bos>新月破寒影，正柳暗清到。千缕万绪浓於雨，多少匆匆俏。梦魂又不得，那堪断得报。听著窗前柳弄歌，寂寞梨花笑。</s>', '<bos>风雨送春归，草软莺簧到。门对宝篆淡淡冰，翠点吴绫俏。小立东风春，不怕春归报。多少山花妒落红，背面一饷笑。</s>']
```
模型文件组成：
```
songnet-base-chinese-songci
├── pytorch_model.bin
└── vocab.txt
```
### 训练数据集
#### 中文宋词数据集
- 数据：[songci](https://github.com/lipiji/SongNet/blob/master/data/ci.txt)
- 相关内容
- [Huggingface](https://huggingface.co/)
- [SongNet paper](https://aclanthology.org/2020.acl-main.68/)
- [textgen](https://github.com/shibing624/textgen)
数据格式：
```text
head -n 2 ci.txt
赵必<s1>水调歌头<s2>百岁人能几，七十世间稀。</s>何况先生八十，蔗境美如饴。</s>好与七松处士，更与梅花君子，永结岁寒知。</s>菊节先五日，满酌紫霞卮。</s>美成词，山谷字，老坡诗。</s>三径田园如昨，久矣赋归辞。</s>不是商山四皓，便是香山九老，红颊白须眉。</s>九十尚入相，绿竹颂猗猗。
李曾伯<s1>水调歌头<s2>千一载英杰，百二国山河。</s>提封几半宇宙，万里仗天戈。</s>十乘晋军旗鼓，三岁秦关扃锁，地利属人和。</s>位次功第一，未数侯何。</s>建青油，持柴荷，听黄麻。</s>乾坤整顿都了，玉殿侍羲娥。</s>且醉东湖花柳，却泛西湖舟楫，留不住岷峨。</s>谁为语儒馆，浓墨被诗歌。
```
如果需要训练SongNet模型，请参考[https://github.com/shibing624/textgen/blob/main/examples/language_generation/training_zh_songnet_demo.py](https://github.com/shibing624/textgen/blob/main/examples/language_generation/training_zh_songnet_demo.py)
## Citation
```latex
@software{textgen,
author = {Xu Ming},
title = {textgen: Implementation of Text Generation models},
year = {2022},
url = {https://github.com/shibing624/textgen},
}
``` |
DarshanDeshpande/marathi-distilbert | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"mr",
"dataset:Oscar Corpus, News, Stories",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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}
}
} | 14 | null | Access to model Nicolas74/jim is restricted and you are not in the authorized list. Visit https://huggingface.co/Nicolas74/jim to ask for access. |
DataikuNLP/paraphrase-albert-small-v2 | [
"pytorch",
"albert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
| sentence-similarity | {
"architectures": [
"AlbertModel"
],
"model_type": "albert",
"task_specific_params": {
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},
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},
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},
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},
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}
}
} | 628 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- kejian/codeparrot-train-more-filter-3.3b-cleaned
model-index:
- name: kejian/final-cond-10-0.1-again-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. -->
# kejian/final-cond-10-0.1-again-2
This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.1,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0},
'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'],
'is_split_by_sentences': True},
'generation': {'batch_size': 64,
'metrics_configs': [{}, {'n': 1}, {}],
'scenario_configs': [{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 704,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 512,
'prefix': '<|aligned|>',
'use_prompt_for_scoring': False},
{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 272,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'functions',
'num_samples': 512,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/functions_csnet.jsonl',
'use_prompt_for_scoring': True}],
'scorer_config': {}},
'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'codeparrot/codeparrot-small'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/final-cond-10-0.1-again-2',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0008,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000.0,
'output_dir': 'training_output',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 5000,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/kjz9xgv1 |
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
| sentence-similarity | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
} | 1,517 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- kejian/codeparrot-train-more-filter-3.3b-cleaned
model-index:
- name: kejian/final-cond-10-0.25-again
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. -->
# kejian/final-cond-10-0.25-again
This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.25,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0},
'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'],
'is_split_by_sentences': True},
'generation': {'batch_size': 64,
'metrics_configs': [{}, {'n': 1}, {}],
'scenario_configs': [{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 704,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 512,
'prefix': '<|aligned|>',
'use_prompt_for_scoring': False},
{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 272,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'functions',
'num_samples': 512,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/functions_csnet.jsonl',
'use_prompt_for_scoring': True}],
'scorer_config': {}},
'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'codeparrot/codeparrot-small'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/final-cond-10-0.25-again',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0008,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000.0,
'output_dir': 'training_output',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 5000,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/evioady7 |
Davlan/bert-base-multilingual-cased-finetuned-yoruba | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 21 | 2022-11-26T13:27:34Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.6438492063492064
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3716577540106952
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.37388724035608306
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4324624791550862
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.72
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.35526315789473684
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3773148148148148
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.896790718698207
- name: F1 (macro)
type: f1_macro
value: 0.8911002385898117
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.795774647887324
- name: F1 (macro)
type: f1_macro
value: 0.5441132147176833
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6235102925243771
- name: F1 (macro)
type: f1_macro
value: 0.6166467084580077
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9611184530847882
- name: F1 (macro)
type: f1_macro
value: 0.8888486397674304
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8617988091507365
- name: F1 (macro)
type: f1_macro
value: 0.8588467057047432
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.3716577540106952
- Accuracy on SAT: 0.37388724035608306
- Accuracy on BATS: 0.4324624791550862
- Accuracy on U2: 0.35526315789473684
- Accuracy on U4: 0.3773148148148148
- Accuracy on Google: 0.72
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.896790718698207
- Micro F1 score on CogALexV: 0.795774647887324
- Micro F1 score on EVALution: 0.6235102925243771
- Micro F1 score on K&H+N: 0.9611184530847882
- Micro F1 score on ROOT09: 0.8617988091507365
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.6438492063492064
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: info_loob
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 6
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
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