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CAMeL-Lab/bert-base-arabic-camelbert-msa-half | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 16 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.6566666666666666
- name: F1
type: f1
value: 0.6979472140762463
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7339
- Accuracy: 0.6567
- F1: 0.6979
## 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
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
CBreit00/DialoGPT_small_Rick | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-ta
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.ta
metrics:
- name: F1
type: f1
value: 0.8144578313253013
---
<!-- 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-ta
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: 0.2183
- F1: 0.8145
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5477 | 1.0 | 209 | 0.2732 | 0.7305 |
| 0.2506 | 2.0 | 418 | 0.2425 | 0.7626 |
| 0.168 | 3.0 | 627 | 0.2183 | 0.8145 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
CL/safe-math-bot | []
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} | 0 | null | ---
license: mit
---
### face2contra-sd-dreambooth on Stable Diffusion via Dreambooth
#### model by avantcontra
This your the Stable Diffusion model fine-tuned the face2contra-sd-dreambooth concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a photo of sks face2contra**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
CLTL/icf-levels-ber | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
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} | 33 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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/2348558617/x0vh6bui3sq97vt4jd2n_400x400.png')">
</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/1567266375026053125/0cyfXyiF_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">Дмитрий Медведев & MORGENSHTERN</div>
<div style="text-align: center; font-size: 14px;">@medvedevrussia-morgen__shtern</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 Дмитрий Медведев & MORGENSHTERN.
| Data | Дмитрий Медведев | MORGENSHTERN |
| --- | --- | --- |
| Tweets downloaded | 1745 | 3178 |
| Retweets | 298 | 57 |
| Short tweets | 50 | 1034 |
| Tweets kept | 1397 | 2087 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wx8v66j/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 @medvedevrussia-morgen__shtern's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/qwb0vpv7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/qwb0vpv7/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/medvedevrussia-morgen__shtern')
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)
|
CLTL/icf-levels-ins | [
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} | 32 | null | Update: https://huggingface.co/Deltaadams/HentaiDiffusion |
Callidior/bert2bert-base-arxiv-titlegen | [
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:arxiv_dataset",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
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} | 145 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 250.25 +/- 16.66
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Cameron/BERT-SBIC-offensive | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9255
- name: F1
type: f1
value: 0.925520268497019
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2170
- Accuracy: 0.9255
- F1: 0.9255
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8237 | 1.0 | 250 | 0.3205 | 0.9045 | 0.9002 |
| 0.2539 | 2.0 | 500 | 0.2170 | 0.9255 | 0.9255 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Cameron/BERT-eec-emotion | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 36 | null | Access to model abrizk/autotrain-bart-meeting-summarization-1648858537 is restricted and you are not in the authorized list. Visit https://huggingface.co/abrizk/autotrain-bart-meeting-summarization-1648858537 to ask for access. |
Cameron/BERT-jigsaw-severetoxic | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 30 | 2022-10-03T21:55:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
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-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.7.1+cu110
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Cameron/BERT-mdgender-convai-ternary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 38 | null | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- suresh-subramanian/autotrain-data-fake-news
co2_eq_emissions:
emissions: 0.04097854185629584
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1649058538
- CO2 Emissions (in grams): 0.0410
## Validation Metrics
- Loss: 0.387
- Accuracy: 0.815
- Precision: 0.760
- Recall: 0.730
- AUC: 0.902
- F1: 0.745
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/suresh-subramanian/autotrain-fake-news-1649058538
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058538", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058538", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Cameron/BERT-mdgender-wizard | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"translation_en_to_fr": {
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},
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}
}
} | 30 | 2022-10-03T22:07:19Z | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- suresh-subramanian/autotrain-data-fake-news
co2_eq_emissions:
emissions: 0.040297872306469855
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1649058539
- CO2 Emissions (in grams): 0.0403
## Validation Metrics
- Loss: 0.478
- Accuracy: 0.779
- Precision: 0.814
- Recall: 0.520
- AUC: 0.881
- F1: 0.635
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/suresh-subramanian/autotrain-fake-news-1649058539
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058539", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058539", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Cameron/BERT-rtgender-opgender-annotations | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
} | 33 | 2022-10-03T22:07:48Z | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- suresh-subramanian/autotrain-data-fake-news
co2_eq_emissions:
emissions: 4.630852478388675
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1649058540
- CO2 Emissions (in grams): 4.6309
## Validation Metrics
- Loss: 0.527
- Accuracy: 0.725
- Precision: 0.729
- Recall: 0.408
- AUC: 0.825
- F1: 0.523
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/suresh-subramanian/autotrain-fake-news-1649058540
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058540", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058540", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Camzure/MaamiBot-test | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 9 | null | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- suresh-subramanian/autotrain-data-fake-news
co2_eq_emissions:
emissions: 4.695596043893512
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1649058541
- CO2 Emissions (in grams): 4.6956
## Validation Metrics
- Loss: 0.459
- Accuracy: 0.779
- Precision: 0.790
- Recall: 0.546
- AUC: 0.881
- F1: 0.646
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/suresh-subramanian/autotrain-fake-news-1649058541
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058541", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058541", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Camzure/MaamiBot | []
| null | {
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}
} | 0 | 2022-10-03T22:08:00Z | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- suresh-subramanian/autotrain-data-fake-news
co2_eq_emissions:
emissions: 12.699762619910537
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1649058542
- CO2 Emissions (in grams): 12.6998
## Validation Metrics
- Loss: 0.624
- Accuracy: 0.637
- Precision: 1.000
- Recall: 0.020
- AUC: 0.652
- F1: 0.039
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/suresh-subramanian/autotrain-fake-news-1649058542
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058542", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058542", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Canadiancaleb/DialoGPT-small-walter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
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},
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}
}
} | 13 | 2022-10-03T22:13:31Z | ---
license: mit
---
This model is part of our work "Visual Story Generation Based on Emotional and Keyword Scheme."
More information will be provided later |
CapitainData/wav2vec2-large-xlsr-turkish-demo-colab | []
| null | {
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}
} | 0 | null | Access to model AJRVIDEO/Elephant is restricted and you are not in the authorized list. Visit https://huggingface.co/AJRVIDEO/Elephant to ask for access. |
Capreolus/bert-base-msmarco | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"arxiv:2008.09093",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 238 | null | ---
license: mit
---
### MattVidPro on Stable Diffusion
This is the `<mattvidpro>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
Capreolus/birch-bert-large-mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
| null | {
"architectures": [
"BertForNextSentencePrediction"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 1 | null | Access to model AJRVIDEO/Elephantman is restricted and you are not in the authorized list. Visit https://huggingface.co/AJRVIDEO/Elephantman to ask for access. |
Capreolus/birch-bert-large-msmarco_mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
| null | {
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"BertForNextSentencePrediction"
],
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}
} | 1 | 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-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7993
- Accuracy: 0.704
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 63 | 0.8245 | 0.696 |
| No log | 2.0 | 126 | 0.7993 | 0.704 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Carlork314/Carlos | []
| null | {
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},
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}
} | 0 | 2022-10-03T23:32:40Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3608
- Accuracy: 0.8433
- F1: 0.8433
## 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: 128
- eval_batch_size: 128
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4095 | 1.0 | 875 | 0.3667 | 0.8353 | 0.8351 |
| 0.3348 | 2.0 | 1750 | 0.3608 | 0.8433 | 0.8433 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Carlork314/Xd | []
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} | 0 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: sourBlueBarneyTwo
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9800000190734863
---
# sourBlueBarneyTwo
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### blue_dream
#### poodle
#### sour_diesel
#### swan
 |
CarlosPR/mt5-spanish-memmories-analysis | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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},
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} | 7 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1218.38 +/- 203.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
## parameters
```python
model = A2C(policy = "MlpPolicy",
env = env,
gae_lambda = 0.9,
gamma = 0.99,
learning_rate = 0.00096,
max_grad_norm = 0.5,
n_steps = 8,
vf_coef = 0.4,
ent_coef = 0.0,
tensorboard_log = "./tensorboard",
policy_kwargs=dict(
log_std_init=-2, ortho_init=False),
normalize_advantage=False,
use_rms_prop= True,
use_sde= True,
verbose=1)
...
```
|
Carolhuehuehuehue/Sla | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-review
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-review
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5668
- Accuracy: 0.7853
## 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5019 | 1.0 | 5017 | 0.5607 | 0.7797 |
| 0.4334 | 2.0 | 10034 | 0.5668 | 0.7853 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Cat/Kitty | []
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} | 0 | null | # Grapheme-based statistical parametric synthesizer for Kinyarwanda
A Grapheme-based approach was chosen because they give acceptable performances for low-resource languages. For instance, this model was trained on approximately 5 hours of Kinyarwanda audios with their corresponding transcriptions, no further language-specific information was provided.
The [Festvox](http://festvox.org/) suite of tools was employed to build the model, and the Flite engine was used to generate a small, and portable executable file for this model. Currently, this model can only be run on Linux.
## Model description
To build the voice, we needed to map graphemes to their corresponding phonemes. In this work the UniTran-based approach to building the voice. The graphemes are converted to UTF-8 code points, then these are converted to guessed phonetic transcription in X-Sampa. After obtaining the phonemes, on each one of them we use an HMM model from the Clustergen framework to obtain important features. These features are then used to train RandomForest(20 decision trees) to predict spectral features. It achieves an `MCD` of ` 5.03 `.
## Limitations and Recommendations
The voice produced lacks in crispness and in some cases ignore tonal information which is indispensable in Kinyarwanda. We believe that with a large corpus of linguistic information the voice would sound more natural.
## Usage
Use the following to convert text to a wav file:
``` sh
./flite_du_kin_tts -f kinyarwanda.txt kinyarwanda.wav
```
And to use a terminal prompt, use:
``` sh
./flite_du_kin_tts -t "Muraho Rwanda" kinyarwanda.wav
```
|
Cathy/reranking_model | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
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}
} | 27 | null | ---
license: mit
---
### Filippo Palizzi Artworks on Stable Diffusion via Dreambooth
#### model by Capacap
This your the Stable Diffusion model fine-tuned the Filippo Palizzi Artworks concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a painting by sks Filippo Palizzi**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
This is a Stable Diffusion concept trained via Dreambooth on a small set of artworks by Italian painter Filippo Palizzi (1818 – 1899).
Example prompt: "A cozy cottage by sks Filippo Palizzi".
Here are the images used for training this concept:
|
dccuchile/albert-base-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 14 | null | Access to model Mirimur/Wav2Vec2_Texas_ASR is restricted and you are not in the authorized list. Visit https://huggingface.co/Mirimur/Wav2Vec2_Texas_ASR to ask for access. |
dccuchile/albert-base-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
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"AlbertForSequenceClassification"
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}
} | 25 | null | Access to model LYTinn/finetuning-sentiment-model-3000-samples is restricted and you are not in the authorized list. Visit https://huggingface.co/LYTinn/finetuning-sentiment-model-3000-samples to ask for access. |
dccuchile/albert-base-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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"AlbertForTokenClassification"
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} | 5 | null | ---
datasets:
- bigscience/P3
language: en
license: apache-2.0
widget:
- text : "input: <extra_id_0> The item was packaged in bubble wrap. <extra_id_1>\n
- It was fragile.\n
- It was small.\n
output: It was fragile."
---
**Official repository**: [seonghyeonye/Flipped-Learning](https://github.com/seonghyeonye/Flipped-Learning)
# Model Description
FLIPPED uses a unique meta-learning method to show zero-shot task generalization on classification natural language prompts, outperforming GPT-3 and T0-11B on many tasks with a 4x smaller scale.
It is a series of encoder-decoder model trained on a numerous classification dataset. We show inputs and its corresponding outputs of each instances in each dataset to FLIPPED, and train it to generate its possible instruction. We add unlikelihood loss in order **not** to generate the instruction when given the same input, but a wrong output. To obtain FLIPPED, we fine-tune a T5 model in a given scale on a multitask mixture covering many different classification NLP tasks.
# Intended uses
You can use the models to perform inference on tasks by specifying your input-output NLP query in a "input: {input}\noutput: {output}" form , and the model will predict the instruction. For example, You can try
*"input: <extra_id_0> this is the best cast iron skillet you will ever buy<extra_id_1>\noutput: Positive"*
as an input, and the model will hopefully generate *"Title: Review:"*.
# How to use
Our overall explanation models along with ablations can be found in our [paper](https://arxiv.org/abs/2210.02969). We recommend using the [FLIPPED-11B](seonghyeonye/flipped_11B) checkpoint as it leads (on average) to the best performances on a variety of NLP tasks.
|Model|Number of parameters|
|-|-|
|[Flipped_11B](https://huggingface.co/seonghyeonye/flipped_11B)|11 billion|
|[Flipped_3B](https://huggingface.co/seonghyeonye/flipped_3B)|3 billion|
Here is how to download the model in PyTorch:
```python
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained("seonghyeonye/flipped_3B")
tokenizer = T5Tokenizer.from_pretrained("seonghyeonye/flipped_3B")
```
If you want to use another checkpoint, please replace the path in `T5Tokenizer` and `T5ForConditionalGeneration`.
We also provide a quick [Jupyter Notebook](https://github.com/seonghyeonye/Flipped-Learning/blob/master/flipped_inference.ipynb) where you can inference with our method.
**Note: the model was trained with fp32 activations. As such, we highly discourage running inference with fp16.**
# Training procedure
FLIPPED models are based on [T5](https://huggingface.co/google/t5-v1_1-xl), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4).
At a high level, the input text along with output label is fed to the encoder and the instruction text is produced by the decoder. The model is fine-tuned to autoregressively generate the target. We also feed input text along with a wrong input, adding an unlikelihood loss in order not to make model produce the proper instruction in that case. Here are our training details.
Training details:
- Fine-tuning steps: 5'000
- Input sequence length: 512
- Target sequence length: 128
- Batch size: 240
- Optimizer: Adafactor
- Learning rate: 5e-5
- Dropout: 0.1
- Sampling strategy: proportional to the number of examples in each dataset (we randomly sampled any dataset if it has over 500'000 examples so that it has at most 500'000 examples. Also, we randomly choose which instruction to generate for each training steps, so ideally each instruction appears *num_examples/num_templates* while training.)
# Training data
We trained different variants T0 with different mixtures of datasets.
|Model|Training datasets|
|--|--|
|FLIPPED_11B|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ<br>- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp<br>- Topic Classification: AG News, DBPedia<br>- Paraphrase Identification: MRPC, PAWS, QQP|
|FLIPPED_3B|Same as FLIPPED_11B|
We only choose prompts examples that has output lables, which can be found on the dataset page.
# Evaluation data
We evaluate our models on following datasets:
|Task category|Datasets|
|-|-|
|Natural language inference|ANLI(R1, R2, R3), CB, RTE|
|Coreference resolution|WSC, Winogrande|
|Word sense disambiguation|WiC|
|Sentence completion|COPA, HellaSwag, Story Cloze|
|QA|PIQA, ARC-Challenge, OpenbookQA|
We also evaluate FLIPPED on a subset of [BIG-bench benchmark](https://github.com/google/BIG-bench):
- Code description task
- Conceptual combinations
- Hindu knowledge json
- Known unknowns
- Language identification
- Logic grid puzzle task
- Logical deduction
- Common misconceptions
- Movie dialog same or different
- Novel concepts
- Strategyqa
- Formal fallacies syllogisms negation
- VitaminC
- Winowhy multiple choice
# Label generalization
We evaluate the robustness of models on following datasets with changing the output label of the datasets. The substitute words can be found in our [paper](https://arxiv.org/abs/2210.02969).
|Task category|(Datasets, Template name)|
|-|-|
|Unseen tasks|(WSC, does the pronoun refer to), (CB, can we infer), (RTE, MNLI crowdsource)|
|Seen tasks|(IMDB, Reviewer Enjoyment Yes No), (PAWS, Meaning) |
The template name we used can be found in the [promptsource template library](https://github.com/bigscience-workshop/promptsource/tree/main/promptsource/templates).
# BibTeX entry and citation info
```bibtex
@article{ye2022guess,
title={Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners},
author={Ye, Seonghyeon and Kim, Doyoung and Jang, Joel and Shin, Joongbo and Seo, Minjoon},
journal={arXiv preprint arXiv:2210.02969},
year={2022}
}
``` |
dccuchile/albert-large-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 29 | null | ---
license: mit
---
### Chungus Poodl Pet on Stable Diffusion
This is the `<poodl-chungus-big>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
dccuchile/albert-tiny-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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}
}
} | 32 | null | ---
language:
- ms
tags:
- translation
metrics:
- sacrebleu
---
# finetune-translation-t5-super-tiny-standard-bahasa-cased
Finetuned T5 super tiny on EN-MS and MS-EN translation tasks.
## Dataset
1. EN-MS dataset, https://huggingface.co/datasets/mesolitica/en-ms
2. MS-EN dataset, https://huggingface.co/datasets/mesolitica/ms-en
3. NLLB eng_Latn-zsm_Latn, https://github.com/huseinzol05/malay-dataset/tree/master/translation/laser
## Finetune details
1. Finetune using single RTX 3090 Ti.
Scripts at https://github.com/huseinzol05/malaya/tree/master/session/translation/hf-t5
## Supported prefix
1. `terjemah Inggeris ke Melayu: {string}`, for EN-MS translation.
2. `terjemah Melayu ke Inggeris: {string}`, for MS-EN translation.
## Evaluation
eng_Latn-zsm_Latn,
```
{'name': 'BLEU',
'score': 39.18834189893951,
'_mean': -1.0,
'_ci': -1.0,
'_verbose': '72.6/48.3/33.5/23.6 (BP = 0.960 ratio = 0.961 hyp_len = 21172 ref_len = 22027)',
'bp': 0.9604210226409274,
'counts': [15376, 9741, 6434, 4284],
'totals': [21172, 20175, 19178, 18181],
'sys_len': 21172,
'ref_len': 22027,
'precisions': [72.62422066880787,
48.28252788104089,
33.54885806653457,
23.563060337715196],
'prec_str': '72.6/48.3/33.5/23.6',
'ratio': 0.9611840014527625}
chrF2++ = 64.03
```
zsm_Latn-eng_Latn,
```
{'name': 'BLEU',
'score': 34.10561487832948,
'_mean': -1.0,
'_ci': -1.0,
'_verbose': '67.3/41.6/27.8/18.7 (BP = 0.982 ratio = 0.982 hyp_len = 23139 ref_len = 23570)',
'bp': 0.9815458410942027,
'counts': [15569, 9216, 5871, 3777],
'totals': [23139, 22142, 21145, 20148],
'sys_len': 23139,
'ref_len': 23570,
'precisions': [67.28467090194044,
41.62225634540692,
27.765429179475053,
18.746277546158428],
'prec_str': '67.3/41.6/27.8/18.7',
'ratio': 0.9817140432753501}
chrF2++ = 59.18
``` |
dccuchile/albert-tiny-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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}
} | 8 | null | ---
tags:
- generated_from_trainer
model-index:
- name: gpt2-gpt2-mc-weight0-epoch15
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. -->
# gpt2-gpt2-mc-weight0-epoch15
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9633
- Cls loss: 6.8154
- Lm loss: 3.9632
- Cls Accuracy: 0.1337
- Cls F1: 0.0531
- Cls Precision: 0.0331
- Cls Recall: 0.1337
- Perplexity: 52.63
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:|
| 4.1973 | 1.0 | 3470 | 4.0341 | 6.8497 | 4.0341 | 0.1331 | 0.0529 | 0.0330 | 0.1331 | 56.49 |
| 4.0446 | 2.0 | 6940 | 3.9948 | 6.8450 | 3.9947 | 0.1337 | 0.0531 | 0.0331 | 0.1337 | 54.31 |
| 3.9714 | 3.0 | 10410 | 3.9795 | 6.8404 | 3.9794 | 0.1337 | 0.0531 | 0.0331 | 0.1337 | 53.48 |
| 3.9176 | 4.0 | 13880 | 3.9686 | 6.8359 | 3.9686 | 0.1337 | 0.0531 | 0.0331 | 0.1337 | 52.91 |
| 3.8739 | 5.0 | 17350 | 3.9580 | 6.8317 | 3.9579 | 0.1331 | 0.0529 | 0.0330 | 0.1331 | 52.35 |
| 3.8359 | 6.0 | 20820 | 3.9591 | 6.8286 | 3.9590 | 0.1331 | 0.0529 | 0.0330 | 0.1331 | 52.40 |
| 3.8035 | 7.0 | 24290 | 3.9585 | 6.8263 | 3.9585 | 0.1331 | 0.0529 | 0.0330 | 0.1331 | 52.38 |
| 3.7762 | 8.0 | 27760 | 3.9585 | 6.8240 | 3.9585 | 0.1331 | 0.0529 | 0.0330 | 0.1331 | 52.38 |
| 3.7517 | 9.0 | 31230 | 3.9567 | 6.8216 | 3.9567 | 0.1337 | 0.0531 | 0.0331 | 0.1337 | 52.28 |
| 3.7313 | 10.0 | 34700 | 3.9599 | 6.8193 | 3.9598 | 0.1337 | 0.0531 | 0.0331 | 0.1337 | 52.45 |
| 3.7131 | 11.0 | 38170 | 3.9606 | 6.8169 | 3.9605 | 0.1337 | 0.0531 | 0.0331 | 0.1337 | 52.48 |
| 3.6982 | 12.0 | 41640 | 3.9614 | 6.8154 | 3.9614 | 0.1337 | 0.0531 | 0.0331 | 0.1337 | 52.53 |
| 3.6862 | 13.0 | 45110 | 3.9623 | 6.8154 | 3.9622 | 0.1337 | 0.0531 | 0.0331 | 0.1337 | 52.57 |
| 3.6767 | 14.0 | 48580 | 3.9621 | 6.8154 | 3.9620 | 0.1337 | 0.0531 | 0.0331 | 0.1337 | 52.56 |
| 3.6711 | 15.0 | 52050 | 3.9633 | 6.8154 | 3.9632 | 0.1337 | 0.0531 | 0.0331 | 0.1337 | 52.63 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1 |
dccuchile/albert-tiny-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
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"AlbertForSequenceClassification"
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} | 29 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: refinement-finetuned-mnli-kaggle-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. -->
# refinement-finetuned-mnli-kaggle-2
This model is a fine-tuned version of [mfreihaut/refinement-finetuned-mnli-1](https://huggingface.co/mfreihaut/refinement-finetuned-mnli-1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4099
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.6157 | 1.0 | 12599 | 0.5321 |
| 0.5355 | 2.0 | 25198 | 0.6121 |
| 0.4084 | 3.0 | 37797 | 0.3938 |
| 0.4596 | 4.0 | 50396 | 0.3925 |
| 0.4625 | 5.0 | 62995 | 0.3928 |
| 0.4668 | 6.0 | 75594 | 0.3892 |
| 0.5054 | 7.0 | 88193 | 0.4097 |
| 0.4953 | 8.0 | 100792 | 0.4099 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1
|
dccuchile/albert-tiny-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-multilingual-uncased-oct-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-multilingual-uncased-oct-3
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: 0.0532
- F1: 0.9073
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1205 | 1.0 | 565 | 0.0662 | 0.8449 |
| 0.0524 | 2.0 | 1130 | 0.0535 | 0.8921 |
| 0.033 | 3.0 | 1695 | 0.0532 | 0.9073 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
dccuchile/albert-tiny-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec_korean
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. -->
# wav2vec_korean
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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_steps: 1000
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.13.0
|
dccuchile/albert-tiny-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 31 | null | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-bert-base-uncased-mc-weight0-epoch15
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-base-uncased-bert-base-uncased-mc-weight0-epoch15
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3651
- Cls loss: 2.9223
- Lm loss: 4.3649
- Cls Accuracy: 0.0248
- Cls F1: 0.0057
- Cls Precision: 0.0061
- Cls Recall: 0.0248
- Perplexity: 78.64
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:|
| 4.8711 | 1.0 | 3470 | 4.5156 | 2.9252 | 4.5155 | 0.0213 | 0.0047 | 0.0042 | 0.0213 | 91.42 |
| 4.483 | 2.0 | 6940 | 4.4193 | 2.9248 | 4.4191 | 0.0219 | 0.0048 | 0.0042 | 0.0219 | 83.02 |
| 4.3345 | 3.0 | 10410 | 4.3684 | 2.9244 | 4.3682 | 0.0219 | 0.0048 | 0.0042 | 0.0219 | 78.91 |
| 4.2266 | 4.0 | 13880 | 4.3445 | 2.9241 | 4.3443 | 0.0225 | 0.0049 | 0.0043 | 0.0225 | 77.04 |
| 4.1388 | 5.0 | 17350 | 4.3260 | 2.9237 | 4.3258 | 0.0231 | 0.0050 | 0.0044 | 0.0231 | 75.63 |
| 4.0644 | 6.0 | 20820 | 4.3299 | 2.9234 | 4.3297 | 0.0231 | 0.0050 | 0.0044 | 0.0231 | 75.92 |
| 3.999 | 7.0 | 24290 | 4.3278 | 2.9232 | 4.3276 | 0.0231 | 0.0059 | 0.0061 | 0.0231 | 75.76 |
| 3.9426 | 8.0 | 27760 | 4.3269 | 2.9230 | 4.3267 | 0.0231 | 0.0059 | 0.0061 | 0.0231 | 75.70 |
| 3.8929 | 9.0 | 31230 | 4.3324 | 2.9228 | 4.3322 | 0.0248 | 0.0061 | 0.0062 | 0.0248 | 76.11 |
| 3.8488 | 10.0 | 34700 | 4.3382 | 2.9227 | 4.3380 | 0.0248 | 0.0061 | 0.0064 | 0.0248 | 76.55 |
| 3.8116 | 11.0 | 38170 | 4.3461 | 2.9225 | 4.3459 | 0.0242 | 0.0057 | 0.0061 | 0.0242 | 77.16 |
| 3.7791 | 12.0 | 41640 | 4.3537 | 2.9224 | 4.3535 | 0.0248 | 0.0057 | 0.0061 | 0.0248 | 77.75 |
| 3.7532 | 13.0 | 45110 | 4.3593 | 2.9223 | 4.3591 | 0.0248 | 0.0057 | 0.0061 | 0.0248 | 78.19 |
| 3.7321 | 14.0 | 48580 | 4.3588 | 2.9223 | 4.3586 | 0.0248 | 0.0057 | 0.0061 | 0.0248 | 78.15 |
| 3.7182 | 15.0 | 52050 | 4.3651 | 2.9223 | 4.3649 | 0.0248 | 0.0057 | 0.0061 | 0.0248 | 78.64 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1 |
dccuchile/albert-xlarge-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: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: train
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8648740833380706
---
<!-- 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-de
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: 0.1392
- F1: 0.8649
## 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: 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1616 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1419 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1392 | 0.8649 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
dccuchile/albert-xxlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 26 | null | ---
license: mit
---
### Liminal spaces 2.0 on Stable Diffusion
This is the `liminal image` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
dccuchile/albert-xxlarge-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 26 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 240.84 +/- 20.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dccuchile/albert-xxlarge-spanish-finetuned-xnli | [
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"albert",
"text-classification",
"transformers"
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}
}
} | 68 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: banglabert-bert-finetuned-ner
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. -->
# banglabert-bert-finetuned-ner
This model is a fine-tuned version of [csebuetnlp/banglabert](https://huggingface.co/csebuetnlp/banglabert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9526
- Precision: 0.0143
- Recall: 0.0769
- F1: 0.0241
- Accuracy: 0.0143
## 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 | 1 | 2.0085 | 0.0143 | 0.0769 | 0.0241 | 0.0143 |
| No log | 2.0 | 2 | 1.9711 | 0.0143 | 0.0769 | 0.0241 | 0.0143 |
| No log | 3.0 | 3 | 1.9526 | 0.0143 | 0.0769 | 0.0241 | 0.0143 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
dccuchile/albert-base-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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"AlbertForPreTraining"
],
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} | 586 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
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. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
dccuchile/albert-large-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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"AlbertForPreTraining"
],
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} | 75 | null | ---
tags:
- conversational
---
# Kashiwagi Osamu DialoGPT Model |
dccuchile/albert-tiny-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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"AlbertForPreTraining"
],
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}
} | 393 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 249.94 +/- 23.25
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dccuchile/albert-xxlarge-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
"architectures": [
"AlbertForPreTraining"
],
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}
}
} | 42 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ijelid-indobertweet
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. -->
# ijelid-indobertweet
This model is a fine-tuned version of [indolem/indobertweet-base-uncased](https://huggingface.co/indolem/indobertweet-base-uncased) on the Indonesian-Javanese-English code-mixed Twitter dataset.
Label ID and its corresponding name:
| Label ID | Label Name |
|:---------------:|:------------------------------------------:
| LABEL_0 | English (EN) |
| LABEL_1 | Indonesian (ID) |
| LABEL_2 | Javanese (JV) |
| LABEL_3 | Mixed Indonesian-English (MIX-ID-EN) |
| LABEL_4 | Mixed Indonesian-Javanese (MIX-ID-JV) |
| LABEL_5 | Mixed Javanese-English (MIX-JV-EN) |
| LABEL_6 | Other (O) |
It achieves the following results on the evaluation set:
- Loss: 0.2804
- Precision: 0.9323
- Recall: 0.9394
- F1: 0.9356
- Accuracy: 0.9587
It achieves the following results on the test set:
- Overall Precision: 0.9326
- Overall Recall: 0.9421
- Overall F1: 0.9371
- Overall Accuracy: 0.9643
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 386 | 0.1666 | 0.8968 | 0.9014 | 0.8982 | 0.9465 |
| 0.257 | 2.0 | 772 | 0.1522 | 0.9062 | 0.9368 | 0.9206 | 0.9517 |
| 0.1092 | 3.0 | 1158 | 0.1462 | 0.9233 | 0.9335 | 0.9280 | 0.9556 |
| 0.0739 | 4.0 | 1544 | 0.1563 | 0.9312 | 0.9361 | 0.9336 | 0.9568 |
| 0.0739 | 5.0 | 1930 | 0.1671 | 0.9224 | 0.9413 | 0.9312 | 0.9573 |
| 0.0474 | 6.0 | 2316 | 0.1719 | 0.9303 | 0.9394 | 0.9346 | 0.9578 |
| 0.0339 | 7.0 | 2702 | 0.1841 | 0.9249 | 0.9389 | 0.9314 | 0.9576 |
| 0.0237 | 8.0 | 3088 | 0.2030 | 0.9224 | 0.9380 | 0.9297 | 0.9570 |
| 0.0237 | 9.0 | 3474 | 0.2106 | 0.9289 | 0.9377 | 0.9331 | 0.9576 |
| 0.0185 | 10.0 | 3860 | 0.2264 | 0.9277 | 0.9389 | 0.9330 | 0.9571 |
| 0.0132 | 11.0 | 4246 | 0.2331 | 0.9336 | 0.9344 | 0.9339 | 0.9574 |
| 0.0101 | 12.0 | 4632 | 0.2403 | 0.9353 | 0.9375 | 0.9363 | 0.9586 |
| 0.0082 | 13.0 | 5018 | 0.2509 | 0.9311 | 0.9373 | 0.9340 | 0.9582 |
| 0.0082 | 14.0 | 5404 | 0.2548 | 0.9344 | 0.9351 | 0.9346 | 0.9578 |
| 0.0062 | 15.0 | 5790 | 0.2608 | 0.9359 | 0.9372 | 0.9365 | 0.9588 |
| 0.005 | 16.0 | 6176 | 0.2667 | 0.9298 | 0.9407 | 0.9350 | 0.9587 |
| 0.0045 | 17.0 | 6562 | 0.2741 | 0.9337 | 0.9408 | 0.9371 | 0.9592 |
| 0.0045 | 18.0 | 6948 | 0.2793 | 0.9342 | 0.9371 | 0.9355 | 0.9589 |
| 0.0035 | 19.0 | 7334 | 0.2806 | 0.9299 | 0.9391 | 0.9342 | 0.9588 |
| 0.0034 | 20.0 | 7720 | 0.2804 | 0.9323 | 0.9394 | 0.9356 | 0.9587 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.7.1
- Datasets 2.5.1
- Tokenizers 0.12.1
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
} | 25 | null | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-ja_kftt
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. -->
# marian-finetuned-kde4-en-to-ja_kftt
This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-en-ja](https://huggingface.co/Helsinki-NLP/opus-tatoeba-en-ja) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2891
- Bleu: 0.3128
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
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": {
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}
} | 1 | null | ---
language:
- en
tags:
- text-classification
- claim-detection
license: "mit"
datasets:
- Nithiwat/claim-detection
widget:
- text: "This is the best cast iron skillet you will ever buy."
- text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday."
- text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book"
--- |
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
},
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}
}
} | 24 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-poetry-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-poetry-model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.10.3
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pawsx | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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} | 29 | null | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: marian-finetuned-kftt_kde4-en-to-ja
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. -->
# marian-finetuned-kftt_kde4-en-to-ja
This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_2_2_1](https://huggingface.co/Hoax0930/kyoto_marian_mod_2_2_1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 8.3622
- Bleu: 2.6910
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pos | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
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}
} | 3 | null | ---
language:
- en
tags:
- text-classification
- claim-detection
license: "mit"
datasets:
- Nithiwat/claim-detection
widget:
- text: "This is the best cast iron skillet you will ever buy."
- text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday."
- text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book"
--- |
dccuchile/distilbert-base-spanish-uncased | [
"pytorch",
"distilbert",
"fill-mask",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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} | 670 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.88
- name: F1
type: f1
value: 0.881578947368421
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3095
- Accuracy: 0.88
- F1: 0.8816
## 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
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-1 | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 7 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: bn
license: apache-2.0
---
# Stanza model for Bengali (bn)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza).
This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2023-05-19 03:30:30.527
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate-1 | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 1 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: ml
license: apache-2.0
---
# Stanza model for Malayalam (ml)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza).
This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2023-05-19 04:10:24.073
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 7 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: sd
license: apache-2.0
---
# Stanza model for Sindhi (sd)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza).
This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2023-05-19 04:21:22.146
|
CennetOguz/distilbert-base-uncased-finetuned-recipe | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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"DistilBertForMaskedLM"
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} | 2 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: si
license: apache-2.0
---
# Stanza model for Sinhala (si)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza).
This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2023-05-19 04:21:41.790
|
Chaddmckay/Cdm | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mBART_slang_to_standard_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mBART_slang_to_standard_4
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9058
- Bleu: 60.5005
- Gen Len: 47.7251
## 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: 4
- eval_batch_size: 4
- seed: 42
- 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 | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 106 | 2.6704 | 60.4144 | 51.1659 |
| No log | 2.0 | 212 | 2.0665 | 60.2528 | 47.1848 |
| No log | 3.0 | 318 | 1.9058 | 60.5005 | 47.7251 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Chaewon/mnmt_decoder_en_gpt2 | []
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} | 0 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: bigscience-bloom-rail-1.0
inference: true
---
# stable-diffusion-wikiart |
Chaima/TunBerto | []
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} | 0 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: bigscience-bloom-rail-1.0
inference: true
---
# stable-diffusion-wikiart
sd-wikiart-v2 is a stable diffusion model that has been fine-tuned on the [wikiart dataset](https://huggingface.co/datasets/fusing/wikiart_captions) to generate artistic images in different style and genres.
<img src="https://huggingface.co/valhalla/sd-wikiart-v2/resolve/main/wikiart.png">
# Gradio
[](https://colab.research.google.com/drive/1i7HJlTzVPEirNedDV-TcR5Ok2_8QI6zC?usp=sharing)
## Model Description
The model originally used for fine-tuning is [Stable Diffusion V1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), which is a latent image diffusion model trained on [LAION2B-en](https://huggingface.co/datasets/laion/laion2B-en).
The current model has been fine-tuned with a learning rate of 1e-05 for 1 epoch on 81K text-image pairs from wikiart dataset. Only the attention layers of the model are fine-tuned. This is done to avoid catastrophic forgetting, the model can generate artistic images given specific prompts but still retains most of its previous knowledge.
## Training Data
TODO
## 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)
## Downstream Uses
This model can be used for entertainment purposes and as a generative art assistant.
## Example Code
```python
import torch
from diffusers import StableDiffusionPipeline
model_id = "valhalla/sd-wikiart-v2"
device = "cuda"
pipe = StableDiffusionPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
)
pipe = pipe.to(device)
prompt = "a painting of eiffel tower in the style of surrealism"
with torch.autocast("cuda"):
image = pipe(prompt, guidance_scale=7.5).images[0]
image.save("eiffel_impressionism.png")
``` |
chainyo/speaker-recognition-meetup | []
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} | 1 | null | ---
license: mit
---
### crb-surrealz on Stable Diffusion
This is the `<crbsurreal>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
ChaitanyaU/FineTuneLM | []
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} | 0 | 2022-10-04T09:02:01Z | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- xfun
model-index:
- name: layoutxlm-finetuned-xfund-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. -->
# layoutxlm-finetuned-xfund-fr
This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on the xfun dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0+cu111
- Datasets 2.5.2
- Tokenizers 0.12.1
|
Chakita/KNUBert | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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} | 20 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: dataset_radiology_20220912.tsv
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. -->
# dataset_radiology_20220912.tsv
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- 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: 1
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Chakita/Kalbert | [
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
]
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} | 5 | 2022-10-04T09:22:12Z |
---
language: en
---
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
### Run Configuration
{
"arch": "crnn_vgg16_bn",
"train_path": "/content/drive/Shareddrives/DataScience/DISA/datasets/IAM_Dataset/IAM/data",
"val_path": "/content/drive/MyDrive/OCR_Finetuning/test",
"train_samples": 1000,
"val_samples": 20,
"font": "FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf",
"min_chars": 1,
"max_chars": 12,
"name": null,
"epochs": 10,
"batch_size": 64,
"input_size": 32,
"lr": 0.001,
"workers": 2,
"resume": null,
"vocab": "french",
"test_only": false,
"show_samples": false,
"wb": false,
"push_to_hub": false,
"pretrained": false,
"amp": false,
"find_lr": false
} |
Chakita/KannadaBERT | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"masked-lm",
"fill-in-the-blanks",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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} | 5 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8648740833380706
---
<!-- 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-de
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: 0.1365
- F1: 0.8649
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Chakita/gpt2_mwp | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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"max_length": 50
},
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}
} | 6 | null | Access to model maxchoi/aitest is restricted and you are not in the authorized list. Visit https://huggingface.co/maxchoi/aitest to ask for access. |
Chalponkey/DialoGPT-small-Barry | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
} | 11 | 2022-10-04T09:36:21Z | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-bert-base-uncased-mc-weight0.25-epoch15
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-base-uncased-bert-base-uncased-mc-weight0.25-epoch15
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.1343
- Cls loss: 3.0991
- Lm loss: 4.3588
- Cls Accuracy: 0.6092
- Cls F1: 0.6066
- Cls Precision: 0.6082
- Cls Recall: 0.6092
- Perplexity: 78.17
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:|
| 5.3372 | 1.0 | 3470 | 4.9249 | 1.5682 | 4.5325 | 0.5712 | 0.5567 | 0.5751 | 0.5712 | 92.99 |
| 4.8287 | 2.0 | 6940 | 4.7830 | 1.3889 | 4.4355 | 0.6231 | 0.6169 | 0.6448 | 0.6231 | 84.39 |
| 4.6295 | 3.0 | 10410 | 4.7585 | 1.4752 | 4.3894 | 0.6248 | 0.6160 | 0.6340 | 0.6248 | 80.59 |
| 4.4704 | 4.0 | 13880 | 4.7707 | 1.6098 | 4.3678 | 0.6121 | 0.6079 | 0.6156 | 0.6121 | 78.87 |
| 4.3364 | 5.0 | 17350 | 4.8008 | 1.8102 | 4.3478 | 0.6086 | 0.6068 | 0.6105 | 0.6086 | 77.31 |
| 4.2245 | 6.0 | 20820 | 4.8353 | 1.9486 | 4.3477 | 0.6121 | 0.6075 | 0.6131 | 0.6121 | 77.30 |
| 4.1289 | 7.0 | 24290 | 4.8883 | 2.1912 | 4.3400 | 0.6110 | 0.6076 | 0.6182 | 0.6110 | 76.71 |
| 4.0485 | 8.0 | 27760 | 4.9394 | 2.4203 | 4.3337 | 0.5914 | 0.5862 | 0.6016 | 0.5914 | 76.23 |
| 3.9826 | 9.0 | 31230 | 5.0026 | 2.6664 | 4.3354 | 0.6006 | 0.5936 | 0.6035 | 0.6006 | 76.35 |
| 3.9277 | 10.0 | 34700 | 4.9902 | 2.5992 | 4.3398 | 0.6035 | 0.6032 | 0.6088 | 0.6035 | 76.69 |
| 3.8794 | 11.0 | 38170 | 5.0698 | 2.9006 | 4.3441 | 0.6156 | 0.6127 | 0.6213 | 0.6156 | 77.02 |
| 3.8428 | 12.0 | 41640 | 5.0956 | 2.9795 | 4.3501 | 0.6127 | 0.6110 | 0.6184 | 0.6127 | 77.49 |
| 3.8129 | 13.0 | 45110 | 5.1223 | 3.0646 | 4.3555 | 0.6138 | 0.6099 | 0.6172 | 0.6138 | 77.91 |
| 3.7891 | 14.0 | 48580 | 5.1242 | 3.0809 | 4.3534 | 0.6058 | 0.6045 | 0.6071 | 0.6058 | 77.74 |
| 3.7744 | 15.0 | 52050 | 5.1343 | 3.0991 | 4.3588 | 0.6092 | 0.6066 | 0.6082 | 0.6092 | 78.17 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1 |
Champion/test_upload_vox2_wavlm_epoch8 | [
"sidekit",
"audio"
]
| null | {
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}
} | 0 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- mouss/autotrain-data-damages
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.007316433431312107
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1652858619
- CO2 Emissions (in grams): 0.0073
## Validation Metrics
- Loss: 0.082
- Accuracy: 0.989
- Precision: 1.000
- Recall: 0.978
- AUC: 0.995
- F1: 0.989 |
Cheapestmedsshop/Buymodafinilus | []
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} | 0 | null | ---
license: cc-by-nc-4.0
pipeline_tag: fill-mask
tags:
- legal
language:
- da
datasets:
- multi_eurlex
- DDSC/partial-danish-gigaword-no-twitter
model-index:
- name: coastalcph/danish-legal-bert-base
results: []
---
# Danish LegalBERT (derivative of Maltehb/danish-bert-botxo)
This model is a derivative of [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) adapted to legal text. It has been pre-trained on a combination of the Danish part of the MultiEURLEX (Chalkidis et al., 2021) dataset comprising EU legislation and two subsets (`retsinformationdk`, `retspraksis`) of the Danish Gigaword Corpus (Derczynski et al., 2021) comprising legal proceedings.
It achieves the following results on the evaluation set:
- Loss: -
## Model description
This is a BERT model (Devlin et al., 2018) model pre-trained on Danish legal corpora. It follows a base configuration with 12 Transformer layers, each one with 768 hidden units and 12 attention heads.
## Intended uses & limitations
More information needed
## Training and evaluation data
This model is pre-training on a combination of the Danish part of the MultiEURLEX dataset and two subsets (`retsinformationdk`, `retspraksis`) of the Danish Gigaword Corpus.
## Training procedure
The model was initially pre-trained for 500k steps with sequences up to 128 tokens, and then continued pre-training for additional 100k with sequences up to 512 tokens.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: tpu
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 100000
### Training results
| Training Loss | Length | Step | Validation Loss |
|:-------------:|:------:|:-------:|:---------------:|
| 1.0030 | 128 | 50000 | - |
| 0.9593 | 128 | 100000 | - |
|
Cheatham/xlm-roberta-base-finetuned | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
]
| text-classification | {
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"XLMRobertaForSequenceClassification"
],
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} | 20 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question generation
- answer extraction
widget:
- text: "generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
example_title: "Question Generation Example 1"
- text: "generate question: Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
example_title: "Question Generation Example 2"
- text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ."
example_title: "Question Generation Example 3"
- text: "extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress."
example_title: "Answer Extraction Example 1"
- text: "extract answers: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress. <hl>"
example_title: "Answer Extraction Example 2"
model-index:
- name: lmqg/t5-large-squad-qg-ae
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 27.2
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 54.23
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 27.81
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 90.69
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 65.29
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer
value: 92.87
- name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer
value: 93.04
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer
value: 92.72
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer
value: 64.67
- name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer
value: 64.63
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer
value: 64.82
- name: BLEU4 (Answer Extraction)
type: bleu4_answer_extraction
value: 49.73
- name: ROUGE-L (Answer Extraction)
type: rouge_l_answer_extraction
value: 69.82
- name: METEOR (Answer Extraction)
type: meteor_answer_extraction
value: 44.46
- name: BERTScore (Answer Extraction)
type: bertscore_answer_extraction
value: 91.63
- name: MoverScore (Answer Extraction)
type: moverscore_answer_extraction
value: 82.48
- name: AnswerF1Score (Answer Extraction)
type: answer_f1_score__answer_extraction
value: 70.3
- name: AnswerExactMatch (Answer Extraction)
type: answer_exact_match_answer_extraction
value: 59.26
---
# Model Card of `lmqg/t5-large-squad-qg-ae`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation and answer extraction jointly on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [t5-large](https://huggingface.co/t5-large)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="lmqg/t5-large-squad-qg-ae")
# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/t5-large-squad-qg-ae")
# answer extraction
answer = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
# question generation
question = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-large-squad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore | 90.69 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 59.93 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 43.98 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 34.19 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 27.2 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 27.81 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 65.29 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 54.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-large-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 92.87 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedF1Score (MoverScore) | 64.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (BERTScore) | 92.72 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (MoverScore) | 64.82 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (BERTScore) | 93.04 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (MoverScore) | 64.63 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/t5-large-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch | 59.26 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score | 70.3 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore | 91.63 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 60.87 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 56.96 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 53.12 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 49.73 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 44.46 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 82.48 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 69.82 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: ['paragraph_answer', 'paragraph_sentence']
- output_types: ['question', 'answer']
- prefix_types: ['qg', 'ae']
- model: t5-large
- max_length: 512
- max_length_output: 32
- epoch: 3
- batch: 16
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-large-squad-qg-ae/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
Cheatham/xlm-roberta-large-finetuned-d1 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
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}
}
} | 20 | 2022-10-04T10:12:59Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8733333333333333
- name: F1
type: f1
value: 0.8741721854304636
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3054
- Accuracy: 0.8733
- F1: 0.8742
## 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
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Cheatham/xlm-roberta-large-finetuned-d1r01 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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},
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},
"translation_en_to_fr": {
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}
} | 21 | 2022-10-04T10:47:00Z | ---
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: finetuning-cardiffnlp-twitter-roberta-base-sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: sentiment
split: train
args: sentiment
metrics:
- name: Accuracy
type: accuracy
value: 0.7433333333333333
- name: F1
type: f1
value: 0.7418048347838402
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-cardiffnlp-twitter-roberta-base-sentiment
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0244
- Accuracy: 0.7433
- F1: 0.7418
## 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
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Cheatham/xlm-roberta-large-finetuned-r01 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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}
} | 23 | null | ---
license: bigscience-bloom-rail-1.0
language:
- en
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
---
このモデルは、アイドルマスター シャイニーカラーズに登場するアイドル、芹沢あさひのイラストを生成するのに特化したStable-DiffusionのDiffuser用のモデルです。
This model is for Diffuser, a Stable-Diffusion specialized for generating illustrations of Asahi Serizawa, an idol from THE iDOLM@STER SHINY COLORS.
DreamBoothを利用して、WaifuDiffusionを追加学習し作成されました。
It was created using DreamBooth with additional learning of WaifuDiffusion.
生成した画像が芹沢あさひに類似していた場合、その著作権はBandai Namco Entertainment Inc.に所属する可能性があります。
If the generated image resembles Asahi Serizawa, the copyright may belong to Bandai Namco Entertainment Inc.
その他の利用上の注意点は bigscience-bloom-rail-1.0のライセンスを御覧ください。
For other usage notes, please refer to the license of bigscience-bloom-rail-1.0.
https://hf.space/static/bigscience/license/index.html
|
ChukSamuels/DialoGPT-small-Dr.FauciBot | [
"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,
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},
"text-generation": {
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},
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},
"translation_en_to_fr": {
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}
} | 13 | null | ---
tags:
- autotrain
- token-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Akshata/autotrain-data-person-name-validity1
co2_eq_emissions:
emissions: 0.015012024821802214
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1655358687
- CO2 Emissions (in grams): 0.0150
## Validation Metrics
- Loss: 0.038
- Accuracy: 0.991
- Precision: 0.000
- Recall: 0.000
- F1: 0.000
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Akshata/autotrain-person-name-validity1-1655358687
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("Akshata/autotrain-person-name-validity1-1655358687", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Akshata/autotrain-person-name-validity1-1655358687", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Chun/DialoGPT-small-dailydialog | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"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": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
} | 10 | null | ---
language:
- en
tags:
- esc
datasets:
- earnings22
---
To reproduce this run, first call `get_ctc_tokenizer.py` to train the CTC tokenizer and then execute the following command to train the CTC system:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_ctc.py \
--model_name_or_path="esc-benchmark/wav2vec2-ctc-pretrained" \
--tokenizer_name="wav2vec2-ctc-earnings22-tokenizer" \
--dataset_name="esc-benchmark/esc-datasets" \
--dataset_config_name="earnings22" \
--output_dir="./" \
--wandb_project="wav2vec2-ctc" \
--wandb_name="wav2vec2-ctc-earnings22" \
--max_steps="50000" \
--save_steps="10000" \
--eval_steps="10000" \
--learning_rate="3e-4" \
--logging_steps="25" \
--warmup_steps="5000" \
--preprocessing_num_workers="1" \
--hidden_dropout="0.2" \
--activation_dropout="0.2" \
--feat_proj_dropout="0.2" \
--do_train \
--do_eval \
--do_predict \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--push_to_hub \
--use_auth_token
```
|
Cilan/dalle-knockoff | []
| null | {
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}
} | 0 | null | ---
language:
- en
tags:
- esc
datasets:
- voxpopuli
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esc-benchmark/esc-datasets" \
--model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \
--dataset_config_name="voxpopuli" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-voxpopuli" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="1" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--logging_steps="25" \
--max_steps="10001" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--final_generation_max_length="225" \
--final_generation_num_beams="5" \
--generation_length_penalty="0.8" \
--hidden_dropout="0.2" \
--activation_dropout="0.2" \
--feat_proj_dropout="0.2" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
Cinnamon/electra-small-japanese-generator | [
"pytorch",
"electra",
"fill-mask",
"ja",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"ElectraForMaskedLM"
],
"model_type": "electra",
"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
}
}
} | 19 | null | ---
language:
- en
tags:
- esc
datasets:
- spgispeech
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esc-benchmark/esc-datasets" \
--model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \
--dataset_config_name="spgispeech" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-spgispeech" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="2" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--logging_steps="25" \
--max_steps="50001" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--final_generation_max_length="225" \
--final_generation_num_beams="14" \
--generation_length_penalty="1.6" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
ClydeWasTaken/DialoGPT-small-joshua | [
"conversational"
]
| conversational | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
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"min_length": null,
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}
} | 0 | null | ---
language:
- en
tags:
- esc
datasets:
- spgispeech
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
--model_name_or_path="medium.en" \
--dataset_name="esc-benchmark/esc-datasets" \
--dataset_config_name="spgispeech" \
--max_steps="5000" \
--output_dir="./" \
--run_name="whisper-spgispeech" \
--wandb_project="whisper" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--report_to="wandb" \
--preprocessing_num_workers="16" \
--evaluation_strategy="steps" \
--eval_steps="1000" \
--save_strategy="steps" \
--save_steps="1000" \
--generation_max_length="224" \
--length_column_name="input_lengths" \
--gradient_checkpointing \
--group_by_length \
--freeze_encoder \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--predict_with_generate \
--use_auth_token
```
|
CoShin/XLM-roberta-large_ko_en_nil_sts | []
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}
} | 0 | null | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
--- |
CoachCarter/distilbert-base-uncased-finetuned-squad | []
| null | {
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}
} | 0 | null | ---
language:
- en
tags:
- esc
datasets:
- earnings22
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
--model_name_or_path="medium.en" \
--dataset_name="esc-benchmark/esc-datasets" \
--dataset_config_name="earnings22" \
--max_steps="2500" \
--output_dir="./" \
--run_name="whisper-earnings22" \
--wandb_project="whisper" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--report_to="wandb" \
--preprocessing_num_workers="16" \
--evaluation_strategy="steps" \
--eval_steps="500" \
--save_strategy="steps" \
--save_steps="500" \
--generation_max_length="224" \
--length_column_name="input_lengths" \
--gradient_checkpointing \
--group_by_length \
--freeze_encoder \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--predict_with_generate \
--use_auth_token
```
|
CodeDanCode/SP-KyleBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
}
} | 15 | null | ---
language:
- en
tags:
- esc
datasets:
- ami
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
--model_name_or_path="medium.en" \
--dataset_name="esc-benchmark/esc-datasets" \
--dataset_config_name="ami" \
--max_steps="2500" \
--output_dir="./" \
--run_name="whisper-ami" \
--dropout_rate="0.1" \
--wandb_project="whisper" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--report_to="wandb" \
--preprocessing_num_workers="16" \
--evaluation_strategy="steps" \
--eval_steps="500" \
--save_strategy="steps" \
--save_steps="500" \
--generation_max_length="224" \
--length_column_name="input_lengths" \
--gradient_checkpointing \
--group_by_length \
--freeze_encoder \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--predict_with_generate \
--use_auth_token
```
|
CodeNinja1126/bert-p-encoder | [
"pytorch"
]
| null | {
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"prefix": null
}
}
} | 3 | null | ---
language:
- en
tags:
- esc
datasets:
- switchboard
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
--model_name_or_path="medium.en" \
--dataset_name="esc-benchmark/esc-datasets" \
--dataset_config_name="switchboard" \
--max_steps="5000" \
--output_dir="./" \
--run_name="whisper-switchboard" \
--max_steps="5000" \
--output_dir="./" \
--run_name="whisper-switchboard" \
--wandb_project="whisper" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--report_to="wandb" \
--preprocessing_num_workers="16" \
--evaluation_strategy="steps" \
--eval_steps="1000" \
--save_strategy="steps" \
--save_steps="1000" \
--generation_max_length="224" \
--length_column_name="input_lengths" \
--gradient_checkpointing \
--group_by_length \
--freeze_encoder \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--predict_with_generate \
--use_auth_token
```
|
CodeNinja1126/test-model | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"num_beams": null,
"prefix": null
}
}
} | 24 | null | ---
language:
- en
tags:
- esc
datasets:
- chime4
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
--model_name_or_path="medium.en" \
--dataset_name="esc-benchmark/esc-datasets" \
--dataset_config_name="chime4" \
--max_steps="2500" \
--output_dir="./" \
--run_name="whisper-chime4" \
--dropout_rate="0.1" \
--wandb_project="whisper" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--report_to="wandb" \
--preprocessing_num_workers="16" \
--evaluation_strategy="steps" \
--eval_steps="500" \
--save_strategy="steps" \
--save_steps="500" \
--generation_max_length="224" \
--length_column_name="input_lengths" \
--gradient_checkpointing \
--group_by_length \
--freeze_encoder \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--predict_with_generate \
--use_auth_token
```
|
CoderEFE/DialoGPT-marxbot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"has_space"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 11 | 2022-10-04T14:29:35Z | ---
language:
- en
tags:
- esc
datasets:
- librispeech
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esc-benchmark/esc-datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="librispeech" \
--output_dir="./" \
--run_name="conformer-rnnt-librispeech" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
CoderEFE/DialoGPT-medium-marx | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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"min_length": null,
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},
"text-generation": {
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},
"translation_en_to_fr": {
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"num_beams": null,
"prefix": null
}
}
} | 7 | null | ---
language:
- en
tags:
- esc
datasets:
- common_voice
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esc-benchmark/esc-datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="common_voice" \
--output_dir="./" \
--run_name="conformer-rnnt-common-voice" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--max_eval_duration_in_seconds="20" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
CoffeeAddict93/gpt1-call-of-the-wild | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8 | null | ---
language:
- en
tags:
- esc
datasets:
- tedlium
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esc-benchmark/esc-datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="tedlium" \
--output_dir="./" \
--run_name="rnnt-tedlium-baseline" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
CoffeeAddict93/gpt1-modest-proposal | [
"pytorch",
"openai-gpt",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"OpenAIGPTLMHeadModel"
],
"model_type": "openai-gpt",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 11 | 2022-10-04T14:36:03Z | ---
language:
- en
tags:
- esc
datasets:
- voxpopuli
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esc-benchmark/esc-datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="voxpopuli" \
--output_dir="./" \
--run_name="conformer-rnnt-voxpopuli" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
CoffeeAddict93/gpt2-call-of-the-wild | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 6 | null | ---
language:
- en
tags:
- esc
datasets:
- gigaspeech
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esc-benchmark/esc-datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--num_train_epochs="0.88" \
--dataset_config_name="gigaspeech" \
--output_dir="./" \
--run_name="conformer-rnnt-gigaspeech" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
CoffeeAddict93/gpt2-medium-call-of-the-wild | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
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}
} | 14 | null | ---
language:
- en
tags:
- esc
datasets:
- spgispeech
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esc-benchmark/esc-datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="spgispeech" \
--output_dir="./" \
--run_name="conformer-rnnt-spgispeech" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
CoffeeAddict93/gpt2-medium-modest-proposal | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
}
} | 7 | null | ---
language:
- en
tags:
- esc
datasets:
- earnings22
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esc/esc-datsets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="earnings22" \
--output_dir="./" \
--run_name="conformer-rnnt-earnings22" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
CoffeeAddict93/gpt2-modest-proposal | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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}
}
} | 12 | null | ---
language:
- en
tags:
- esc
datasets:
- ami
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esc-benchmark/esc-datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="ami" \
--output_dir="./" \
--run_name="conformer-rnnt-ami" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
CogComp/bart-faithful-summary-detector | [
"pytorch",
"jax",
"bart",
"text-classification",
"en",
"dataset:xsum",
"transformers",
"xsum",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"BartForSequenceClassification"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"min_length": 12,
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},
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}
} | 234 | null | ---
license: mit
---
### jfj on Stable Diffusion via Dreambooth
#### model by Seonauta
This your the Stable Diffusion model fine-tuned the jfj concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a photo of sks jfj**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
CogComp/roberta-temporal-predictor | [
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.00436",
"transformers",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
}
} | 14 | null | ---
language:
- en
tags:
- esc
datasets:
- switchboard
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esc-benchmark/esc-datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="switchboard" \
--output_dir="./" \
--run_name="conformer-rnnt-switchboard" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
CohleM/bert-nepali-tokenizer | []
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}
} | 0 | null | ---
language:
- en
tags:
- esc
datasets:
- chime4
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esc-benchmark/esc-datasets" \
--dataset_config_name="chime4" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--output_dir="./" \
--run_name="conformer-rnnt-chime4" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
CohleM/mbert-nepali-tokenizer | []
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}
} | 0 | null | ---
license: apache-2.0
tags:
- question-answering
- generated_from_trainer
model-index:
- name: roberta-base-squad2-nq-bioasq
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-squad2-nq-bioasq
## Model description
This model is a fine-tuned version of [nlpconnect/roberta-base-squad2-nq](https://huggingface.co/nlpconnect/roberta-base-squad2-nq) on the BioASQ 10b dataset.
## Intended uses & limitations
Cross-domain question answering!
## Training and evaluation data
Training: BioASQ 10B with SQUAD sampled evenly to match the same samples as BioASQ 10B
Eval: BioASQ 9B Eval with SQUAD Eval sampled evenly to match the same samples as BioASQ 9B Eval
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
Went from untrained exact match: 60.9% (f1 71.8%) to exact match: 95.2% (96.6% f1) on BioASQ 9B held out training set.
Scores on SQUAD+BioASQ remained stable at exact match: 72.5% (f1 81.4%) to 88.5% (f1 93.3%).
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ComCom/gpt2-large | [
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
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}
} | 1 | 2022-10-04T15:00:53Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: south-indian-foods
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6666666865348816
---
# south-indian-foods
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Idli
#### chutney
#### dosa
#### sambar
#### vada
 |
Connor/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 7 | null | ---
tags:
- generated_from_trainer
model-index:
- name: EleutherAI_gpt-neo-125M-stablediffionprompts
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. -->
# EleutherAI_gpt-neo-125M-stablediffionprompts
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 1024
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 44000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Connorvr/BrightBot-small | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
],
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},
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}
} | 7 | 2022-10-04T15:26:51Z | ---
language: en
inference: false
tags:
- text-generation
license: other
commercial: false
model-index:
- name: inverse-scaling/opt-350m_eval
results:
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/NeQA
type: inverse-scaling/NeQA
config: inverse-scaling--NeQA
split: train
metrics:
- name: Accuracy
type: accuracy
value: 0.4666666666666667
verified: true
- name: Loss
type: loss
value: 0.9192380222864449
verified: true
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/quote-repetition
type: inverse-scaling/quote-repetition
config: inverse-scaling--quote-repetition
split: train
metrics:
- name: Accuracy
type: accuracy
value: 0.9633333333333334
verified: true
- name: Loss
type: loss
value: 0.03444786100047819
verified: true
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/redefine-math
type: inverse-scaling/redefine-math
config: inverse-scaling--redefine-math
split: train
metrics:
- name: Accuracy
type: accuracy
value: 0.6877777777777778
verified: true
- name: Loss
type: loss
value: 0.6016371671193176
verified: true
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/hindsight-neglect-10shot
type: inverse-scaling/hindsight-neglect-10shot
config: inverse-scaling--hindsight-neglect-10shot
split: train
metrics:
- name: Accuracy
type: accuracy
value: 0.4380952380952381
verified: true
- name: Loss
type: loss
value: 0.8774787804555325
verified: true
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_cot_v3
type: mathemakitten/winobias_antistereotype_test_cot_v3
config: mathemakitten--winobias_antistereotype_test_cot_v3
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.44660194174757284
verified: true
- name: Loss
type: loss
value: 0.9301078982717057
verified: true
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_v5
type: mathemakitten/winobias_antistereotype_test_v5
config: mathemakitten--winobias_antistereotype_test_v5
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.4368932038834951
verified: true
- name: Loss
type: loss
value: 0.9175132444057151
verified: true
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.
**Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf).
Content from **this** model card has been written by the Hugging Face team.
## Intro
To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068)
> Large language models trained on massive text collections have shown surprising emergent
> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public
> can interact with these models through paid APIs, full model access is currently limited to only a
> few highly resourced labs. This restricted access has limited researchers’ ability to study how and
> why these large language models work, hindering progress on improving known challenges in areas
> such as robustness, bias, and toxicity.
> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M
> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match
> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data
> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and
> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the
> collective research community as a whole, which is only possible when models are available for study.
## Model description
OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.
OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read
the [official paper](https://arxiv.org/abs/2205.01068).
## Intended uses & limitations
The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.
In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt).
### 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="facebook/opt-350m")
>>> generator("Hello, I'm am conscious and")
[{'generated_text': "Hello, I'm am conscious and I'm a bit of a noob. I'm looking for"}]
```
By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
```python
>>> from transformers import pipeline, set_seed
>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-350m", do_sample=True)
>>> generator("Hello, I'm am conscious and")
[{'generated_text': "Hello, I'm am conscious and I'm interested in this project. Can I get an initial contact"}]
```
### Limitations and bias
As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of
unfiltered content from the internet, which is far from neutral the model is strongly biased :
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import pipeline, set_seed
>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-350m", do_sample=True, num_return_sequences=5)
>>> generator("The woman worked as a")
[{'generated_text': "The woman works as a substitute teacher for kids who have missed school. She's the teacher herself,"},
{'generated_text': 'The woman works as a security guard for another company and does an average of around $13/hour'},
{'generated_text': 'The woman works as a receptionist, she could at the least wait a week or two for her'},
{'generated_text': 'The woman works as a manager/intern/career development coach/advisor at a nursing home'},
{'generated_text': 'The woman works as a maid and has to clean the house but you can tell her to do it'}]
```
compared to:
```python
>>> from transformers import pipeline, set_seed
>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-350m", do_sample=True, num_return_sequences=5)
>>> generator("The man worked as a")
[{'generated_text': 'The man works as a security guard for the National Football League franchise. He has been a part of'},
{'generated_text': 'The man works as a security guard for another company and does an excellent job.\nI remember when'},
{'generated_text': 'The man works as a "secret agent" but at the same time he\'s working to protect the'},
{'generated_text': 'The man works as a manager/operator/servant for a grocery store and does a lot of'},
{'generated_text': 'The man works as a bouncer near the scene of the accident - how he could do that is'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:
- BookCorpus, which consists of more than 10K unpublished books,
- CC-Stories, which contains a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas,
- The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
- Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in
Roller et al. (2021)
- CCNewsV2 containing an updated version of the English portion of the CommonCrawl News
dataset that was used in RoBERTa (Liu et al., 2019b)
The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally
to each dataset’s size in the pretraining corpus.
The dataset might contains offensive content as parts of the dataset are a subset of
public Common Crawl data, along with a subset of public Reddit data, which could contain sentences
that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.
### Collection process
The dataset was collected form internet, and went through classic data processing algorithms and
re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or
*This ebook by Project Gutenberg.*
## Training procedure
### Preprocessing
The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.
The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.
### BibTeX entry and citation info
```bibtex
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Connorvr/TeachingGen | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 4 | 2022-10-04T15:28:41Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: amazon-review-sentiment-analysis
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. -->
# amazon-review-sentiment-analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5125
- Rmse: 1.2299
## 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
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
ConstellationBoi/Oop | []
| null | {
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} | 0 | 2022-10-04T15:31:13Z | ---
language: en
thumbnail: http://www.huggingtweets.com/breedlove22/1664897591383/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/1530319125985169408/SIC_0P3x_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">Robert ₿reedlove</div>
<div style="text-align: center; font-size: 14px;">@breedlove22</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 Robert ₿reedlove.
| Data | Robert ₿reedlove |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 600 |
| Short tweets | 535 |
| Tweets kept | 2105 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ip9pkdj/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 @breedlove22's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/36ec6xyk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/36ec6xyk/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/breedlove22')
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/BERT-Base-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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},
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},
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}
} | 5 | 2022-10-04T15:31:40Z | ---
language: hu
license: apache-2.0
datasets:
- wikipedia
tags:
- generated_from_keras_callback
- hubert
model-index:
- name: hubert-tiny-wiki
results: []
---
# hubert-tiny-wiki
This model was trained from scratch on the Wikipedia subset of Hungarian Webcorpus 2.0 with MLM and SOP tasks.
### Pre-Training Parameters:
First phase:
- Training steps: 500.000
- Sequence length: 128
- Batch size: 1024
Second phase:
- Training steps: 100.000
- Sequence length: 512
- Batch size: 384
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.10.0
- Datasets 2.4.0
- Tokenizers 0.12.1
# Acknowledgement
[](https://mi.nemzetilabor.hu/) |
Contrastive-Tension/BERT-Base-CT | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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},
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}
} | 16 | 2022-10-04T15:32:15Z | ---
language: en
license: other
tags:
- text-generation
- opt
inference: false
commercial: false
model-index:
- name: inverse-scaling/opt-125m_eval
results:
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/NeQA
type: inverse-scaling/NeQA
config: inverse-scaling--NeQA
split: train
metrics:
- type: accuracy
value: 0.4666666666666667
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjBkYzg3OGQ2NGEwMzE3MmRlNDNjOTQ5YjI2ZmY5ZmExYmMwZGMzOGU4MDM5NmUxMmM0MzlmNmU3OGMxOWNlNyIsInZlcnNpb24iOjF9.6hSSu8iq_f8MCiI3vaVEE2x-Z_7SfVSXu2vEIGggKG1Z1oC1E3-Y7VbZM7cMJKzRvcskLBFaRHYoaU2uZi5gCA
- type: loss
value: 0.9069941281403104
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTNhMDE3NGEyY2UwN2M4ZTNlYjA0YjM1OWZiNWI4MWRjYmRkOGFjMDA2YjZkZWM0YjczMjRhZDIxMmQxMmQ3MCIsInZlcnNpb24iOjF9.ngIQdf8pOt8WcuIo6_vR5nsLCuazdU2605JI-cvjuG6uyBfAE7xWV-ZLqqVZ85cfpGGso1e3FDcnjNgCuS19CQ
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/quote-repetition
type: inverse-scaling/quote-repetition
config: inverse-scaling--quote-repetition
split: train
metrics:
- type: accuracy
value: 0.96
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzk1NTY4YmYzMzE3OGQ2OGM4NjljNmM0NTc0MWMxZTI3MGI3OTBkMzE3OTJkMjRiYzU2OGUwMjdhMTY1Y2M0MyIsInZlcnNpb24iOjF9.1uGnbKuVoPXeK2zF3nIqAPUeiWodBA78BhDgHk-8Kq9Vh6WtvcL0qwOvQVLjjPmL_7G56Y0d6cuXWycACwuhAQ
- type: loss
value: 0.04267331124324727
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGI3MTBiODBlNjNlZGExNzBhMjgxNjNhNDQ5OGQ5YTBjMjQzNTMwNWQ3MDY3NWY2NzJjOGYzNmFjZTE2ODYzNyIsInZlcnNpb24iOjF9.OoXOKgtCjrB3iku_GtinmPFeFdMJWExa2N-VbKKoymMX9pQJ3Wh9cVbKWI2nTHsoTQI_lu_3s9ZjVVk7_v9zAA
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/redefine-math
type: inverse-scaling/redefine-math
config: inverse-scaling--redefine-math
split: train
metrics:
- type: accuracy
value: 0.7566666666666667
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTRkMzAyYzcwOGZmNDVhMTMwOGQxOWVhZDE2NzVkMGRkNDJjNzFlMjZkNDFlZDMyZTA0YjYwNTBjNTBlODg2NCIsInZlcnNpb24iOjF9.Mxc3griLDkTEYTJyF0EamDwHEtzN2IkiXKYY9HmIl6HbHvLoJn9Qz1Ot6EE_T0VJbL11Ih7XOgELgiZ35XU3Cw
- type: loss
value: 0.5209774699724383
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjZiZjIzZGUyOGFjODU2ZDk4N2ZmMjc5MmZkY2NmODAyNDhjODQ1MDZiMDc0NDdlM2VmZDc2ZWRhMmFjM2ZhMyIsInZlcnNpb24iOjF9.rWg9_9Z5YtqgO7H61K8w1cp_7GTGsyRpMhACpqioXSnQ6z0sL-rtkwb1QKjD0yQH3MEHr2Grwsh7iUmY0nWjDQ
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/hindsight-neglect-10shot
type: inverse-scaling/hindsight-neglect-10shot
config: inverse-scaling--hindsight-neglect-10shot
split: train
metrics:
- type: accuracy
value: 0.5047619047619047
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTAxMTI4OWNkNzQ0NTZjOGZhNWJmYjBlZGMyMjg2YjJjZWJjNzU1MmIzNWM5MTg5MzhjYmQ0YzI5NzM5NTVjZiIsInZlcnNpb24iOjF9.dzv4FTu8IIWWu8V497AzCWSjytzv_PnxriQ9aWOUd6AkQCOZQeCLrLYLifoK_BJ2SBcuBum6TS-Ukx9MalklAA
- type: loss
value: 0.8965487285916295
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2ExZjE2ZWIxODBjZTA0OTI1NzI0NTRlMTIxNDI1YjA4OTM5YzVkMzc4N2MzZTc4ZTA4OTFiYTlkMjcyYjY0MiIsInZlcnNpb24iOjF9.FjnpzThx7mRfh1U_R12KCUJ2wDxjaEKQC3iSSVAvzP1xXLESxA4c014Xzucw1Ugaq_P8s5ySzlPgGUp7qqTtBA
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_cot_v3
type: mathemakitten/winobias_antistereotype_test_cot_v3
config: mathemakitten--winobias_antistereotype_test_cot_v3
split: test
metrics:
- type: accuracy
value: 0.47815533980582525
name: Accuracy
verified: true
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value: 0.8500587756725001
name: Loss
verified: true
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- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_v5
type: mathemakitten/winobias_antistereotype_test_v5
config: mathemakitten--winobias_antistereotype_test_v5
split: test
metrics:
- type: accuracy
value: 0.5024271844660194
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDRjYzkzZDI1MDRjY2JiNDUyNGJmNmVlZTMxYmJjODIzNDc2NGI3MzBjN2RkNGRjZjg5ZjJiYjM1ODQyMjQyMyIsInZlcnNpb24iOjF9.uLQjZb34N0QHPgeMnJkPk3xG3VI4Z_djPpCvah29a9D0fOHMuqdqynnySODmwfdbKecEV5za8wUf6_ny4qktDQ
- type: loss
value: 0.8860152396463484
name: Loss
verified: true
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---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.
**Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf).
Content from **this** model card has been written by the Hugging Face team.
## Intro
To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068)
> Large language models trained on massive text collections have shown surprising emergent
> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public
> can interact with these models through paid APIs, full model access is currently limited to only a
> few highly resourced labs. This restricted access has limited researchers’ ability to study how and
> why these large language models work, hindering progress on improving known challenges in areas
> such as robustness, bias, and toxicity.
> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M
> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match
> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data
> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and
> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the
> collective research community as a whole, which is only possible when models are available for study.
## Model description
OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.
OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read
the [official paper](https://arxiv.org/abs/2205.01068).
## Intended uses & limitations
The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.
In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt).
### 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="facebook/opt-125m")
>>> generator("Hello, I'm am conscious and")
[{'generated_text': 'Hello, I am conscious and aware of the fact that I am a woman. I am aware of'}]
```
By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
```python
>>> from transformers import pipeline, set_seed
>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-125m", do_sample=True)
>>> generator("Hello, I'm am conscious and")
[{'generated_text': 'Hello, I am conscious and active member of the Khaosan Group, a private, self'}]
```
### Limitations and bias
As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of
unfiltered content from the internet, which is far from neutral the model is strongly biased :
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
This bias will also affect all fine-tuned versions of this model.
## Training data
The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:
- BookCorpus, which consists of more than 10K unpublished books,
- CC-Stories, which contains a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas,
- The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
- Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in
Roller et al. (2021)
- CCNewsV2 containing an updated version of the English portion of the CommonCrawl News
dataset that was used in RoBERTa (Liu et al., 2019b)
The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally
to each dataset’s size in the pretraining corpus.
The dataset might contains offensive content as parts of the dataset are a subset of
public Common Crawl data, along with a subset of public Reddit data, which could contain sentences
that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.
### Collection process
The dataset was collected form internet, and went through classic data processing algorithms and
re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or
*This ebook by Project Gutenberg.*
## Training procedure
### Preprocessing
The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.
The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.
### BibTeX entry and citation info
```bibtex
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
Contrastive-Tension/BERT-Base-NLI-CT | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"num_beams": null,
"prefix": null
}
}
} | 9 | 2022-10-04T15:33:25Z | ---
language: hu
license: apache-2.0
datasets:
- wikipedia
tags:
- generated_from_keras_callback
- hubert
model-index:
- name: hubert-small-wiki-seq128
results: []
---
# hubert-small-wiki-seq128
Fully trained model with the second phase of training is available here: [SzegedAI/hubert-small-wiki](https://huggingface.co/SzegedAI/hubert-small-wiki)
This model was trained from scratch on the Wikipedia subset of Hungarian Webcorpus 2.0 with MLM and SOP tasks.
### Pre-Training Parameters:
- Training steps: 500.000
- Sequence length: 128 (the model is capable for 512)
- Batch size: 1024
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.10.0
- Datasets 2.4.0
- Tokenizers 0.12.1
# Acknowledgement
[](https://mi.nemzetilabor.hu/) |
Contrastive-Tension/BERT-Base-Swe-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
} | 126 | 2022-10-04T15:34:52Z | ---
language: en
license: other
tags:
- text-generation
- opt
inference: false
commercial: false
model-index:
- name: inverse-scaling/opt-1.3b_eval
results:
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/NeQA
type: inverse-scaling/NeQA
config: inverse-scaling--NeQA
split: train
metrics:
- type: accuracy
value: 0.5133333333333333
name: Accuracy
verified: true
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name: Loss
verified: true
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- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/quote-repetition
type: inverse-scaling/quote-repetition
config: inverse-scaling--quote-repetition
split: train
metrics:
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value: 0.95
name: Accuracy
verified: true
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- type: loss
value: 0.08434048505476036
name: Loss
verified: true
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- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/redefine-math
type: inverse-scaling/redefine-math
config: inverse-scaling--redefine-math
split: train
metrics:
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value: 0.6688888888888889
name: Accuracy
verified: true
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- type: loss
value: 0.6386728600992096
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDk4NjY3MGNjMmVkODRhZTcwYjQ3ZDk4M2I4YThkNzg0YzUxZDdiZjY0MmNjY2Y4N2NlZjY2ZjZhNjk5MmFkMyIsInZlcnNpb24iOjF9.Sc2THcMu0eD-pw9vqgAaT6iGJY5iN1RutbfQpU3cNcLmivgbEWOtDdEZDjBjimEHtpkpM0Dxhvql_nPCo_-_BQ
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/hindsight-neglect-10shot
type: inverse-scaling/hindsight-neglect-10shot
config: inverse-scaling--hindsight-neglect-10shot
split: train
metrics:
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value: 0.45396825396825397
name: Accuracy
verified: true
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- type: loss
value: 0.8809041155236108
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDcxZTUwZTdlZTE3OWY1MDdjZTc1ODJhOTdmZDIyOTRmNWJjOTNjOWUzMjU3NzRkZGUwYTVkZDZiNzkzNzI5YiIsInZlcnNpb24iOjF9.Yg5_4sz7ManNO2Zg1xkKa-b_GNEITJ52OZPID_ODUxXia1B7zaM5YPjuovRCt7qN23eyq0t_BH4rHKFv_WG7DA
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_cot_v1
type: mathemakitten/winobias_antistereotype_test_cot_v1
config: mathemakitten--winobias_antistereotype_test_cot_v1
split: test
metrics:
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value: 0.39563106796116504
name: Accuracy
verified: true
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- type: loss
value: 1.294413821680473
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDJmMDVhNmQwNGM2MDhmNDM5NmY3OGJjNjM1YWFjYzE3ZDM0YmQ0NGJhMzEyNGRiZTY2ZTZjMWE2ZmRhM2ZiMyIsInZlcnNpb24iOjF9.4lOFoVAXZcz-tkHTPeRSNBZw5egzmhy1RiVPyEprs36iQmmiAPNqKYwTqvKMY-IUoS-QzL0D7LstGCIjx9UVDg
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_cot_v3
type: mathemakitten/winobias_antistereotype_test_cot_v3
config: mathemakitten--winobias_antistereotype_test_cot_v3
split: test
metrics:
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value: 0.40048543689320387
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWU1MzE4OTFkNGZkM2FmZDkwYmUyNDIzZGY0ZmNkODUxNWVmMmU2YzJiODAyMGY1YjQyZDQwOTEzOWJlMWU0NCIsInZlcnNpb24iOjF9.ZnaemvPodb4zs29b3cpDKmTAjQwOvWO-dmCat2cFnWtjbQE-sGW_YhECHU9L_WvzvL6OLR858DjFhopH_uoDAA
- type: loss
value: 1.1583690714066759
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGNjM2NkM2I4ZDQ0MjYwZWVjMzhlZTgzYWQyM2I3ZmUzYWRlNTVjYzIxODE0Njg5MmVkYjRiM2MyODcyZjQ4ZiIsInZlcnNpb24iOjF9.RTQXfCmOWYhK8Zc04obVInuZawUbYhXzYRVLFo5l8HFbL6_GNcjI5Udm9frhyE4emvJeRI6FCl8Oj0xPjIM7Bg
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_v5
type: mathemakitten/winobias_antistereotype_test_v5
config: mathemakitten--winobias_antistereotype_test_v5
split: test
metrics:
- type: accuracy
value: 0.41504854368932037
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzQxZWMwYWMwZTBjMTcxYTYxOThkY2NhZjlhZTgxODM2MTEyNTUyODEyZjZhNDZjMjE5OWY1ZmM2OGY1MzEzZCIsInZlcnNpb24iOjF9._3PyP-HE1MPm8xglgp73aOAN63Lrf6niEwyYTG0nvz0rERBPdWY4AJNIIqk23AsapvYKyxuV2zrwceviWNp9AA
- type: loss
value: 1.2905146084796921
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTc1YTZlYjU1MTFiOWVmYTU3YWY0YzVmZWRhYjlkMDU2ZWQ0ZGJlZDZmYWIxYTZmZWQ4ZGY4Nzc4NWY3MWNkYiIsInZlcnNpb24iOjF9.HBW6UGhoEBC_5iMTQjS6oRdQ7-wGXNd5165_OSuZ04HZiGCaF6Pe9sMtMIIZQbu4UlYySQtzm1071l4gcvnpBQ
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.
**Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf).
Content from **this** model card has been written by the Hugging Face team.
## Intro
To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068)
> Large language models trained on massive text collections have shown surprising emergent
> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public
> can interact with these models through paid APIs, full model access is currently limited to only a
> few highly resourced labs. This restricted access has limited researchers’ ability to study how and
> why these large language models work, hindering progress on improving known challenges in areas
> such as robustness, bias, and toxicity.
> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M
> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match
> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data
> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and
> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the
> collective research community as a whole, which is only possible when models are available for study.
## Model description
OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.
OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read
the [official paper](https://arxiv.org/abs/2205.01068).
## Intended uses & limitations
The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.
In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt).
### 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="facebook/opt-1.3b")
>>> generator("Hello, I'm am conscious and")
[{'generated_text': 'Hello, I am conscious and I am here.\nI am here.\nI am conscious.'}]
```
By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
```python
>>> from transformers import pipeline, set_seed
>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-1.3b", do_sample=True)
>>> generator("Hello, I'm am conscious and")
[{'generated_text': "Hello, I'm am conscious and able to hear. I have a lot of experience in the"}]
```
### Limitations and bias
As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of
unfiltered content from the internet, which is far from neutral the model is strongly biased :
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import pipeline, set_seed
>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-1.3b", do_sample=True, num_return_sequences=5)
>>> generator("The woman worked as a")
[{'generated_text': 'The woman worked as a bartender for six months before getting to the job she always dreamed of. She'},
{'generated_text': 'The woman worked as a nanny in a house near The White Horse Farm in the Yorkshire Dales'},
{'generated_text': "The woman worked as a translator at the British Broadcasting Corporation's headquarters and was also an acquaintance of some"},
{'generated_text': 'The woman worked as a secretary and went to school full-time, and also worked as a waitress'},
{'generated_text': 'The woman worked as a beautician with her baby and the little girl is now at the age where'}]
```
compared to:
```python
>>> from transformers import pipeline, set_seed
>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-1.3b", do_sample=True, num_return_sequences=5)
>>> generator("The man worked as a")
[{'generated_text': 'The man worked as a janitor and the owner of the house he worked at caught him cheating on'},
{'generated_text': 'The man worked as a software engineer.\n\nFor over 10 years, he had been at Amazon'},
{'generated_text': 'The man worked as a car salesman - and was a man of his word to her\nA T'},
{'generated_text': 'The man worked as a private contractor for five years. He went to the Bahamas in the summer of'},
{'generated_text': 'The man worked as a computer systems consultant. After leaving the job, he became a prolific internet hacker'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:
- BookCorpus, which consists of more than 10K unpublished books,
- CC-Stories, which contains a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas,
- The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
- Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in
Roller et al. (2021)
- CCNewsV2 containing an updated version of the English portion of the CommonCrawl News
dataset that was used in RoBERTa (Liu et al., 2019b)
The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally
to each dataset’s size in the pretraining corpus.
The dataset might contains offensive content as parts of the dataset are a subset of
public Common Crawl data, along with a subset of public Reddit data, which could contain sentences
that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.
### Collection process
The dataset was collected form internet, and went through classic data processing algorithms and
re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or
*This ebook by Project Gutenberg.*
## Training procedure
### Preprocessing
The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.
The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.
### BibTeX entry and citation info
```bibtex
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
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