modelId
stringlengths 4
81
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sequence | pipeline_tag
stringclasses 17
values | config
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int64 0
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timestamp[ns, tz=UTC] | card
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Davlan/bert-base-multilingual-cased-finetuned-swahili | [
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"tf",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 67 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: s288cExpressionPrediction_k4
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. -->
# s288cExpressionPrediction_k4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 2e-05
- train_batch_size: 32
- 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
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Davlan/distilbert-base-multilingual-cased-ner-hrl | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] | token-classification | {
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} | 123,856 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3783
- Wer: 0.3036
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.0054 | 3.67 | 400 | 0.7096 | 0.6999 |
| 0.4061 | 7.34 | 800 | 0.4152 | 0.4637 |
| 0.1797 | 11.01 | 1200 | 0.4008 | 0.4164 |
| 0.1201 | 14.68 | 1600 | 0.4275 | 0.4152 |
| 0.0937 | 18.35 | 2000 | 0.4297 | 0.3978 |
| 0.074 | 22.02 | 2400 | 0.3670 | 0.3618 |
| 0.0602 | 25.69 | 2800 | 0.3875 | 0.3129 |
| 0.0472 | 29.36 | 3200 | 0.3783 | 0.3036 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Davlan/mt5-small-pcm-en | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 9 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# rare-puppers
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
#### corgi
#### samoyed
#### shiba inu
 |
Declan/NewYorkTimes_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
"model_type": "bert",
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} | 7 | null | ---
language:
- en
thumbnail: "url to a thumbnail used in social sharing"
license: cc
datasets:
- MIMIC-III
widget:
- text: "This report discusses the diagnosis of lung cancer in a female patient who has never smoked."
---
## Model information:
This model is the [roberta-base](https://huggingface.co/roberta-base) model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiology report texts into two classes. Labels of [0,1] were assigned to radiology reports in MIMIC-III that were linked to an ICD9 diagnosis code for lung cancer = 1 and a random sample of reports which were not linked to any type of cancer diagnosis code at all = 0.
## Intended uses:
This model is intended to be used to classify texts to identify the presence of lung cancer. The model will predict lables of [0,1].
## Limitations:
Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before use -
- [MIMIC-III](https://www.nature.com/articles/sdata201635.pdf)
- [roberta-base](https://huggingface.co/roberta-base)
## How to use:
Load the model from the library using the following checkpoints:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/roberta-base-ft-m3-lc")
model = AutoModel.from_pretrained("sarahmiller137/roberta-base-ft-m3-lc")
```
|
Denver/distilbert-base-uncased-finetuned-squad | [] | null | {
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlnet-base-cased-finetuned-hotpot_qa
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. -->
# xlnet-base-cased-finetuned-hotpot_qa
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9574
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.027 | 1.0 | 923 | 1.0340 |
| 0.8758 | 2.0 | 1846 | 0.9574 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"AlbertForMaskedLM"
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} | 26,792 | 2022-07-02T09:42:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4049
- Wer: 0.3556
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.7319 | 1.0 | 500 | 1.3558 | 0.8890 |
| 0.7826 | 2.01 | 1000 | 0.5655 | 0.5398 |
| 0.4157 | 3.01 | 1500 | 0.4692 | 0.4682 |
| 0.2722 | 4.02 | 2000 | 0.4285 | 0.4193 |
| 0.2094 | 5.02 | 2500 | 0.4170 | 0.3949 |
| 0.1682 | 6.02 | 3000 | 0.3895 | 0.3751 |
| 0.1295 | 7.03 | 3500 | 0.3943 | 0.3628 |
| 0.1064 | 8.03 | 4000 | 0.4198 | 0.3648 |
| 0.0869 | 9.04 | 4500 | 0.4049 | 0.3556 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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} | 341 | 2022-07-02T09:46:10Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1638
- Accuracy: 0.975
## Model description
The model Urdu audio and classify in following categories
* Angry
* Happy
* Neutral
* Sad
## Training and evaluation data
The dataset is available at
https://www.kaggle.com/datasets/kingabzpro/urdu-emotion-dataset
## Training procedure
Training code is available at
https://www.kaggle.com/code/chtalhaanwar/urdu-emotions-hf
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3838 | 1.0 | 10 | 1.3907 | 0.225 |
| 1.3732 | 2.0 | 20 | 1.3872 | 0.2125 |
| 1.3354 | 3.0 | 30 | 1.3116 | 0.6625 |
| 1.2689 | 4.0 | 40 | 1.1820 | 0.6375 |
| 1.1179 | 5.0 | 50 | 1.0075 | 0.7 |
| 0.9962 | 6.0 | 60 | 0.8707 | 0.7125 |
| 0.8842 | 7.0 | 70 | 0.7485 | 0.7625 |
| 0.786 | 8.0 | 80 | 0.6326 | 0.8 |
| 0.6757 | 9.0 | 90 | 0.5995 | 0.8 |
| 0.6104 | 10.0 | 100 | 0.4835 | 0.825 |
| 0.5821 | 11.0 | 110 | 0.3886 | 0.9 |
| 0.4721 | 12.0 | 120 | 0.3935 | 0.8625 |
| 0.3976 | 13.0 | 130 | 0.3020 | 0.925 |
| 0.4483 | 14.0 | 140 | 0.3171 | 0.9 |
| 0.2665 | 15.0 | 150 | 0.3016 | 0.9125 |
| 0.2119 | 16.0 | 160 | 0.2722 | 0.925 |
| 0.3376 | 17.0 | 170 | 0.3163 | 0.8875 |
| 0.1518 | 18.0 | 180 | 0.2681 | 0.9125 |
| 0.1559 | 19.0 | 190 | 0.3074 | 0.925 |
| 0.1031 | 20.0 | 200 | 0.3526 | 0.8875 |
| 0.1557 | 21.0 | 210 | 0.2254 | 0.9375 |
| 0.0846 | 22.0 | 220 | 0.2410 | 0.9375 |
| 0.0733 | 23.0 | 230 | 0.2369 | 0.925 |
| 0.0964 | 24.0 | 240 | 0.2273 | 0.9375 |
| 0.0574 | 25.0 | 250 | 0.2066 | 0.95 |
| 0.1113 | 26.0 | 260 | 0.2941 | 0.9125 |
| 0.1313 | 27.0 | 270 | 0.2715 | 0.925 |
| 0.0851 | 28.0 | 280 | 0.1725 | 0.9625 |
| 0.0402 | 29.0 | 290 | 0.2221 | 0.95 |
| 0.1075 | 30.0 | 300 | 0.2199 | 0.9625 |
| 0.0418 | 31.0 | 310 | 0.1699 | 0.95 |
| 0.1869 | 32.0 | 320 | 0.2287 | 0.9625 |
| 0.0637 | 33.0 | 330 | 0.3230 | 0.9125 |
| 0.0483 | 34.0 | 340 | 0.1602 | 0.975 |
| 0.0891 | 35.0 | 350 | 0.1615 | 0.975 |
| 0.0359 | 36.0 | 360 | 0.1571 | 0.975 |
| 0.1006 | 37.0 | 370 | 0.1809 | 0.9625 |
| 0.0417 | 38.0 | 380 | 0.1923 | 0.9625 |
| 0.0346 | 39.0 | 390 | 0.2035 | 0.9625 |
| 0.0417 | 40.0 | 400 | 0.1737 | 0.9625 |
| 0.0396 | 41.0 | 410 | 0.1833 | 0.9625 |
| 0.0202 | 42.0 | 420 | 0.1946 | 0.9625 |
| 0.0137 | 43.0 | 430 | 0.1785 | 0.9625 |
| 0.0214 | 44.0 | 440 | 0.1841 | 0.9625 |
| 0.0304 | 45.0 | 450 | 0.1690 | 0.9625 |
| 0.0199 | 46.0 | 460 | 0.1646 | 0.975 |
| 0.0122 | 47.0 | 470 | 0.1622 | 0.975 |
| 0.0324 | 48.0 | 480 | 0.1615 | 0.975 |
| 0.0269 | 49.0 | 490 | 0.1625 | 0.975 |
| 0.0245 | 50.0 | 500 | 0.1638 | 0.975 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
albert-xlarge-v2 | [
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"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
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} | 2,973 | 2022-07-02T10:12:27Z | ---
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/1388858833582297095/5_Fg641d_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">CrimseyVT~</div>
<div style="text-align: center; font-size: 14px;">@crimseyvt</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 CrimseyVT~.
| Data | CrimseyVT~ |
| --- | --- |
| Tweets downloaded | 1417 |
| Retweets | 195 |
| Short tweets | 182 |
| Tweets kept | 1040 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vwlwiq1/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 @crimseyvt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x7shpw89) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x7shpw89/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/crimseyvt')
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)
|
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"AlbertForMaskedLM"
],
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} | 42,640 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-qa-en
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-qa-en
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"BertForMaskedLM"
],
"model_type": "bert",
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}
} | 11,644 | 2022-07-02T10:18:49Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="a-doering/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
} | 8,621,271 | 2022-07-02T10:28:13Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="a-doering/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"BertForMaskedLM"
],
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}
} | 68,305 | 2022-07-02T10:39:07Z | ---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- gtsrb
metrics:
- accuracy
model-index:
- name: gtsrb-model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: bazyl/GTSRB
type: gtsrb
args: gtsrb
metrics:
- name: Accuracy
type: accuracy
value: 0.9993199591975519
---
<!-- 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. -->
# gtsrb-model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the bazyl/GTSRB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0034
- Accuracy: 0.9993
## Model description
The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties:
- Single-image, multi-class classification problem
- More than 40 classes
- More than 50,000 images in total
- Large, lifelike database
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2593 | 1.0 | 4166 | 0.1585 | 0.9697 |
| 0.2659 | 2.0 | 8332 | 0.0472 | 0.9900 |
| 0.2825 | 3.0 | 12498 | 0.0155 | 0.9971 |
| 0.0953 | 4.0 | 16664 | 0.0113 | 0.9983 |
| 0.1277 | 5.0 | 20830 | 0.0076 | 0.9985 |
| 0.0816 | 6.0 | 24996 | 0.0047 | 0.9988 |
| 0.0382 | 7.0 | 29162 | 0.0041 | 0.9990 |
| 0.0983 | 8.0 | 33328 | 0.0059 | 0.9990 |
| 0.1746 | 9.0 | 37494 | 0.0034 | 0.9993 |
| 0.1153 | 10.0 | 41660 | 0.0038 | 0.9990 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 8,214 | 2022-07-02T10:48:53Z | ---
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: 0.8627004891366169
---
<!-- 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: 0.1363
- F1: 0.8627
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2539 | 1.0 | 525 | 0.1697 | 0.8179 |
| 0.1317 | 2.0 | 1050 | 0.1327 | 0.8516 |
| 0.0819 | 3.0 | 1575 | 0.1363 | 0.8627 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
bert-large-cased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"BertForMaskedLM"
],
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}
} | 2,316 | 2022-07-02T11:15:56Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1644
- F1: 0.8617
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2891 | 1.0 | 715 | 0.1780 | 0.8288 |
| 0.1471 | 2.0 | 1430 | 0.1627 | 0.8509 |
| 0.0947 | 3.0 | 2145 | 0.1644 | 0.8617 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
bert-large-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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],
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} | 388,769 | 2022-07-02T11:22:47Z | ---
language:
- "fr"
tags:
- t5
- french
- punctuation
license: apache-2.0
datasets:
- orange_sum
- mlsum
---
# 🚀 Text Punctuator Based on Transformers model T5.
T5 model fine-tuned for punctuation restoration.
Model currently supports only French Language. More language supports will be added later using mT5.
Train Datasets :
Model trained using 2 french datasets (around 500k records):
- [orange_sum](https://huggingface.co/datasets/orange_sum)
- [mlsum](https://huggingface.co/datasets/mlsum) (only french text)
More info will be added later.
## 🚀 Usage
**TextPunctuator as a wrapper of the model.**
1. Install the package.
```bash
pip install TextPunctuator
```
2. Simple example
```python
from Punctuator import TextPunctuator
punctuator = TextPunctuator(use_gpu=False)
# text input
text = "Sur la base de ces échanges Blake Lemoine a donc jugé que le système avait atteint \
un niveau de conscience lui permettant d'être sensible Ce dernier a ensuite envoyé \
par email un rapport sur la sensibilité supposée de LaMDA à deux cents employés de \
Google Très vite les dirigeants de l’entreprise ont rejeté les allégations"
text_punctuated = punctuator.punctuate(text, lang='fr')
text_punctuated
# output :
""" Sur la base de ces échanges, Blake Lemoine a donc jugé que le système avait atteint un niveau de
conscience lui permettant d’être sensible. Ce dernier a ensuite envoyé par email un rapport sur
la sensibilité supposée de LaMDA à deux cents employés de Google. Très vite, les dirigeants de
l’entreprise ont rejeté les allégations. """
```
## ☕ Contact
Contact [Zakarya ROUZKI ](mailto:[email protected]) or at [Linkedin](https://linkedin.com/in/rouzki).
|
ctrl | [
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
"has_space"
] | null | {
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} | 17,007 | 2022-07-02T12:12:28Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-sol
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-sol
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3922
- Wer: 0.2862
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6222 | 6.85 | 500 | 1.5843 | 0.9627 |
| 0.509 | 13.7 | 1000 | 0.4149 | 0.3417 |
| 0.1221 | 20.55 | 1500 | 0.3692 | 0.2992 |
| 0.0618 | 27.4 | 2000 | 0.3922 | 0.2862 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.12.1
|
distilbert-base-cased | [
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
] | null | {
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} | 574,859 | 2022-07-02T12:27:50Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: opencampus_age-detection
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5892857313156128
---
# opencampus_age-detection
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
#### child portrait face
#### generation x portrait face
#### millennials portrait face
#### pensioner portrait face
#### teenager portrait face
 |
xlm-roberta-large-finetuned-conll02-dutch | [
"pytorch",
"rust",
"xlm-roberta",
"fill-mask",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:1911.02116",
"arxiv:1910.09700",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"XLMRobertaForMaskedLM"
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} | 802 | 2022-07-02T19:08:51Z | ---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-tweetner7-all
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweetner7
type: tner/tweetner7
args: tner/tweetner7
metrics:
- name: F1 (test_2021)
type: f1
value: 0.6574551220340903
- name: Precision (test_2021)
type: precision
value: 0.644212629008989
- name: Recall (test_2021)
type: recall
value: 0.6712534690101758
- name: Macro F1 (test_2021)
type: f1_macro
value: 0.6124665667529737
- name: Macro Precision (test_2021)
type: precision_macro
value: 0.6005167968535563
- name: Macro Recall (test_2021)
type: recall_macro
value: 0.625251837701222
- name: Entity Span F1 (test_2021)
type: f1_entity_span
value: 0.7881979839166384
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7722783264898457
- name: Entity Span Recall (test_2021)
type: recall_entity_span
value: 0.804787787672025
- name: F1 (test_2020)
type: f1
value: 0.6628787878787878
- name: Precision (test_2020)
type: precision
value: 0.6924816280384398
- name: Recall (test_2020)
type: recall
value: 0.6357031655422937
- name: Macro F1 (test_2020)
type: f1_macro
value: 0.6297223287745568
- name: Macro Precision (test_2020)
type: precision_macro
value: 0.6618492079232416
- name: Macro Recall (test_2020)
type: recall_macro
value: 0.601311568050436
- name: Entity Span F1 (test_2020)
type: f1_entity_span
value: 0.7642760487144791
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7986425339366516
- name: Entity Span Recall (test_2020)
type: recall_entity_span
value: 0.7327451997924235
pipeline_tag: token-classification
widget:
- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}"
example_title: "NER Example 1"
---
# tner/roberta-large-tweetner7-all
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.6574551220340903
- Precision (micro): 0.644212629008989
- Recall (micro): 0.6712534690101758
- F1 (macro): 0.6124665667529737
- Precision (macro): 0.6005167968535563
- Recall (macro): 0.625251837701222
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.5392156862745098
- creative_work: 0.4760582928521859
- event: 0.4673321234119782
- group: 0.6139798488664987
- location: 0.6707399864222675
- person: 0.8293212669683258
- product: 0.6906187624750498
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.6484148010152769, 0.6672289519134409]
- 95%: [0.6470100684797441, 0.6689850350992637]
- F1 (macro):
- 90%: [0.6484148010152769, 0.6672289519134409]
- 95%: [0.6470100684797441, 0.6689850350992637]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweetner7-all/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweetner7-all/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip.
```shell
pip install tner
```
[TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are
converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.
```python
import re
from urlextract import URLExtract
from tner import TransformersNER
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/roberta-large-tweetner7-all")
model.predict([text_format])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/tweetner7']
- dataset_split: train_all
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 30
- batch_size: 32
- lr: 1e-05
- random_seed: 0
- gradient_accumulation_steps: 1
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.15
- max_grad_norm: 1
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-tweetner7-all/raw/main/trainer_config.json).
### Reference
If you use the model, please cite T-NER paper and TweetNER7 paper.
- T-NER
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
- TweetNER7
```
@inproceedings{ushio-etal-2022-tweet,
title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
author = "Ushio, Asahi and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco. and
Camacho-Collados, Jose",
booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
month = nov,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
2umm3r/bert-base-uncased-finetuned-cls | [] | null | {
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} | 0 | null | ---
tags:
- token-classification
datasets:
- djagatiya/ner-ontonotes-v5-eng-v4
widget:
- text: "On September 1st George won 1 dollar while watching Game of Thrones."
---
# (NER) ALBERT-base-v2 : conll2012_ontonotesv5-english-v4
This `ALBERT-base-v2` NER model was finetuned on `conll2012_ontonotesv5` version `english-v4` dataset. <br>
Check out [NER-System Repository](https://github.com/djagatiya/NER-System) for more information.
## Evaluation
- Precision: 86.20
- Recall: 86.18
- F1-Score: 86.19
> check out this [eval.log](eval.log) file for evaluation metrics and classification report.
```
precision recall f1-score support
CARDINAL 0.84 0.83 0.83 935
DATE 0.84 0.87 0.86 1602
EVENT 0.61 0.52 0.56 63
FAC 0.54 0.59 0.56 135
GPE 0.95 0.94 0.95 2240
LANGUAGE 0.85 0.50 0.63 22
LAW 0.56 0.57 0.57 40
LOC 0.61 0.65 0.63 179
MONEY 0.85 0.88 0.86 314
NORP 0.88 0.92 0.90 841
ORDINAL 0.78 0.86 0.81 195
ORG 0.84 0.81 0.82 1795
PERCENT 0.88 0.87 0.88 349
PERSON 0.94 0.92 0.93 1988
PRODUCT 0.57 0.53 0.55 76
QUANTITY 0.77 0.81 0.79 105
TIME 0.59 0.66 0.62 212
WORK_OF_ART 0.60 0.52 0.56 166
micro avg 0.86 0.86 0.86 11257
macro avg 0.75 0.74 0.74 11257
weighted avg 0.86 0.86 0.86 11257
``` |
ARTeLab/it5-summarization-mlsum | [
"pytorch",
"t5",
"text2text-generation",
"it",
"dataset:ARTeLab/mlsum-it",
"transformers",
"summarization",
"autotrain_compatible",
"has_space"
] | summarization | {
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"T5ForConditionalGeneration"
],
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} | 16 | 2022-07-03T18:30:52Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: kingabzpro/MLAgents-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Akashpb13/xlsr_kurmanji_kurdish | [
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"kmr",
"ku",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch8-ep10
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch8-ep10
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.4142
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 4.5620 | 0 |
| 3.5677 | 1 |
| 3.4972 | 2 |
| 3.4740 | 3 |
| 3.4562 | 4 |
| 3.4406 | 5 |
| 3.4313 | 6 |
| 3.4272 | 7 |
| 3.4152 | 8 |
| 3.4142 | 9 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Aleksandra/distilbert-base-uncased-finetuned-squad | [] | null | {
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} | 0 | null | ---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- abhishek/autotrain-data-iris-train
- scikit-learn/iris
co2_eq_emissions: 0.0006300767567816624
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 9705273
- CO2 Emissions (in grams): 0.0006300767567816624
## Validation Metrics
- Loss: 0.15987505325856152
- Accuracy: 0.9
- Macro F1: 0.899749373433584
- Micro F1: 0.9
- Weighted F1: 0.8997493734335841
- Macro Precision: 0.9023569023569024
- Micro Precision: 0.9
- Weighted Precision: 0.9023569023569025
- Macro Recall: 0.9
- Micro Recall: 0.9
- Weighted Recall: 0.9
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
``` |
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} | 0 | null | ---
tags:
- spacy
- token-classification
language:
- de
model-index:
- name: de_GERNERMEDpp_GottBERT
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9240268876
- name: NER Recall
type: recall
value: 0.9207165109
- name: NER F Score
type: f_score
value: 0.922368729
---
GermanBERT-based model of the GERNERMED++ German NER model for medical entities.
| Feature | Description |
| --- | --- |
| **Name** | `de_GERNERMEDpp_GottBERT` |
| **Version** | `1.0.0` |
| **spaCy** | `>=3.2.3,<3.3.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [Johann Frei](https://github.com/frankkramer-lab/GERNERMED-pp) |
### Label Scheme
<details>
<summary>View label scheme (6 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `Dosage`, `Drug`, `Duration`, `Form`, `Frequency`, `Strength` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 92.24 |
| `ENTS_P` | 92.40 |
| `ENTS_R` | 92.07 |
| `TRANSFORMER_LOSS` | 353176.15 |
| `NER_LOSS` | 525846.32 | |
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} | 0 | null | ---
tags:
- spacy
- token-classification
language:
- de
model-index:
- name: de_GERNERMEDpp_Slim
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9020724569
- name: NER Recall
type: recall
value: 0.8881619938
- name: NER F Score
type: f_score
value: 0.8950631819
---
Slim model of the GERNERMED++ German NER model for medical entities.
| Feature | Description |
| --- | --- |
| **Name** | `de_GERNERMEDpp_Slim` |
| **Version** | `1.0.0` |
| **spaCy** | `>=3.2.3,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [Johann Frei](https://github.com/frankkramer-lab/GERNERMED-pp) |
### Label Scheme
<details>
<summary>View label scheme (6 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `Dosage`, `Drug`, `Duration`, `Form`, `Frequency`, `Strength` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 89.51 |
| `ENTS_P` | 90.21 |
| `ENTS_R` | 88.82 |
| `TOK2VEC_LOSS` | 129329.99 |
| `NER_LOSS` | 603008.42 | |
Anamika/autonlp-Feedback1-479512837 | [
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"xlm-roberta",
"text-classification",
"unk",
"dataset:Anamika/autonlp-data-Feedback1",
"transformers",
"autonlp",
"co2_eq_emissions"
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} | 34 | null | ---
tags:
- FrozenLake-v1-4x4-slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-slippery
results:
- metrics:
- type: mean_reward
value: 0.16 +/- 0.37
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-slippery
type: FrozenLake-v1-4x4-slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="infinitejoy/q-FrozenLake-v1-4x4-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
AnonymousSub/AR_rule_based_roberta_only_classfn_epochs_1_shard_10 | [
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} | 2 | null | ---
license: afl-3.0
---
Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO |
AnonymousSub/AR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_1 | [
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"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="kws/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
AnonymousSub/AR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_10 | [
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} | 6 | null | ---
license: apache-2.0
tags:
- summarization
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-ftn-multi_news
results:
- task:
name: Sequence-to-sequence Language Modeling
type: summarization
dataset:
name: multi_news
type: multi_news
args: default
metrics:
- name: Rouge1
type: rouge
value: 41.6136
- task:
type: summarization
name: Summarization
dataset:
name: multi_news
type: multi_news
config: default
split: test
metrics:
- name: ROUGE-1
type: rouge
value: 39.6512
verified: true
- name: ROUGE-2
type: rouge
value: 14.333
verified: true
- name: ROUGE-L
type: rouge
value: 21.5797
verified: true
- name: ROUGE-LSUM
type: rouge
value: 35.5793
verified: true
- name: loss
type: loss
value: 5.507579803466797
verified: true
- name: gen_len
type: gen_len
value: 132.1745
verified: true
---
<!-- 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. -->
# distilbart-cnn-12-6-ftn-multi_news
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8143
- Rouge1: 41.6136
- Rouge2: 14.7454
- Rougel: 23.3597
- Rougelsum: 36.1973
- Gen Len: 130.874
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.8821 | 0.89 | 2000 | 3.8143 | 41.6136 | 14.7454 | 23.3597 | 36.1973 | 130.874 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/AR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1 | [
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"roberta",
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-test
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. -->
# recipe-test
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: 2.9583
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3675 | 1.0 | 16 | 3.0009 |
| 3.0062 | 2.0 | 32 | 2.9583 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/AR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10 | [
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} | 2 | 2022-07-06T10:28:02Z | ---
license: apache-2.0
library_name: sklearn
tags:
- tabular-classification
- baseline-trainer
---
## Baseline Model trained on breast_cancernb8gjv4n to apply classification on diagnosis
**Metrics of the best model:**
accuracy 0.978932
average_precision 0.994309
roc_auc 0.995448
recall_macro 0.976607
f1_macro 0.977365
Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64
**See model plot below:**
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concavity_mean Tr...
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concave points_worst True False ... False False
symmetry_worst True False ... False False
fractal_dimension_worst True False ... False False[31 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-12" type="checkbox" ><label for="sk-estimator-id-12" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
id True False ... False False
radius_mean True False ... False False
texture_mean True False ... False False
perimeter_mean True False ... False False
area_mean True False ... False False
smoothness_mean True False ... False False
compactness_mean True False ... False False
concavity_mean Tr...
area_worst True False ... False False
smoothness_worst True False ... False False
compactness_worst True False ... False False
concavity_worst True False ... False False
concave points_worst True False ... False False
symmetry_worst True False ... False False
fractal_dimension_worst True False ... False False[31 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-13" type="checkbox" ><label for="sk-estimator-id-13" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float ... free_string useless
id True False ... False False
radius_mean True False ... False False
texture_mean True False ... False False
perimeter_mean True False ... False False
area_mean True False ... False False
smoothness_mean True False ... False False
compactness_mean True False ... False False
concavity_mean True False ... False False
concave points_me...
texture_worst True False ... False False
perimeter_worst True False ... False False
area_worst True False ... False False
smoothness_worst True False ... False False
compactness_worst True False ... False False
concavity_worst True False ... False False
concave points_worst True False ... False False
symmetry_worst True False ... False False
fractal_dimension_worst True False ... False False[31 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-14" type="checkbox" ><label for="sk-estimator-id-14" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt |
AnonymousSub/AR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1 | [
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"roberta",
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"transformers"
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-rim_one-new
results:
- task:
type: image-classification
name: Image Classification
dataset:
type: rimonedl
name: RIM ONE DL
split: test
metrics:
- type: f1
value: 0.9197860962566845
name: F1
- task:
type: image-classification
name: Image Classification
dataset:
type: rim one
name: RIMONEDL
split: test
metrics:
- type: precision
value: 0.9247311827956989
name: precision
- type: recall
value: 0.9148936170212766
name: Recall
- type: accuracy
value: 0.8972602739726028
name: Accuracy
- type: roc_auc
value: 0.8901391162029461
name: AUC
---
<!-- 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. -->
# beit-base-patch16-224-pt22k-ft22k-rim_one-new
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4550
- Accuracy: 0.8767
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.73 | 2 | 0.2411 | 0.9178 |
| No log | 1.73 | 4 | 0.2182 | 0.8973 |
| No log | 2.73 | 6 | 0.3085 | 0.8973 |
| No log | 3.73 | 8 | 0.2794 | 0.8973 |
| 0.1392 | 4.73 | 10 | 0.2398 | 0.9110 |
| 0.1392 | 5.73 | 12 | 0.2925 | 0.8973 |
| 0.1392 | 6.73 | 14 | 0.2798 | 0.9110 |
| 0.1392 | 7.73 | 16 | 0.2184 | 0.9178 |
| 0.1392 | 8.73 | 18 | 0.3007 | 0.9110 |
| 0.0416 | 9.73 | 20 | 0.3344 | 0.9041 |
| 0.0416 | 10.73 | 22 | 0.3626 | 0.9110 |
| 0.0416 | 11.73 | 24 | 0.4842 | 0.8904 |
| 0.0416 | 12.73 | 26 | 0.3664 | 0.8973 |
| 0.0416 | 13.73 | 28 | 0.3458 | 0.9110 |
| 0.0263 | 14.73 | 30 | 0.2810 | 0.9110 |
| 0.0263 | 15.73 | 32 | 0.4695 | 0.8699 |
| 0.0263 | 16.73 | 34 | 0.3723 | 0.9041 |
| 0.0263 | 17.73 | 36 | 0.3447 | 0.9041 |
| 0.0263 | 18.73 | 38 | 0.3708 | 0.8904 |
| 0.0264 | 19.73 | 40 | 0.4052 | 0.9110 |
| 0.0264 | 20.73 | 42 | 0.4492 | 0.9041 |
| 0.0264 | 21.73 | 44 | 0.4649 | 0.8904 |
| 0.0264 | 22.73 | 46 | 0.4061 | 0.9178 |
| 0.0264 | 23.73 | 48 | 0.4136 | 0.9110 |
| 0.0139 | 24.73 | 50 | 0.4183 | 0.8973 |
| 0.0139 | 25.73 | 52 | 0.4504 | 0.8904 |
| 0.0139 | 26.73 | 54 | 0.4368 | 0.8973 |
| 0.0139 | 27.73 | 56 | 0.4711 | 0.9110 |
| 0.0139 | 28.73 | 58 | 0.3928 | 0.9110 |
| 0.005 | 29.73 | 60 | 0.4550 | 0.8767 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/AR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10 | [
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} | 4 | null | ---
license: afl-3.0
---
Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO |
AnonymousSub/AR_rule_based_roberta_twostagetriplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 6 | 2022-07-06T10:33:57Z | ---
license: apache-2.0
library_name: sklearn
tags:
- tabular-classification
- baseline-trainer
---
## Baseline Model trained on UCI_Credit_Cardyi6q1ptm to apply classification on PAY_0
**Metrics of the best model:**
accuracy 0.715467
recall_macro 0.777916
precision_macro 0.578960
f1_macro 0.596625
Name: DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01), dtype: float64
**See model plot below:**
<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-5" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
LIMIT_BAL False False ... False False
SEX False False ... False False
EDUCATION False False ... False False
MARRIAGE False False ... False False
AGE False False ... False False
PAY_2 False False ... False False
PAY_3 False False ... False False
PAY_4 False False ... False False
PAY_5 False False ......
PAY_AMT1 True False ... False False
PAY_AMT2 True False ... False False
PAY_AMT3 True False ... False False
PAY_AMT4 True False ... False False
PAY_AMT5 True False ... False False
PAY_AMT6 True False ... False False
default.payment.next.month False False ... False False[23 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced',min_impurity_decrease=0.01))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-15" type="checkbox" ><label for="sk-estimator-id-15" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
LIMIT_BAL False False ... False False
SEX False False ... False False
EDUCATION False False ... False False
MARRIAGE False False ... False False
AGE False False ... False False
PAY_2 False False ... False False
PAY_3 False False ... False False
PAY_4 False False ... False False
PAY_5 False False ......
PAY_AMT1 True False ... False False
PAY_AMT2 True False ... False False
PAY_AMT3 True False ... False False
PAY_AMT4 True False ... False False
PAY_AMT5 True False ... False False
PAY_AMT6 True False ... False False
default.payment.next.month False False ... False False[23 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced',min_impurity_decrease=0.01))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-16" type="checkbox" ><label for="sk-estimator-id-16" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float ... free_string useless
LIMIT_BAL False False ... False False
SEX False False ... False False
EDUCATION False False ... False False
MARRIAGE False False ... False False
AGE False False ... False False
PAY_2 False False ... False False
PAY_3 False False ... False False
PAY_4 False False ... False False
PAY_5 False False ... False False
PAY_6 False False ... False Fal...
BILL_AMT3 True False ... False False
BILL_AMT4 True False ... False False
BILL_AMT5 True False ... False False
BILL_AMT6 True False ... False False
PAY_AMT1 True False ... False False
PAY_AMT2 True False ... False False
PAY_AMT3 True False ... False False
PAY_AMT4 True False ... False False
PAY_AMT5 True False ... False False
PAY_AMT6 True False ... False False
default.payment.next.month False False ... False False[23 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-17" type="checkbox" ><label for="sk-estimator-id-17" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt |
AnonymousSub/AR_rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
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"roberta",
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"transformers"
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} | 2 | 2022-07-06T10:49:17Z | Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scrollTo=nYtUtmyDFAqP |
AnonymousSub/AR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 1 | null | ---
tags:
- fastai
- text-generation
language: ml
widget:
- text: "ഓഹരി വിപണി തകരുമ്പോള് നിക്ഷേപം എങ്ങനെ സുരക്ഷിതമാക്കാം"
example_title: "Malayalam Casual Language Model"
datasets:
- rajeshradhakrishnan/malayalam_wiki
---
# Blurr x Casual Machine Learning Model trained on Malayalam (മലയാളം) text. (Working in Progress)
[](https://nbviewer.org/github/rajeshradhakrishnanmvk/kitchen2.0/blob/main/ml/malayalam_blurr_xlm_roberta_base.ipynb)
---
# malayalam-blurr-xlm-roberta-base (base-sized model)
malayalam-blurr-xlm-roberta-base model is pre-trained on [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) using the library [blurr](https://ohmeow.github.io/blurr/) Language Model using fastai x huggingface frameworks.
Ref: [Causal Language Modeling](https://ohmeow.github.io/blurr/text-modeling-language-modeling.html#Causal-language-modeling).
## Usage
```
!pip install -Uqq huggingface_hub["fastai"] ohmeow-blurr
from huggingface_hub import from_pretrained_fastai
learner = from_pretrained_fastai(repo_id)
learner.blurr_generate("ബ്ളൂർ പഠിക്കാൻ വളെരെ എളുപ്പമാണ് എന്തുകൊണ്ട് എന്നാൽ", max_length=50, do_sample=True, top_k=25)
```
## Intended uses & limitations
It's not fine tuned to the state of the art accuracy
## Training and evaluation data
[Wiki 2020 Malayalam Dataset ](https://huggingface.co/datasets/rajeshradhakrishnan/malayalam_wiki)
|
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.3577
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4783
- Rouge1: 28.3577
- Rouge2: 7.759
- Rougel: 22.274
- Rougelsum: 22.2869
- Gen Len: 18.8298
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| 2.7158 | 1.0 | 12753 | 2.4783 | 28.3577 | 7.759 | 22.274 | 22.2869 | 18.8298 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 2 | null | ---
language: is
tag: text2text-generation
pipeline_tag: text2text-generation
widget:
- text: "ék var að borðaði maturinn min"
inference:
parameters:
max_length: 512
license: cc-by-sa-4.0
---
This is a model for correcting spelling and grammar errors in Icelandic text. It is based on the pretrained ByT5 model (https://arxiv.org/abs/2105.13626) and finetuned on Icelandic error correction data along with synthetic error data. The model is trained using the HuggingFace and PyTorch libraries.
The model is trained to correct a single sentence at a time, but may work on longer context.
The model performs well on correcting a variety of common issues in Icelandic text.
This README will be updated soon along with citation reference.
|
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} | 2 | null | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- zhifei/autotrain-data-autotrain-chinese-title-summarization-9
co2_eq_emissions: 1.565396518204961
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1101340178
- CO2 Emissions (in grams): 1.565396518204961
## Validation Metrics
- Loss: 0.00012778821110259742
- Rouge1: 29.2308
- Rouge2: 0.0
- RougeL: 29.2308
- RougeLsum: 29.2308
- Gen Len: 18.4462
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/zhifei/autotrain-autotrain-chinese-title-summarization-9-1101340178
``` |
AnonymousSub/unsup-consert-papers-bert | [
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} | 9 | null | ---
license: apache-2.0
library_name: sklearn
tags:
- tabular-classification
- baseline-trainer
---
## Baseline Model trained on trainii_ac94u to apply classification on label
**Metrics of the best model:**
accuracy 0.361046
recall_macro 0.353192
precision_macro 0.240667
f1_macro 0.278231
Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64
**See model plot below:**
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id True False False ... False False False
text False False False ... False True False[2 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
id True False False ... False False False
text False False False ... False True False[2 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
id True False False ... False False False
text False False False ... False True False[2 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt |
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} | 0 | null | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: En-Nso
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. -->
# En-Nso
This model is a fine-tuned version of [kabelomalapane/en_nso_ukuxhumana_model](https://huggingface.co/kabelomalapane/en_nso_ukuxhumana_model) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9067
- Bleu: 23.5436
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 14 | 3.7614 | 8.0360 |
| No log | 2.0 | 28 | 3.3181 | 20.7201 |
| No log | 3.0 | 42 | 3.1627 | 21.5932 |
| No log | 4.0 | 56 | 3.0935 | 22.0268 |
| No log | 5.0 | 70 | 3.0227 | 21.0859 |
| No log | 6.0 | 84 | 2.9740 | 21.6963 |
| No log | 7.0 | 98 | 2.9419 | 23.2214 |
| No log | 8.0 | 112 | 2.9227 | 24.4649 |
| No log | 9.0 | 126 | 2.9102 | 23.5293 |
| No log | 10.0 | 140 | 2.9067 | 23.5516 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | 2022-07-07T11:42:30Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: TRY
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. -->
# TRY
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4234
- eval_wer: 0.3884
- eval_runtime: 51.9275
- eval_samples_per_second: 32.353
- eval_steps_per_second: 4.044
- epoch: 7.03
- step: 3500
## 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: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
Apisate/DialoGPT-small-jordan | [
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"gpt2",
"text-generation",
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} | 12 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: puppies_classify
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9701492786407471
---
# puppies_classify
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
#### corgi
#### husky
#### pomeranian
 |
Apisate/Discord-Ai-Bot | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 11 | null | ---
license: "cc-by-nc-4.0"
tags:
- vision
- video-classification
---
# VideoMAE (base-sized model, pre-trained only)
VideoMAE model pre-trained on Kinetics-400 for 800 epochs in a self-supervised way. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Tong et al. and first released in [this repository](https://github.com/MCG-NJU/VideoMAE).
Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
VideoMAE is an extension of [Masked Autoencoders (MAE)](https://arxiv.org/abs/2111.06377) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches.
Videos are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds fixed sinus/cosinus position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of videos that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled videos for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire video.
## Intended uses & limitations
You can use the raw model for predicting pixel values for masked patches of a video, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=videomae) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to predict pixel values for randomly masked patches:
```python
from transformers import VideoMAEImageProcessor, VideoMAEForPreTraining
import numpy as np
import torch
num_frames = 16
video = list(np.random.randn(16, 3, 224, 224))
processor = VideoMAEImageProcessor.from_pretrained("MCG-NJU/videomae-base-short")
model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short")
pixel_values = processor(video, return_tensors="pt").pixel_values
num_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2
seq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame
bool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool()
outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
loss = outputs.loss
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/videomae.html#).
## Training data
(to do, feel free to open a PR)
## Training procedure
### Preprocessing
(to do, feel free to open a PR)
### Pretraining
(to do, feel free to open a PR)
## Evaluation results
(to do, feel free to open a PR)
### BibTeX entry and citation info
```bibtex
misc{https://doi.org/10.48550/arxiv.2203.12602,
doi = {10.48550/ARXIV.2203.12602},
url = {https://arxiv.org/abs/2203.12602},
author = {Tong, Zhan and Song, Yibing and Wang, Jue and Wang, Limin},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
``` |
Apoorva/k2t-test | [
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"keytotext",
"k2t",
"Keywords to Sentences",
"autotrain_compatible"
] | text2text-generation | {
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"T5ForConditionalGeneration"
],
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"translation_en_to_de": {
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 7 | 2022-07-07T13:29:04Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: turkishReviews-ds-mini
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# turkishReviews-ds-mini
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 9.1630
- Validation Loss: 9.2431
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -896, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.2672 | 9.9647 | 0 |
| 9.6445 | 9.6190 | 1 |
| 9.1630 | 9.2431 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ArBert/albert-base-v2-finetuned-ner-gmm | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
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} | 8 | null | ---
language: it
license: gpl-3.0
tags:
- text classification
- abusive language
- hate speech
- offensive language
widget:
- text: "Ci sono dei bellissimi capibara!"
example_title: "Hate Speech Classification 1"
- text: "Sei una testa di cazzo!!"
example_title: "Hate Speech Classification 2"
- text: "Ti odio!"
example_title: "Hate Speech Classification 3"
---
#
[Debora Nozza](http://dnozza.github.io/) •
[Federico Bianchi](https://federicobianchi.io/) •
[Giuseppe Attanasio](https://gattanasio.cc/)
# HATE-ITA Base
HATE-ITA is a binary hate speech classification model for Italian social media text.
<img src="https://raw.githubusercontent.com/MilaNLProc/hate-ita/main/hateita.png?token=GHSAT0AAAAAABTEBAJ4PNDWAMU3KKIGUOCSYWG4IBA" width="200">
## Abstract
Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly. While Natural Language Processing has been successfully used for the purpose, many of the research efforts are directed toward the English language. This choice severely limits the classification power in non-English languages. In this paper, we test several learning frameworks for identifying hate speech in Italian text. We release **HATE-ITA, a set of multi-language models trained on a large set of English data and available Italian datasets**. HATE-ITA performs better than mono-lingual models and seems to adapt well also on language-specific slurs. We believe our findings will encourage research in other mid-to-low resource communities and provide a valuable benchmarking tool for the Italian community.
## Model
This model is the fine-tuned version of the [XLM-RoBERTa-base](https://huggingface.co/xlm-roberta-base) model.
| Model | Download |
| ------ | -------------------------|
| `hate-ita` | [Link](https://huggingface.co/MilaNLProc/hate-ita) |
| `hate-ita-xlm-r-base` | [Link](https://huggingface.co/MilaNLProc/hate-ita-xlm-r-base) |
| `hate-ita-xlm-r-large` | [Link](https://huggingface.co/MilaNLProc/hate-ita-xlm-r-large) |
## Usage
```python
from transformers import pipeline
classifier = pipeline("text-classification",model='MilaNLProc/hate-ita-xlm-r-base',top_k=2)
prediction = classifier("ti odio")
print(prediction)
```
## Citation
Please use the following BibTeX entry if you use this model in your project:
```
@inproceedings{nozza-etal-2022-hate-ita,
title = {{HATE-ITA}: Hate Speech Detection in Italian Social Media Text},
author = "Nozza, Debora and Bianchi, Federico and Attanasio, Giuseppe",
booktitle = "Proceedings of the 6th Workshop on Online Abuse and Harms",
year = "2022",
publisher = "Association for Computational Linguistics"
}
```
## Ethical Statement
While promising, the results in this work should not be interpreted as a definitive assessment of the performance of hate speech detection in Italian. We are unsure if our model can maintain a stable and fair precision across the different targets and categories. HATE-ITA might overlook some sensible details, which practitioners should treat with care.
## License
[GNU GPLv3](https://choosealicense.com/licenses/gpl-3.0/) |
ArBert/bert-base-uncased-finetuned-ner-gmm | [] | null | {
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# gemasphi/laprador_trained
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('gemasphi/laprador_trained')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('gemasphi/laprador_trained')
model = AutoModel.from_pretrained('gemasphi/laprador_trained')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=gemasphi/laprador_trained)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
ArBert/bert-base-uncased-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
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"BertForTokenClassification"
],
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}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_modified_for_t5_qg
model-index:
- name: t5-end2end-questions-generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8015
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.609 | 0.34 | 100 | 1.9542 |
| 2.0336 | 0.68 | 200 | 1.8015 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AriakimTaiyo/DialoGPT-cultured-Kumiko | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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} | 8 | 2022-07-07T18:02:47Z | ---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: tner/twitter-roberta-base-2019-90m-tweetner7-continuous
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweetner7
type: tner/tweetner7
args: tner/tweetner7
metrics:
- name: F1 (test_2021)
type: f1
value: 0.6587179789871326
- name: Precision (test_2021)
type: precision
value: 0.6727755003617073
- name: Recall (test_2021)
type: recall
value: 0.6452358926919519
- name: Macro F1 (test_2021)
type: f1_macro
value: 0.6107285696131857
- name: Macro Precision (test_2021)
type: precision_macro
value: 0.6215631908472189
- name: Macro Recall (test_2021)
type: recall_macro
value: 0.6039860329938679
- name: Entity Span F1 (test_2021)
type: f1_entity_span
value: 0.7843692816244613
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.8010610079575596
- name: Entity Span Recall (test_2021)
type: recall_entity_span
value: 0.7683589684283566
- name: F1 (test_2020)
type: f1
value: 0.6475869809203142
- name: Precision (test_2020)
type: precision
value: 0.7049480757483201
- name: Recall (test_2020)
type: recall
value: 0.598858329008822
- name: Macro F1 (test_2020)
type: f1_macro
value: 0.6057800656625983
- name: Macro Precision (test_2020)
type: precision_macro
value: 0.6627892226359489
- name: Macro Recall (test_2020)
type: recall_macro
value: 0.5669673771050993
- name: Entity Span F1 (test_2020)
type: f1_entity_span
value: 0.755331088664422
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.8222357971899816
- name: Entity Span Recall (test_2020)
type: recall_entity_span
value: 0.6984950700570836
pipeline_tag: token-classification
widget:
- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}"
example_title: "NER Example 1"
---
# tner/twitter-roberta-base-2019-90m-tweetner7-continuous
This model is a fine-tuned version of [tner/twitter-roberta-base-2019-90m-tweetner-2020](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner-2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split). The model is first fine-tuned on `train_2020`, and then continuously fine-tuned on `train_2021`.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.6587179789871326
- Precision (micro): 0.6727755003617073
- Recall (micro): 0.6452358926919519
- F1 (macro): 0.6107285696131857
- Precision (macro): 0.6215631908472189
- Recall (macro): 0.6039860329938679
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.5165775401069518
- creative_work: 0.480106100795756
- event: 0.4846715328467153
- group: 0.6041666666666665
- location: 0.6836268754076973
- person: 0.8458527493010252
- product: 0.6600985221674878
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.6500084574752211, 0.6675327789934176]
- 95%: [0.6480876172354417, 0.6695072839398589]
- F1 (macro):
- 90%: [0.6500084574752211, 0.6675327789934176]
- 95%: [0.6480876172354417, 0.6695072839398589]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-continuous/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-continuous/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip.
```shell
pip install tner
```
[TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are
converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.
```python
import re
from urlextract import URLExtract
from tner import TransformersNER
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/twitter-roberta-base-2019-90m-tweetner7-continuous")
model.predict([text_format])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/tweetner7']
- dataset_split: train_2021
- dataset_name: None
- local_dataset: None
- model: tner/twitter-roberta-base-2019-90m-tweetner-2020
- crf: True
- max_length: 128
- epoch: 30
- batch_size: 32
- lr: 1e-05
- random_seed: 0
- gradient_accumulation_steps: 1
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.3
- max_grad_norm: 1
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-continuous/raw/main/trainer_config.json).
### Reference
If you use the model, please cite T-NER paper and TweetNER7 paper.
- T-NER
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
- TweetNER7
```
@inproceedings{ushio-etal-2022-tweet,
title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
author = "Ushio, Asahi and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco. and
Camacho-Collados, Jose",
booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
month = nov,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
AriakimTaiyo/DialoGPT-medium-Kumiko | [
"conversational"
] | conversational | {
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}
}
} | 0 | null | ---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: tner/twitter-roberta-base-dec2020-tweetner7-continuous
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweetner7
type: tner/tweetner7
args: tner/tweetner7
metrics:
- name: F1 (test_2021)
type: f1
value: 0.655146764318819
- name: Precision (test_2021)
type: precision
value: 0.6484313059236607
- name: Recall (test_2021)
type: recall
value: 0.6620027752081407
- name: Macro F1 (test_2021)
type: f1_macro
value: 0.60565538970149
- name: Macro Precision (test_2021)
type: precision_macro
value: 0.5978135601251405
- name: Macro Recall (test_2021)
type: recall_macro
value: 0.6152969312272543
- name: Entity Span F1 (test_2021)
type: f1_entity_span
value: 0.7802700846875715
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7722278853777325
- name: Entity Span Recall (test_2021)
type: recall_entity_span
value: 0.7884815542962877
- name: F1 (test_2020)
type: f1
value: 0.6529060293318849
- name: Precision (test_2020)
type: precision
value: 0.6849002849002849
- name: Recall (test_2020)
type: recall
value: 0.6237675142708874
- name: Macro F1 (test_2020)
type: f1_macro
value: 0.6127864056494463
- name: Macro Precision (test_2020)
type: precision_macro
value: 0.6440791059118922
- name: Macro Recall (test_2020)
type: recall_macro
value: 0.5885664058069695
- name: Entity Span F1 (test_2020)
type: f1_entity_span
value: 0.7588267246061923
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.796011396011396
- name: Entity Span Recall (test_2020)
type: recall_entity_span
value: 0.724961079398028
pipeline_tag: token-classification
widget:
- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}"
example_title: "NER Example 1"
---
# tner/twitter-roberta-base-dec2020-tweetner7-continuous
This model is a fine-tuned version of [tner/twitter-roberta-base-dec2020-tweetner-2020](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner-2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split). The model is first fine-tuned on `train_2020`, and then continuously fine-tuned on `train_2021`.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.655146764318819
- Precision (micro): 0.6484313059236607
- Recall (micro): 0.6620027752081407
- F1 (macro): 0.60565538970149
- Precision (macro): 0.5978135601251405
- Recall (macro): 0.6152969312272543
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.5356371490280778
- creative_work: 0.4529526281635302
- event: 0.4692272096251735
- group: 0.610738255033557
- location: 0.6627831715210356
- person: 0.8433472499546196
- product: 0.6649020645844361
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.646485917836786, 0.6644401423537809]
- 95%: [0.6449507873997479, 0.6659444015725502]
- F1 (macro):
- 90%: [0.646485917836786, 0.6644401423537809]
- 95%: [0.6449507873997479, 0.6659444015725502]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-continuous/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-continuous/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip.
```shell
pip install tner
```
[TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are
converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.
```python
import re
from urlextract import URLExtract
from tner import TransformersNER
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/twitter-roberta-base-dec2020-tweetner7-continuous")
model.predict([text_format])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/tweetner7']
- dataset_split: train_2021
- dataset_name: None
- local_dataset: None
- model: tner/twitter-roberta-base-dec2020-tweetner-2020
- crf: True
- max_length: 128
- epoch: 30
- batch_size: 32
- lr: 1e-06
- random_seed: 0
- gradient_accumulation_steps: 1
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.3
- max_grad_norm: 1
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-continuous/raw/main/trainer_config.json).
### Reference
If you use the model, please cite T-NER paper and TweetNER7 paper.
- T-NER
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
- TweetNER7
```
@inproceedings{ushio-etal-2022-tweet,
title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
author = "Ushio, Asahi and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco. and
Camacho-Collados, Jose",
booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
month = nov,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
AriakimTaiyo/DialoGPT-revised-Kumiko | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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},
"text-generation": {
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 6 | null | ---
license:
- cc-by-nc-sa-4.0
- apache-2.0
tags:
- grammar
- spelling
- punctuation
- error-correction
- grammar synthesis
datasets:
- jfleg
widget:
- text: "i can has cheezburger"
example_title: "cheezburger"
- text: "There car broke down so their hitching a ride to they're class."
example_title: "compound-1"
- text: "so em if we have an now so with fito ringina know how to estimate the tren given the ereafte mylite trend we can also em an estimate is nod s
i again tort watfettering an we have estimated the trend an
called wot to be called sthat of exty right now we can and look at
wy this should not hare a trend i becan we just remove the trend an and we can we now estimate
tesees ona effect of them exty"
example_title: "Transcribed Audio Example 2"
- text: "My coworker said he used a financial planner to help choose his stocks so he wouldn't loose money."
example_title: "incorrect word choice (context)"
- text: "good so hve on an tadley i'm not able to make it to the exla session on monday this week e which is why i am e recording pre recording
an this excelleision and so to day i want e to talk about two things and first of all em i wont em wene give a summary er about
ta ohow to remove trents in these nalitives from time series"
example_title: "lowercased audio transcription output"
- text: "Frustrated, the chairs took me forever to set up."
example_title: "dangling modifier"
- text: "I would like a peice of pie."
example_title: "miss-spelling"
- text: "Which part of Zurich was you going to go hiking in when we were there for the first time together? ! ?"
example_title: "chatbot on Zurich"
- text: "Most of the course is about semantic or content of language but there are also interesting topics to be learned from the servicefeatures except statistics in characters in documents. At this point, Elvthos introduces himself as his native English speaker and goes on to say that if you continue to work on social scnce,"
example_title: "social science ASR summary output"
- text: "they are somewhat nearby right yes please i'm not sure how the innish is tepen thut mayyouselect one that istatte lo variants in their property e ere interested and anyone basical e may be applyind reaching the browing approach were"
- "medical course audio transcription"
parameters:
max_length: 128
min_length: 4
num_beams: 4
repetition_penalty: 1.21
length_penalty: 1
early_stopping: True
---
# grammar-synthesis-large - beta
A fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) for grammar correction on an expanded version of the [JFLEG](https://paperswithcode.com/dataset/jfleg) dataset.
usage in Python (after `pip install transformers`):
```python
from transformers import pipeline
corrector = pipeline(
'text2text-generation',
'pszemraj/grammar-synthesis-large',
)
raw_text = 'i can has cheezburger'
results = corrector(raw_text)
print(results)
```
give it a spin in Colab at [this notebook](https://colab.research.google.com/gist/pszemraj/be3d1d060d1da14768af75c66429dc44/grammar-synthesis-large.ipynb)
## Model description
The intent is to create a text2text language model that successfully completes "single-shot grammar correction" on a potentially grammatically incorrect text **that could have a lot of mistakes** with the important qualifier of **it does not semantically change text/information that IS grammatically correct.**
Compare some of the heavier-error examples on [other grammar correction models](https://huggingface.co/models?dataset=dataset:jfleg) to see the difference :)
### Other checkpoints
If trading a slight decrease in grammatical correction quality for faster inference speed makes sense for your use case, check out the **[base](https://huggingface.co/pszemraj/grammar-synthesis-base)** and **[small](https://huggingface.co/pszemraj/grammar-synthesis-small)** checkpoints fine-tuned from the relevant t5 checkpoints.
## Limitations
- dataset: `cc-by-nc-sa-4.0`
- model: `apache-2.0`
- this is **still a work-in-progress** and while probably useful for "single-shot grammar correction" in a lot of cases, **give the outputs a glance for correctness ok?**
## Use Cases
Obviously, this section is quite general as there are many things one can use "general single-shot grammar correction" for. Some ideas or use cases:
1. Correcting highly error-prone LM outputs. Some examples would be audio transcription (ASR) (this is literally some of the examples) or something like handwriting OCR.
- To be investigated further, depending on what model/system is used it _might_ be worth it to apply this after OCR on typed characters.
2. Correcting/infilling text generated by text generation models to be cohesive/remove obvious errors that break the conversation immersion. I use this on the outputs of [this OPT 2.7B chatbot-esque model of myself](https://huggingface.co/pszemraj/opt-peter-2.7B).
> An example of this model running on CPU with beam search:
```
original response:
ive heard it attributed to a bunch of different philosophical schools, including stoicism, pragmatism, existentialism and even some forms of post-structuralism. i think one of the most interesting (and most difficult) philosophical problems is trying to let dogs (or other animals) out of cages. the reason why this is a difficult problem is because it seems to go against our grain (so to
synthesizing took 306.12 seconds
Final response in 1294.857 s:
I've heard it attributed to a bunch of different philosophical schools, including solipsism, pragmatism, existentialism and even some forms of post-structuralism. i think one of the most interesting (and most difficult) philosophical problems is trying to let dogs (or other animals) out of cages. the reason why this is a difficult problem is because it seems to go against our grain (so to speak)
```
_Note: that I have some other logic that removes any periods at the end of the final sentence in this chatbot setting [to avoid coming off as passive aggressive](https://www.npr.org/2020/09/05/909969004/before-texting-your-kid-make-sure-to-double-check-your-punctuation)_
3. Somewhat related to #2 above, fixing/correcting so-called [tortured-phrases](https://arxiv.org/abs/2107.06751) that are dead giveaways text was generated by a language model. _Note that _SOME_ of these are not fixed, especially as they venture into domain-specific terminology (i.e. irregular timberland instead of Random Forest)._
## Training and evaluation data
More information needed 😉
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 1
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AriakimTaiyo/DialoGPT-small-Rikka | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
} | 8 | null | ## Wav2Vec2.0 XLSR-53 large model の日本語 Fine Tuning モデル
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)を日本語用にFine Tuningしたモデル
## 使用データセット
- [Common Voice](https://commonvoice.mozilla.org/ja)
## 使い方
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("kwmr/wav2vec2_japanese")
model = Wav2Vec2ForCTC.from_pretrained("kwmr/wav2vec2_japanese")
``` |
Aries/T5_question_answering | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: hsohn3/cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hsohn3/cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8009
- Epoch: 99
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 3.8699 | 0 |
| 3.1667 | 1 |
| 3.1286 | 2 |
| 3.1169 | 3 |
| 3.1077 | 4 |
| 3.0989 | 5 |
| 3.0911 | 6 |
| 3.0896 | 7 |
| 3.0820 | 8 |
| 3.0856 | 9 |
| 3.0827 | 10 |
| 3.0800 | 11 |
| 3.0647 | 12 |
| 3.0396 | 13 |
| 3.0052 | 14 |
| 2.9879 | 15 |
| 2.9633 | 16 |
| 2.9449 | 17 |
| 2.9217 | 18 |
| 2.8921 | 19 |
| 2.8625 | 20 |
| 2.8153 | 21 |
| 2.7495 | 22 |
| 2.6202 | 23 |
| 2.3762 | 24 |
| 2.1064 | 25 |
| 1.8489 | 26 |
| 1.6556 | 27 |
| 1.5005 | 28 |
| 1.4110 | 29 |
| 1.3472 | 30 |
| 1.2896 | 31 |
| 1.2391 | 32 |
| 1.2001 | 33 |
| 1.1663 | 34 |
| 1.1418 | 35 |
| 1.1159 | 36 |
| 1.0987 | 37 |
| 1.0753 | 38 |
| 1.0608 | 39 |
| 1.0456 | 40 |
| 1.0381 | 41 |
| 1.0248 | 42 |
| 1.0127 | 43 |
| 0.9970 | 44 |
| 0.9958 | 45 |
| 0.9847 | 46 |
| 0.9789 | 47 |
| 0.9617 | 48 |
| 0.9575 | 49 |
| 0.9517 | 50 |
| 0.9442 | 51 |
| 0.9379 | 52 |
| 0.9350 | 53 |
| 0.9325 | 54 |
| 0.9235 | 55 |
| 0.9182 | 56 |
| 0.9139 | 57 |
| 0.9074 | 58 |
| 0.8984 | 59 |
| 0.8988 | 60 |
| 0.8958 | 61 |
| 0.8937 | 62 |
| 0.8853 | 63 |
| 0.8812 | 64 |
| 0.8758 | 65 |
| 0.8729 | 66 |
| 0.8732 | 67 |
| 0.8647 | 68 |
| 0.8634 | 69 |
| 0.8604 | 70 |
| 0.8577 | 71 |
| 0.8597 | 72 |
| 0.8508 | 73 |
| 0.8510 | 74 |
| 0.8450 | 75 |
| 0.8451 | 76 |
| 0.8398 | 77 |
| 0.8392 | 78 |
| 0.8345 | 79 |
| 0.8350 | 80 |
| 0.8329 | 81 |
| 0.8299 | 82 |
| 0.8257 | 83 |
| 0.8217 | 84 |
| 0.8192 | 85 |
| 0.8211 | 86 |
| 0.8208 | 87 |
| 0.8171 | 88 |
| 0.8166 | 89 |
| 0.8134 | 90 |
| 0.8124 | 91 |
| 0.8102 | 92 |
| 0.8133 | 93 |
| 0.8066 | 94 |
| 0.8023 | 95 |
| 0.8049 | 96 |
| 0.8024 | 97 |
| 0.7980 | 98 |
| 0.8009 | 99 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Arina/Erine | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8536
- Epoch: 99
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 3.9508 | 0 |
| 3.4063 | 1 |
| 3.3682 | 2 |
| 3.3468 | 3 |
| 3.3330 | 4 |
| 3.3308 | 5 |
| 3.3225 | 6 |
| 3.3106 | 7 |
| 3.2518 | 8 |
| 3.1859 | 9 |
| 3.1373 | 10 |
| 3.0923 | 11 |
| 3.0390 | 12 |
| 2.9560 | 13 |
| 2.8605 | 14 |
| 2.7564 | 15 |
| 2.4969 | 16 |
| 2.2044 | 17 |
| 1.9566 | 18 |
| 1.7686 | 19 |
| 1.5995 | 20 |
| 1.4932 | 21 |
| 1.4100 | 22 |
| 1.3538 | 23 |
| 1.2973 | 24 |
| 1.2610 | 25 |
| 1.2160 | 26 |
| 1.1916 | 27 |
| 1.1607 | 28 |
| 1.1468 | 29 |
| 1.1262 | 30 |
| 1.1123 | 31 |
| 1.0942 | 32 |
| 1.0816 | 33 |
| 1.0717 | 34 |
| 1.0575 | 35 |
| 1.0503 | 36 |
| 1.0411 | 37 |
| 1.0293 | 38 |
| 1.0229 | 39 |
| 1.0139 | 40 |
| 1.0081 | 41 |
| 1.0028 | 42 |
| 0.9967 | 43 |
| 0.9906 | 44 |
| 0.9834 | 45 |
| 0.9782 | 46 |
| 0.9766 | 47 |
| 0.9676 | 48 |
| 0.9618 | 49 |
| 0.9611 | 50 |
| 0.9553 | 51 |
| 0.9504 | 52 |
| 0.9483 | 53 |
| 0.9404 | 54 |
| 0.9423 | 55 |
| 0.9361 | 56 |
| 0.9327 | 57 |
| 0.9327 | 58 |
| 0.9263 | 59 |
| 0.9275 | 60 |
| 0.9218 | 61 |
| 0.9202 | 62 |
| 0.9158 | 63 |
| 0.9152 | 64 |
| 0.9091 | 65 |
| 0.9104 | 66 |
| 0.9094 | 67 |
| 0.9087 | 68 |
| 0.9034 | 69 |
| 0.9063 | 70 |
| 0.8984 | 71 |
| 0.8966 | 72 |
| 0.8953 | 73 |
| 0.8910 | 74 |
| 0.8913 | 75 |
| 0.8887 | 76 |
| 0.8868 | 77 |
| 0.8868 | 78 |
| 0.8815 | 79 |
| 0.8821 | 80 |
| 0.8791 | 81 |
| 0.8752 | 82 |
| 0.8731 | 83 |
| 0.8779 | 84 |
| 0.8727 | 85 |
| 0.8702 | 86 |
| 0.8712 | 87 |
| 0.8689 | 88 |
| 0.8646 | 89 |
| 0.8644 | 90 |
| 0.8608 | 91 |
| 0.8643 | 92 |
| 0.8602 | 93 |
| 0.8605 | 94 |
| 0.8568 | 95 |
| 0.8567 | 96 |
| 0.8557 | 97 |
| 0.8543 | 98 |
| 0.8536 | 99 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Arpita/opus-mt-en-ro-finetuned-syn-to-react | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 9 | 2022-07-07T20:31:33Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="phyous/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
ArshdeepSekhon050/DialoGPT-medium-RickAndMorty | [] | null | {
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} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/gassy_dragon/1657227895422/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/1423289998544044032/vc29B5yA_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">Bau be tootin on ur butt.</div>
<div style="text-align: center; font-size: 14px;">@gassy_dragon</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 Bau be tootin on ur butt..
| Data | Bau be tootin on ur butt. |
| --- | --- |
| Tweets downloaded | 3188 |
| Retweets | 953 |
| Short tweets | 487 |
| Tweets kept | 1748 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3puk9479/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 @gassy_dragon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3cp8z35e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3cp8z35e/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/gassy_dragon')
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)
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9158064516129032
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7786
- Accuracy: 0.9158
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2838 | 1.0 | 318 | 3.2787 | 0.7455 |
| 2.622 | 2.0 | 636 | 1.8706 | 0.8332 |
| 1.5466 | 3.0 | 954 | 1.1623 | 0.8939 |
| 1.0135 | 4.0 | 1272 | 0.8619 | 0.91 |
| 0.7985 | 5.0 | 1590 | 0.7786 | 0.9158 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Ashkanmh/bert-base-parsbert-uncased-finetuned | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: fancy-animales
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9464285969734192
---
# fancy-animales
Just for fun and to test the template!
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
#### chow chow
#### panda
#### penguin
#### sloth
#### wombat
 |
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-hinglish
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-hinglish
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5475
## 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: 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: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3557 | 1.0 | 460 | 0.7714 |
| 0.6349 | 2.0 | 920 | 0.5475 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
At3ee/wav2vec2-base-timit-demo-colab | [] | null | {
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} | 0 | 2022-07-08T00:35:14Z | ---
license: apache-2.0
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1
This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1](https://huggingface.co/gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5459
- Wer: 0.2463
## 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-06
- 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_steps: 1000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 0.3909 | 1.0 | 2309 | 0.5615 | 0.2459 |
| 0.4094 | 2.0 | 4618 | 0.5654 | 0.2439 |
| 0.326 | 3.0 | 6927 | 0.5568 | 0.2470 |
| 0.4577 | 4.0 | 9236 | 0.5795 | 0.2474 |
| 0.3628 | 5.0 | 11545 | 0.5459 | 0.2463 |
| 0.3135 | 6.0 | 13854 | 0.5582 | 0.2473 |
| 0.5058 | 7.0 | 16163 | 0.5677 | 0.2439 |
| 0.3188 | 8.0 | 18472 | 0.5646 | 0.2445 |
| 0.3589 | 9.0 | 20781 | 0.5626 | 0.2479 |
| 0.4021 | 10.0 | 23090 | 0.5722 | 0.2452 |
| 0.4362 | 11.0 | 25399 | 0.5659 | 0.2431 |
| 0.3215 | 12.0 | 27708 | 0.5658 | 0.2445 |
| 0.3646 | 13.0 | 30017 | 0.5785 | 0.2459 |
| 0.3757 | 14.0 | 32326 | 0.5757 | 0.2418 |
| 0.3311 | 15.0 | 34635 | 0.5672 | 0.2455 |
| 0.3709 | 16.0 | 36944 | 0.5669 | 0.2434 |
| 0.3342 | 17.0 | 39253 | 0.5610 | 0.2455 |
| 0.3236 | 18.0 | 41562 | 0.5652 | 0.2436 |
| 0.3566 | 19.0 | 43871 | 0.5773 | 0.2407 |
| 0.2912 | 20.0 | 46180 | 0.5764 | 0.2453 |
| 0.3652 | 21.0 | 48489 | 0.5732 | 0.2423 |
| 0.3785 | 22.0 | 50798 | 0.5696 | 0.2423 |
| 0.3968 | 23.0 | 53107 | 0.5690 | 0.2429 |
| 0.2968 | 24.0 | 55416 | 0.5800 | 0.2427 |
| 0.428 | 25.0 | 57725 | 0.5704 | 0.2441 |
| 0.383 | 26.0 | 60034 | 0.5739 | 0.2450 |
| 0.3694 | 27.0 | 62343 | 0.5791 | 0.2437 |
| 0.3449 | 28.0 | 64652 | 0.5780 | 0.2451 |
| 0.3008 | 29.0 | 66961 | 0.5749 | 0.2418 |
| 0.3939 | 30.0 | 69270 | 0.5737 | 0.2424 |
| 0.3451 | 31.0 | 71579 | 0.5805 | 0.2402 |
| 0.3513 | 32.0 | 73888 | 0.5670 | 0.2379 |
| 0.3866 | 33.0 | 76197 | 0.5706 | 0.2389 |
| 0.3831 | 34.0 | 78506 | 0.5635 | 0.2401 |
| 0.3641 | 35.0 | 80815 | 0.5708 | 0.2405 |
| 0.3345 | 36.0 | 83124 | 0.5699 | 0.2405 |
| 0.2902 | 37.0 | 85433 | 0.5711 | 0.2373 |
| 0.2868 | 38.0 | 87742 | 0.5713 | 0.2389 |
| 0.3232 | 39.0 | 90051 | 0.5702 | 0.2392 |
| 0.3277 | 40.0 | 92360 | 0.5658 | 0.2393 |
| 0.3234 | 41.0 | 94669 | 0.5732 | 0.2412 |
| 0.3625 | 42.0 | 96978 | 0.5740 | 0.2396 |
| 0.4075 | 43.0 | 99287 | 0.5733 | 0.2389 |
| 0.3473 | 44.0 | 101596 | 0.5735 | 0.2394 |
| 0.3157 | 45.0 | 103905 | 0.5721 | 0.2391 |
| 0.3866 | 46.0 | 106214 | 0.5715 | 0.2381 |
| 0.4062 | 47.0 | 108523 | 0.5711 | 0.2380 |
| 0.3871 | 48.0 | 110832 | 0.5716 | 0.2380 |
| 0.2924 | 49.0 | 113141 | 0.5723 | 0.2374 |
| 0.3655 | 50.0 | 115450 | 0.5709 | 0.2379 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
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} | 0 | 2022-07-08T01:09:10Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-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. -->
# Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) 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: 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
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Ateeb/FullEmotionDetector | [
"pytorch",
"funnel",
"text-classification",
"transformers"
] | text-classification | {
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} | 31 | null | ---
license: cc
---
# D&D&VQGAN
## Intro
As I get a chance to play around with a lot more of these models. I find myself wanting to create D&D (or general fantasy and Sci-Fi themed images) generated from text prompt (think of what you see being implemented now in AI Dungeon). |
Augustab/distilbert-base-uncased-finetuned-cola | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9454838709677419
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3120
- Accuracy: 0.9455
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 1.8803 | 0.7426 |
| 2.2488 | 2.0 | 636 | 0.9662 | 0.8626 |
| 2.2488 | 3.0 | 954 | 0.5640 | 0.9103 |
| 0.8679 | 4.0 | 1272 | 0.4093 | 0.9332 |
| 0.4101 | 5.0 | 1590 | 0.3554 | 0.9435 |
| 0.4101 | 6.0 | 1908 | 0.3312 | 0.9445 |
| 0.2894 | 7.0 | 2226 | 0.3179 | 0.9452 |
| 0.2496 | 8.0 | 2544 | 0.3137 | 0.9448 |
| 0.2496 | 9.0 | 2862 | 0.3120 | 0.9455 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_wav2vec2_s203
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Augustvember/wokka2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 12 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_xlsr-53_s870
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AvatarXD/DialoGPT-medium-Blitzo | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 14 | null | ---
tags:
- image-classification
- timm
library_tag: timm
---
# Model card for resnet50d |
Aviora/news2vec | [] | null | {
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} | 0 | 2022-07-08T05:35:18Z | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_unispeech_s227
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/albert_distilgpt2_summarization_cnn_dailymail | [
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"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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} | 9 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_no-pretraining_s852
Fine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/albert_gpt2_Full_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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} | 9 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_wavlm_s767
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/albert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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} | 6 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_wavlm_s461
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/bert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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} | 4 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_unispeech-ml_s756
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/bert_gpt2_summarization_cnndm_new | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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}
} | 8 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-fr_s118
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/bert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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}
}
} | 6 | null | ---
language:
- "lzh"
tags:
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "question-answering"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "question-answering"
inference:
parameters:
align_to_words: false
widget:
- text: "穴"
context: "不入虎穴不得虎子"
- text: "子"
context: "不入虎穴不得虎子"
- text: "不"
context: "[MASK]入虎穴不得虎子"
---
# bert-ancient-chinese-base-ud-head
## Model Description
This is a BERT model pre-trained on Classical Chinese texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from [bert-ancient-chinese](https://huggingface.co/Jihuai/bert-ancient-chinese) and [UD_Classical_Chinese-Kyoto](https://github.com/UniversalDependencies/UD_Classical_Chinese-Kyoto). Use [MASK] inside `context` to avoid ambiguity when specifying a multiple-used word as `question`.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-ancient-chinese-base-ud-head")
model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/bert-ancient-chinese-base-ud-head")
qap=QuestionAnsweringPipeline(tokenizer=tokenizer,model=model,align_to_words=False)
print(qap(question="穴",context="不入虎穴不得虎子"))
```
or (with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/))
```py
class TransformersUD(object):
def __init__(self,bert):
import os
from transformers import (AutoTokenizer,AutoModelForQuestionAnswering,
AutoModelForTokenClassification,AutoConfig,TokenClassificationPipeline)
self.tokenizer=AutoTokenizer.from_pretrained(bert)
self.model=AutoModelForQuestionAnswering.from_pretrained(bert)
x=AutoModelForTokenClassification.from_pretrained
if os.path.isdir(bert):
d,t=x(os.path.join(bert,"deprel")),x(os.path.join(bert,"tagger"))
else:
from transformers.utils import cached_file
c=AutoConfig.from_pretrained(cached_file(bert,"deprel/config.json"))
d=x(cached_file(bert,"deprel/pytorch_model.bin"),config=c)
s=AutoConfig.from_pretrained(cached_file(bert,"tagger/config.json"))
t=x(cached_file(bert,"tagger/pytorch_model.bin"),config=s)
self.deprel=TokenClassificationPipeline(model=d,tokenizer=self.tokenizer,
aggregation_strategy="simple")
self.tagger=TokenClassificationPipeline(model=t,tokenizer=self.tokenizer)
def __call__(self,text):
import numpy,torch,ufal.chu_liu_edmonds
w=[(t["start"],t["end"],t["entity_group"]) for t in self.deprel(text)]
z,n={t["start"]:t["entity"].split("|") for t in self.tagger(text)},len(w)
r,m=[text[s:e] for s,e,p in w],numpy.full((n+1,n+1),numpy.nan)
v,c=self.tokenizer(r,add_special_tokens=False)["input_ids"],[]
for i,t in enumerate(v):
q=[self.tokenizer.cls_token_id]+t+[self.tokenizer.sep_token_id]
c.append([q]+v[0:i]+[[self.tokenizer.mask_token_id]]+v[i+1:]+[[q[-1]]])
b=[[len(sum(x[0:j+1],[])) for j in range(len(x))] for x in c]
with torch.no_grad():
d=self.model(input_ids=torch.tensor([sum(x,[]) for x in c]),
token_type_ids=torch.tensor([[0]*x[0]+[1]*(x[-1]-x[0]) for x in b]))
s,e=d.start_logits.tolist(),d.end_logits.tolist()
for i in range(n):
for j in range(n):
m[i+1,0 if i==j else j+1]=s[i][b[i][j]]+e[i][b[i][j+1]-1]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
if [0 for i in h if i==0]!=[0]:
i=([p for s,e,p in w]+["root"]).index("root")
j=i+1 if i<n else numpy.nanargmax(m[:,0])
m[0:j,0]=m[j+1:,0]=numpy.nan
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
u="# text = "+text.replace("\n"," ")+"\n"
for i,(s,e,p) in enumerate(w,1):
p="root" if h[i]==0 else "dep" if p=="root" else p
u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]),
str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n"
return u+"\n"
nlp=TransformersUD("KoichiYasuoka/bert-ancient-chinese-base-ud-head")
print(nlp("不入虎穴不得虎子"))
```
|
Ayham/bert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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}
}
} | 3 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-fr_s691
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/distilbert_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
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}
}
} | 5 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-fr_s51
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/distilbert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
} | 6 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-es_s952
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/distilbert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
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}
}
} | 8 | null | ---
language:
- en
- ro
tags:
- generated_from_trainer
datasets:
- wmt16
model-index:
- name: finetuned-mbart-large-10epoch
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. -->
# finetuned-mbart-large-10epoch
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6032
## 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: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Ayham/distilbert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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"EncoderDecoderModel"
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}
}
} | 14 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-es_s474
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/ernie_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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"max_length": null
},
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}
} | 13 | null | ---
language:
- en
- tok
- multilingual
license: apache-2.0
tags:
- generated_from_trainer
- translation
widget:
- text: Hello, my name is Tom.
- text: Can the cat speak English?
model-index:
- name: en-toki-mt
results: []
---
# en-toki-mt
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ROMANCE](https://huggingface.co/Helsinki-NLP/opus-mt-en-ROMANCE) on the English - toki pona translation dataset on Tatoeba.
## Model description
toki pona is a minimalist constructed language created in 2014 by Sonja Lang. The language features a very small volcabulary (~130 words) and a very simple grammar structure.
## Intended uses & limitations
This model aims to translate English to Toki pona.
## Training and evaluation data
The training data is acquired from all En-Toki sentence pairs on [Tatoeba](https://tatoeba.org/en) (~20000 pairs), without any filtering. Since this dataset mostly only includes core words (pu), it may produce inaccurate results when encountering more complex words. The model achieved a BLEU score of 54 on the testing set.
## 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: 10
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Ayham/roberta_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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"EncoderDecoderModel"
],
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}
}
} | 12 | 2022-07-08T07:53:28Z | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-es_s186
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/xlnet_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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}
}
} | 7 | 2022-07-08T08:46:09Z | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_unispeech-sat_s459
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayham/xlnet_distilgpt2_summarization_cnn_dailymail | [
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"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
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}
} | 13 | 2022-07-08T08:50:41Z | ---
tags:
- conversational
---
#Michael from Office DialoGPT Model |
Ayham/xlnet_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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} | 8 | 2022-07-08T08:54:25Z | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_xls-r_s957
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
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} | 0 | 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
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8684210526315789
---
<!-- 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: 0.3157
- Accuracy: 0.8667
- F1: 0.8684
## 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 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Ayham/xlnet_roberta_summarization_cnn_dailymail | [
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"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
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} | 10 | null | ---
language: chinese
---
# ERNIE-Gram-chinese
## Introduction
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding
More detail: https://arxiv.org/abs/2010.12148
## Released Model Info
|Model Name|Language|Model Structure|
|:---:|:---:|:---:|
|ernie-gram-chinese| Chinese |Layer:12, Hidden:768, Heads:12|
This released Pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conducted to check the accuracy of the conversion.
- Official PaddlePaddle ERNIE repo: https://github.com/PaddlePaddle/ERNIE
- Pytorch Conversion repo: https://github.com/nghuyong/ERNIE-Pytorch
## How to use
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("swtx/ernie-gram-chinese")
model = AutoModel.from_pretrained("swtx/ernie-gram-chinese")
``` |
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} | 0 | 2022-07-08T09:18:32Z | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_r-wav2vec2_s863
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayoola/wav2vec2-large-xlsr-turkish-demo-colab | [] | null | {
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} | 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: epsil/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ayran/DialoGPT-medium-harry-potter-1-through-3 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
} | 12 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_r-wav2vec2_s44
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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}
} | 12 | null | ---
language:
- ace
- acm
- acq
- aeb
- af
- ajp
- ak
- als
- am
- apc
- ar
- ars
- ary
- arz
- as
- ast
- awa
- ayr
- azb
- azj
- ba
- bm
- ban
- be
- bem
- bn
- bho
- bjn
- bo
- bs
- bug
- bg
- ca
- ceb
- cs
- cjk
- ckb
- crh
- cy
- da
- de
- dik
- dyu
- dz
- el
- en
- eo
- et
- eu
- ee
- fo
- fj
- fi
- fon
- fr
- fur
- fuv
- gaz
- gd
- ga
- gl
- gn
- gu
- ht
- ha
- he
- hi
- hne
- hr
- hu
- hy
- ig
- ilo
- id
- is
- it
- jv
- ja
- kab
- kac
- kam
- kn
- ks
- ka
- kk
- kbp
- kea
- khk
- km
- ki
- rw
- ky
- kmb
- kmr
- knc
- kg
- ko
- lo
- lij
- li
- ln
- lt
- lmo
- ltg
- lb
- lua
- lg
- luo
- lus
- lvs
- mag
- mai
- ml
- mar
- min
- mk
- mt
- mni
- mos
- mi
- my
- nl
- nn
- nb
- npi
- nso
- nus
- ny
- oc
- ory
- pag
- pa
- pap
- pbt
- pes
- plt
- pl
- pt
- prs
- quy
- ro
- rn
- ru
- sg
- sa
- sat
- scn
- shn
- si
- sk
- sl
- sm
- sn
- sd
- so
- st
- es
- sc
- sr
- ss
- su
- sv
- swh
- szl
- ta
- taq
- tt
- te
- tg
- tl
- th
- ti
- tpi
- tn
- ts
- tk
- tum
- tr
- tw
- tzm
- ug
- uk
- umb
- ur
- uzn
- vec
- vi
- war
- wo
- xh
- ydd
- yo
- yue
- zh
- zsm
- zu
language_details: "ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn"
tags:
- nllb
- translation
license: "cc-by-nc-4.0"
datasets:
- flores-200
metrics:
- bleu
- spbleu
- chrf++
inference: false
---
# NLLB-200
This is the model card of NLLB-200's distilled 600M variant.
Here are the [metrics](https://tinyurl.com/nllb200densedst600mmetrics) for that particular checkpoint.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.
- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022
- License: CC-BY-NC
- Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues
## Intended Use
- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.
- Primary intended users: Primary users are researchers and machine translation research community.
- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.
## Metrics
• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations.
## Evaluation Data
- Datasets: Flores-200 dataset is described in Section 4
- Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200
- Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The
SentencePiece model is released along with NLLB-200.
## Training Data
• We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2.
## Ethical Considerations
• In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety).
## Caveats and Recommendations
• Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments.
## Carbon Footprint Details
• The carbon dioxide (CO2e) estimate is reported in Section 8.8. |
Ayran/DialoGPT-small-harry-potter-1-through-3 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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}
}
} | 12 | null | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-it_s859
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Ayta/Haha | [] | null | {
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}
} | 0 | null | ---
license: cc-by-4.0
language:
- ca
- de
- multilingual
datasets:
- Softcatala/parallel-catalan-corpus/deu-cat
metrics:
- "bleu"
- "meteor"
- "chrf"
- "ter"
model-index:
- name: m2m100_418M_ft_de_ca
results:
- task:
type: translation
dataset:
type: flores
name: Flores
metrics:
- name: BLEU
type: bleu
value: 28.5
- task:
type: translation
dataset:
type: wmt/wmt13
name: WMT13
metrics:
- name: BLEU
type: bleu
value: 22.9
- task:
type: translation
dataset:
type: flores
name: Flores
metrics:
- name: TER
type: ter
value: 60.7
- task:
type: translation
dataset:
type: wmt/wmt13
name: WMT13
metrics:
- name: TER
type: ter
value: 71.0
- task:
type: translation
dataset:
type: flores
name: Flores
metrics:
- name: METEOR
type: meteor
value: 55.9
- task:
type: translation
dataset:
type: wmt/wmt13
name: WMT13
metrics:
- name: METEOR
type: meteor
value: 49.5
- task:
type: translation
dataset:
type: flores
name: Flores
metrics:
- name: chrF
type: chrf
value: 55.9
- task:
type: translation
dataset:
type: wmt/wmt13
name: WMT13
metrics:
- name: chrF
type: chrf
value: 54.1
---
## m2m100 fine-tuned on Softcatalà's parallel Catalan-German dataset for machine translation
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to Use](#how-to-use)
- [Training](#training)
- [Training data](#training-data)
- [Training procedure](#training-procedure)
- [Tokenization](#tokenization)
- [Hyperparameters](#hyperparameters)
- [Evaluation](#evaluation)
- [Variable and metrics](#variable-and-metrics)
- [Evaluation results](#evaluation-results)
- [Additional information](#additional-information)
- [Licensing information](#licensing-information)
- [Funding](#funding)
- [Disclaimer](#disclaimer)
</details>
## Model description
This model was obtained by fine-tuning the [m2m100_418M](https://huggingface.co/facebook/m2m100_418M) model on a De-Ca machine translation task with the [Softcatalà Catalan-German parallel corpus](https://github.com/Softcatala/parallel-catalan-corpus/tree/master/deu-cat) dataset, with sentences deduplicated and filtered by the [GEnCaTa quality filter](https://huggingface.co/projecte-aina/mbert-base-gencata). We also evaluate it on a general-domain multilingual testset [Flores-200](https://github.com/facebookresearch/flores) and [WMT13](https://www.statmt.org/wmt13/).
## Intended uses and limitations
You can use this model for machine translation from German to Catalan.
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("projecte-aina/2m100-418M-ft-de-ca")
model = AutoModelForSeq2SeqLM.from_pretrained("projecte-aina/2m100-418M-ft-de-ca")
```
## Training
### Training data
As a data for fine-tuning we used the [Softcatalà Catalan-German parallel corpus](https://github.com/Softcatala/parallel-catalan-corpus/tree/master/deu-cat) dataset, with sentences deduplicated and filtered by the [GEnCaTa quality filter](https://huggingface.co/projecte-aina/mbert-base-gencata).
### Training procedure
#### Tokenization
The original [m2m100_418M](https://huggingface.co/facebook/m2m100_418M) model's sentencepiece tokenizer was used.
#### Hyperparameters
The model was trained for 2 epochs with the default parameters and \\(LR = 2\mathrm{e}{-5}\\).
## Evaluation
### Variable and metrics
We use the BLEU score for evaluation on test sets: [Flores-200](https://github.com/facebookresearch/flores) and [WMT13](https://www.statmt.org/wmt13/).
### Evaluation results
Below are the evaluation results on the machine translation from German to Catalan compared with the original m2m100 on a testset: [Flores-200](https://github.com/facebookresearch/flores).
|Test set | Model | BLEU | TER | METEOR | chrF
| ------------|-------------| -----| -----| -----| -----|
|Flores-200 | m2m100 | 26.6 | 63.1 | 54.0 | 53.5 |
| | m2m100-418M-ft-de-ca | **28.5** | **60.7** | **55.9** | **55.9** |
|WMT13 | m2m100 | 21.8 | 72.8 | 48.0 | 53.5 |
| | m2m100-418M-ft-de-ca | **22.9** | **71.0** | **49.5** | **54.1** |
## Additional information
### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Citation Information
```
@article{garriga2022catalan,
title={A Catalan-German machine translation system based on the M2M-100 multilingual model},
author={Garriga Riba, Pol},
year={2022},
url={https://repositori.upf.edu/bitstream/handle/10230/54301/GarrigaRiba_2022.pdf?sequence=1&isAllowed=y}
}
```
### Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
|
AyushPJ/ai-club-inductions-21-nlp-XLNet | [
"pytorch",
"xlnet",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"XLNetForQuestionAnsweringSimple"
],
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}
}
} | 9 | null | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_wav2vec2_s664
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AyushPJ/ai-club-inductions-21-nlp-distilBERT | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
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"prefix": null
}
}
} | 8 | null | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_wav2vec2_s729
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Azaghast/DistilBERT-SCP-Class-Classification | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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}
}
} | 42 | 2022-07-08T10:26:54Z | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_xlsr-53_s711
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Azaghast/GPT2-SCP-Descriptions | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
}
} | 5 | null | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_xlsr-53_s218
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Azaghast/GPT2-SCP-Miscellaneous | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
} | 5 | null | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech_s328
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Azizun/Geotrend-10-epochs | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
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"BertForTokenClassification"
],
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}
} | 6 | null | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech_s624
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Azuris/DialoGPT-medium-envy | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
}
} | 12 | 2022-07-08T10:45:06Z | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech_s131
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Azuris/DialoGPT-medium-senorita | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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}
}
} | 14 | null | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_hubert_s975
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Azuris/DialoGPT-small-envy | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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} | 14 | null | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_hubert_s533
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner_swedish_small_set_health_and_prices
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner_swedish_small_set_health_and_prices
This model is a fine-tuned version of [KBLab/bert-base-swedish-cased-ner](https://huggingface.co/KBLab/bert-base-swedish-cased-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0942
- Precision: 0.7709
- Recall: 0.8118
- F1: 0.7908
- Accuracy: 0.9741
## 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 | 250 | 0.1310 | 0.6116 | 0.7471 | 0.6726 | 0.9578 |
| 0.1583 | 2.0 | 500 | 0.0939 | 0.7560 | 0.8020 | 0.7783 | 0.9737 |
| 0.1583 | 3.0 | 750 | 0.0942 | 0.7709 | 0.8118 | 0.7908 | 0.9741 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.7.1
- Datasets 2.2.2
- Tokenizers 0.12.1
|
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