More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 40.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3421        | 2.16  | 2500  | 1.2730          | 0.4097 |
| 1.229         | 4.31  | 5000  | 1.2522          | 0.3908 |
| 1.1494        | 6.47  | 7500  | 1.1937          | 0.3857 |
| 1.0801        | 8.62  | 10000 | 1.1936          | 0.3838 |
| 1.0366        | 10.78 | 12500 | 1.1860          | 0.3936 |
| 1.0292        | 12.93 | 15000 | 1.2014          | 0.3819 |
| 0.9217        | 15.09 | 17500 | 1.2313          | 0.3857 |
| 0.9182        | 17.24 | 20000 | 1.2617          | 0.3923 |
| 0.8731        | 19.4  | 22500 | 1.2850          | 0.3940 |
| 0.8471        | 21.55 | 25000 | 1.3432          | 0.3912 |
| 0.8372        | 23.71 | 27500 | 1.3238          | 0.3888 |
| 0.7905        | 25.86 | 30000 | 1.3911          | 0.3962 |
| 0.7553        | 28.02 | 32500 | 1.4314          | 0.3974 |
| 0.7448        | 30.17 | 35000 | 1.4246          | 0.4007 |
| 0.7228        | 32.33 | 37500 | 1.4303          | 0.4006 |
| 0.6941        | 34.48 | 40000 | 1.5059          | 0.4006 |
| 0.6804        | 36.64 | 42500 | 1.5281          | 0.4008 |
| 0.6652        | 38.79 | 45000 | 1.5382          | 0.4004 |


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.12.2.dev0
- Tokenizers 0.10.3


model name: wav2vec2-large-robust-ami_multi-nithin9


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, en, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-tune_0.0001_4
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# wav2vec2-base-tune_0.0001_4


This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 1.5284
- Wer: 0.4735


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.971         | 0.86  | 1000  | 1.8257          | 0.6751 |
| 1.4062        | 1.72  | 2000  | 1.4239          | 0.4815 |
| 1.2763        | 2.59  | 3000  | 1.3776          | 0.4461 |
| 1.2106        | 3.45  | 4000  | 1.3215          | 0.4428 |
| 1.1394        | 4.31  | 5000  | 1.3168          | 0.4343 |
| 1.0651        | 5.17  | 6000  | 1.2975          | 0.4258 |
| 1.0268        | 6.03  | 7000  | 1.3086          | 0.4242 |
| 1.0056        | 6.9   | 8000  | 1.3209          | 0.4295 |
| 0.9655        | 7.76  | 9000  | 1.3159          | 0.4284 |
| 0.9283        | 8.62  | 10000 | 1.3286          | 0.4259 |
| 0.9244        | 9.48  | 11000 | 1.3411          | 0.4243 |


### Framework versions


model name: wav2vec2-base-ami_multi-tune_0.0001_4


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: ami-dummy-nithin
results: []


# ami-dummy-nithin


- Loss: 25.1441
- Wer: 1.0


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| No log        | 1.24  | 15   | 85.3333         | 1.0 |
| No log        | 2.48  | 30   | 43.9463         | 1.0 |


### Framework versions


model name: ami-dummy-nithin


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-ami_multi-nithin3
results: []


# wav2vec2-base-ami_multi-nithin3


- Loss: 1.9953
- Wer: 0.4577


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.7412        | 1.07  | 2500  | 2.9356          | 0.9925 |
| 2.0224        | 2.13  | 5000  | 2.0951          | 0.5730 |
| 1.9017        | 3.2   | 7500  | 1.8801          | 0.5070 |
| 1.8356        | 4.27  | 10000 | 2.0530          | 0.4778 |
| 1.8002        | 5.33  | 12500 | 1.9465          | 0.4620 |
| 1.7424        | 6.4   | 15000 | 1.9561          | 0.4529 |
| 1.7406        | 7.47  | 17500 | 1.9190          | 0.4477 |
| 1.7046        | 8.53  | 20000 | 1.8138          | 0.4402 |
| 1.6784        | 9.6   | 22500 | 1.8275          | 0.4385 |
| 1.6657        | 10.67 | 25000 | 1.7603          | 0.4307 |
| 1.6618        | 11.73 | 27500 | 1.7269          | 0.4249 |
| 1.6037        | 12.8  | 30000 | 1.7071          | 0.4272 |
| 1.639         | 13.87 | 32500 | 1.6559          | 0.4234 |
| 1.614         | 14.93 | 35000 | 1.7535          | 0.4237 |
| 1.6044        | 16.0  | 37500 | 1.7945          | 0.4200 |
| 1.5685        | 17.06 | 40000 | 1.7135          | 0.4170 |
| 1.6194        | 18.13 | 42500 | 1.8712          | 0.4161 |
| 1.566         | 19.2  | 45000 | 1.8720          | 0.4176 |
| 1.5572        | 20.26 | 47500 | 1.7077          | 0.4135 |
| 1.5715        | 21.33 | 50000 | 1.7538          | 0.4143 |
| 1.5595        | 22.4  | 52500 | 1.8135          | 0.4133 |
| 1.5465        | 23.46 | 55000 | 1.8119          | 0.4134 |
| 1.5369        | 24.53 | 57500 | 1.7565          | 0.4086 |
| 1.5392        | 25.6  | 60000 | 1.7323          | 0.4101 |
| 1.5383        | 26.66 | 62500 | 1.7516          | 0.4097 |
| 1.5266        | 27.73 | 65000 | 1.7961          | 0.4104 |
| 1.525         | 28.8  | 67500 | 1.7472          | 0.4094 |
| 1.5779        | 29.86 | 70000 | 1.7600          | 0.4096 |


### Framework versions


model name: wav2vec2-base-ami_multi-nithin3


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-tune_0.00005_4
results: []


# wav2vec2-base-tune_0.00005_4


- Loss: 1.5405
- Wer: 0.4744


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.4032        | 0.86  | 1000  | 2.1379          | 0.8193 |
| 1.4611        | 1.72  | 2000  | 1.4984          | 0.5155 |
| 1.315         | 2.59  | 3000  | 1.4401          | 0.4707 |
| 1.2574        | 3.45  | 4000  | 1.3587          | 0.4559 |
| 1.1924        | 4.31  | 5000  | 1.3372          | 0.4450 |
| 1.1313        | 5.17  | 6000  | 1.3187          | 0.4351 |
| 1.0911        | 6.03  | 7000  | 1.3446          | 0.4354 |
| 1.0753        | 6.9   | 8000  | 1.3450          | 0.4396 |
| 1.0504        | 7.76  | 9000  | 1.3342          | 0.4378 |
| 1.0249        | 8.62  | 10000 | 1.3442          | 0.4335 |
| 1.0327        | 9.48  | 11000 | 1.3412          | 0.4293 |


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.12.2.dev0
- Tokenizers 0.10.3


model name: wav2vec2-base-ami_multi-tune_0.00005_4


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, en, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-tune_dropout_0.0003_8
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-lv60-ami_multi-tune_dropout_0.0003_8


This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 1.4292
- Wer: 0.4203


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.6596        | 0.86  | 1000  | 1.4908          | 0.5105 |
| 1.4211        | 1.72  | 2000  | 1.3459          | 0.4167 |
| 1.3246        | 2.59  | 3000  | 1.2844          | 0.3992 |
| 1.2588        | 3.45  | 4000  | 1.2392          | 0.3995 |
| 1.2045        | 4.31  | 5000  | 1.2349          | 0.3928 |
| 1.1543        | 5.17  | 6000  | 1.2056          | 0.3886 |
| 1.119         | 6.03  | 7000  | 1.2005          | 0.3793 |
| 1.0984        | 6.9   | 8000  | 1.2024          | 0.3808 |
| 1.0726        | 7.76  | 9000  | 1.1921          | 0.3791 |
| 1.054         | 8.62  | 10000 | 1.1835          | 0.3793 |
| 1.0498        | 9.48  | 11000 | 1.1854          | 0.3743 |


### Framework versions


model name: wav2vec2-large-lv60-ami_multi-tune_dropout_0.0003_8


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: ami-dummy-vumichien
results: []


# ami-dummy-vumichien


This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 90.3471
- Wer: 1.0


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP


### Training results


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.13.3
- Tokenizers 0.10.3


model name: ami-dummy-vumichien


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, en, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


The vocabulary was obtained using the code here: https://github.com/Nithin-Holla/transformers/blob/ami/examples/research_projects/wav2vec2/ami_preprocess.py


model name: ami_vocab_and_preprocessing


tags:


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-ami_single-vumichien2
results: []


# wav2vec2-base-ami_single-vumichien2


- Loss: nan
- Wer: 1.0


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 3e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:---:|
| 0.0           | 0.53  | 2500  | nan             | 1.0 |
| 0.0           | 1.06  | 5000  | nan             | 1.0 |
| 0.0           | 1.59  | 7500  | nan             | 1.0 |
| 0.0           | 2.12  | 10000 | nan             | 1.0 |
| 0.0           | 2.64  | 12500 | nan             | 1.0 |
| 0.0           | 3.17  | 15000 | nan             | 1.0 |
| 0.0           | 3.7   | 17500 | nan             | 1.0 |
| 0.0           | 4.23  | 20000 | nan             | 1.0 |
| 0.0           | 4.76  | 22500 | nan             | 1.0 |
| 0.0           | 5.29  | 25000 | nan             | 1.0 |
| 0.0           | 5.82  | 27500 | nan             | 1.0 |
| 0.0           | 6.35  | 30000 | nan             | 1.0 |
| 0.0           | 6.87  | 32500 | nan             | 1.0 |
| 0.0           | 7.4   | 35000 | nan             | 1.0 |
| 0.0           | 7.93  | 37500 | nan             | 1.0 |
| 0.0           | 8.46  | 40000 | nan             | 1.0 |
| 0.0           | 8.99  | 42500 | nan             | 1.0 |
| 0.0           | 9.52  | 45000 | nan             | 1.0 |


### Framework versions


model name: wav2vec2-base-ami_single-vumichien2


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, en, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-tune_0.0001_16
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-lv60-ami_multi-tune_0.0001_16


This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 1.4974
- Wer: 0.4508


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.4959        | 1.72  | 1000 | 1.4430          | 0.4864 |
| 1.2059        | 3.45  | 2000 | 1.2716          | 0.4219 |
| 1.0863        | 5.17  | 3000 | 1.2448          | 0.4069 |
| 1.0271        | 6.9   | 4000 | 1.2464          | 0.3996 |
| 0.9656        | 8.62  | 5000 | 1.2551          | 0.4048 |


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.12.2.dev0
- Tokenizers 0.10.3


model name: wav2vec2-large-lv60-ami_multi-tune_0.0001_16


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-tune_0.00005_16
results: []


# wav2vec2-large-lv60-ami_multi-tune_0.00005_16


- Loss: 1.5257
- Wer: 0.4840


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.7983        | 1.72  | 1000 | 2.6819          | 0.9987 |
| 1.4           | 3.45  | 2000 | 1.3997          | 0.4810 |
| 1.2656        | 5.17  | 3000 | 1.3366          | 0.4491 |
| 1.2027        | 6.9   | 4000 | 1.3150          | 0.4385 |
| 1.1618        | 8.62  | 5000 | 1.3018          | 0.4348 |


### Framework versions


model name: wav2vec2-large-lv60-ami_multi-tune_0.00005_16


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-ami_multi-nithin2
results: []


# wav2vec2-base-ami_multi-nithin2


This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 2.3235
- Wer: 0.4971


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.7645        | 1.07  | 2500  | 3.0172          | 0.9979 |
| 2.0313        | 2.13  | 5000  | 2.0832          | 0.5786 |
| 1.9158        | 3.2   | 7500  | 1.9347          | 0.5201 |
| 1.8579        | 4.27  | 10000 | 2.1931          | 0.4882 |
| 1.8222        | 5.33  | 12500 | 2.1480          | 0.4706 |
| 1.7784        | 6.4   | 15000 | 2.0791          | 0.4638 |
| 1.7736        | 7.47  | 17500 | 2.0789          | 0.4590 |
| 1.7471        | 8.53  | 20000 | 2.1862          | 0.4533 |
| 1.7264        | 9.6   | 22500 | 2.0762          | 0.4543 |


### Framework versions


model name: wav2vec2-base-ami_multi-nithin2


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, en, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-ami_multi-nithin6
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-ami_multi-nithin6


- Loss: 1.7654
- Wer: 0.4952


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3222        | 4.31  | 2500  | 1.4875          | 0.5021 |
| 1.164         | 8.62  | 5000  | 1.4255          | 0.4816 |
| 1.0753        | 12.93 | 7500  | 1.4086          | 0.4717 |
| 0.9196        | 17.24 | 10000 | 1.4163          | 0.4695 |
| 0.8326        | 21.55 | 12500 | 1.5326          | 0.4650 |
| 0.7306        | 25.86 | 15000 | 1.5793          | 0.4670 |
| 0.5763        | 30.17 | 17500 | 1.7485          | 0.4728 |
| 0.4869        | 34.48 | 20000 | 1.9050          | 0.4797 |
| 0.4183        | 38.79 | 22500 | 2.1386          | 0.4835 |


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.12.2.dev0
- Tokenizers 0.10.3


model name: wav2vec2-base-ami_multi-nithin6


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-ami_multi-nithin5
results: []


# wav2vec2-base-ami_multi-nithin5


- Loss: 1.5392
- Wer: 0.4481


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.8839        | 2.16  | 2500  | 1.7172          | 0.6249 |
| 1.5323        | 4.31  | 5000  | 1.4628          | 0.4930 |
| 1.4325        | 6.47  | 7500  | 1.3856          | 0.4495 |
| 1.3461        | 8.62  | 10000 | 1.3695          | 0.4350 |
| 1.3249        | 10.78 | 12500 | 1.3640          | 0.4294 |
| 1.3288        | 12.93 | 15000 | 1.3429          | 0.4220 |
| 1.2503        | 15.09 | 17500 | 1.3325          | 0.4171 |
| 1.2587        | 17.24 | 20000 | 1.3201          | 0.4108 |
| 1.2135        | 19.4  | 22500 | 1.3329          | 0.4083 |
| 1.2154        | 21.55 | 25000 | 1.3341          | 0.4057 |
| 1.2162        | 23.71 | 27500 | 1.3291          | 0.4046 |
| 1.2062        | 25.86 | 30000 | 1.3305          | 0.4031 |
| 1.1853        | 28.02 | 32500 | 1.3299          | 0.4023 |


### Framework versions


model name: wav2vec2-base-ami_multi-nithin5


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-tune_0.00005_8
results: []


# wav2vec2-large-lv60-ami_multi-tune_0.00005_8


This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 1.4987
- Wer: 0.4569


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.8603        | 0.86  | 1000  | 2.8296          | 1.0    |
| 1.4382        | 1.72  | 2000  | 1.4212          | 0.4776 |
| 1.3           | 2.59  | 3000  | 1.3231          | 0.4330 |
| 1.2322        | 3.45  | 4000  | 1.2824          | 0.4251 |
| 1.1741        | 4.31  | 5000  | 1.2740          | 0.4187 |
| 1.1268        | 5.17  | 6000  | 1.2600          | 0.4161 |
| 1.0911        | 6.03  | 7000  | 1.2624          | 0.4076 |
| 1.0701        | 6.9   | 8000  | 1.2607          | 0.4076 |
| 1.0426        | 7.76  | 9000  | 1.2629          | 0.4091 |
| 1.0273        | 8.62  | 10000 | 1.2596          | 0.4117 |
| 1.0294        | 9.48  | 11000 | 1.2663          | 0.4077 |


### Framework versions


model name: wav2vec2-large-lv60-ami_multi-tune_0.00005_8


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, en, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-tune_0.0005_8
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-tune_0.0005_8


This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 1.5092
- Wer: 0.4821


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.5221        | 1.72  | 1000 | 1.6180          | 0.5266 |
| 1.3259        | 3.45  | 2000 | 1.4400          | 0.4921 |
| 1.1732        | 5.17  | 3000 | 1.3968          | 0.4669 |
| 1.0888        | 6.9   | 4000 | 1.3652          | 0.4569 |
| 0.9659        | 8.62  | 5000 | 1.3176          | 0.4332 |


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.12.2.dev0
- Tokenizers 0.10.3


model name: wav2vec2-base-ami_multi-tune_0.0005_8


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-nithin8
results: []


# wav2vec2-large-lv60-ami_multi-nithin8


- Loss: 1.4945
- Wer: 0.4291


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 40.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.336         | 2.16  | 2500  | 1.2807          | 0.4097 |
| 1.216         | 4.31  | 5000  | 1.2406          | 0.3931 |
| 1.1353        | 6.47  | 7500  | 1.2145          | 0.3801 |
| 1.0674        | 8.62  | 10000 | 1.1930          | 0.3825 |
| 1.0223        | 10.78 | 12500 | 1.2283          | 0.3907 |
| 1.009         | 12.93 | 15000 | 1.2266          | 0.3810 |
| 0.8998        | 15.09 | 17500 | 1.2719          | 0.3839 |
| 0.8912        | 17.24 | 20000 | 1.2889          | 0.3867 |
| 0.8459        | 19.4  | 22500 | 1.3031          | 0.3941 |
| 0.8193        | 21.55 | 25000 | 1.3543          | 0.3862 |
| 0.8048        | 23.71 | 27500 | 1.3533          | 0.3858 |
| 0.7663        | 25.86 | 30000 | 1.3941          | 0.3993 |
| 0.7311        | 28.02 | 32500 | 1.4745          | 0.3937 |
| 0.716         | 30.17 | 35000 | 1.4788          | 0.3989 |
| 0.6868        | 32.33 | 37500 | 1.4966          | 0.3925 |
| 0.6558        | 34.48 | 40000 | 1.5457          | 0.3901 |
| 0.6473        | 36.64 | 42500 | 1.5662          | 0.3944 |
| 0.631         | 38.79 | 45000 | 1.5689          | 0.3956 |


### Framework versions


model name: wav2vec2-large-lv60-ami_multi-nithin8


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-ami_multi-nithin0
results: []


# wav2vec2-base-ami_multi-nithin0


- Loss: 11.0605
- Wer: 1.0


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:---:|
| 3.1566        | 1.07  | 5000  | 9.5587          | 1.0 |
| 3.149         | 2.13  | 10000 | 9.0950          | 1.0 |
| 3.1518        | 3.2   | 15000 | 9.7352          | 1.0 |
| 3.1716        | 4.27  | 20000 | 9.0866          | 1.0 |
| 3.1611        | 5.33  | 25000 | 9.6718          | 1.0 |
| 3.1308        | 6.4   | 30000 | 9.6227          | 1.0 |
| 3.1762        | 7.46  | 35000 | 9.4326          | 1.0 |
| 3.1503        | 8.53  | 40000 | 9.7609          | 1.0 |
| 3.1591        | 9.6   | 45000 | 9.3503          | 1.0 |


### Framework versions


model name: wav2vec2-base-ami_multi-nithin0


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, en, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-tune_dropout_0.0001_8
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-lv60-ami_multi-tune_dropout_0.0001_8


This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 1.4880
- Wer: 0.4295


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.8417        | 0.86  | 1000  | 2.6883          | 0.9997 |
| 1.5626        | 1.72  | 2000  | 1.4253          | 0.4517 |
| 1.4476        | 2.59  | 3000  | 1.3356          | 0.4157 |
| 1.3874        | 3.45  | 4000  | 1.2814          | 0.4073 |
| 1.3391        | 4.31  | 5000  | 1.2700          | 0.4044 |
| 1.2983        | 5.17  | 6000  | 1.2423          | 0.3967 |
| 1.2618        | 6.03  | 7000  | 1.2429          | 0.3879 |
| 1.2414        | 6.9   | 8000  | 1.2290          | 0.3878 |
| 1.2286        | 7.76  | 9000  | 1.2301          | 0.3882 |
| 1.2254        | 8.62  | 10000 | 1.2140          | 0.3885 |
| 1.2257        | 9.48  | 11000 | 1.2154          | 0.3840 |


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.12.2.dev0
- Tokenizers 0.10.3


model name: wav2vec2-large-lv60-ami_multi-tune_dropout_0.0001_8


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-tune_dropout_0.0001_16
results: []


# wav2vec2-large-lv60-ami_multi-tune_dropout_0.0001_16


- Loss: 1.5093
- Wer: 0.4409


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.3734        | 1.72  | 1000 | 1.8809          | 0.7894 |
| 1.4869        | 3.45  | 2000 | 1.3396          | 0.4307 |
| 1.3855        | 5.17  | 3000 | 1.2935          | 0.4099 |
| 1.3272        | 6.9   | 4000 | 1.2572          | 0.4044 |
| 1.3057        | 8.62  | 5000 | 1.2504          | 0.3975 |


### Framework versions


model name: wav2vec2-large-lv60-ami_multi-tune_dropout_0.0001_16


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-ami_multi-nithin1
results: []


# wav2vec2-base-ami_multi-nithin1


This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 9.4710
- Wer: 1.0


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:---:|
| 3.1594        | 1.07  | 5000  | 8.2680          | 1.0 |
| 3.1647        | 2.13  | 10000 | 7.7283          | 1.0 |
| 3.152         | 3.2   | 15000 | 8.5267          | 1.0 |
| 3.1738        | 4.27  | 20000 | 7.8057          | 1.0 |
| 3.1628        | 5.33  | 25000 | 8.4358          | 1.0 |
| 3.1314        | 6.4   | 30000 | 8.2546          | 1.0 |
| 3.1772        | 7.46  | 35000 | 8.0952          | 1.0 |
| 3.1504        | 8.53  | 40000 | 8.4454          | 1.0 |
| 3.1598        | 9.6   | 45000 | 8.0497          | 1.0 |


### Framework versions


model name: wav2vec2-base-ami_multi-nithin1


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, en, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: ami-dummy
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. -->


# ami-dummy


- Loss: 94.6519
- Wer: 1.0


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- 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: 3.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| No log        | 2.46  | 15   | 102.9094        | 1.0 |


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.12.2.dev0
- Tokenizers 0.10.3


model name: ami-dummy


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-tune_0.0001_8
results: []


# wav2vec2-base-tune_0.0001_8


- Loss: 1.5750
- Wer: 0.4813


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.5458        | 1.72  | 1000 | 1.5351          | 0.5397 |
| 1.2552        | 3.45  | 2000 | 1.3582          | 0.4540 |
| 1.1246        | 5.17  | 3000 | 1.3412          | 0.4378 |
| 1.0614        | 6.9   | 4000 | 1.3356          | 0.4344 |
| 1.0007        | 8.62  | 5000 | 1.3410          | 0.4352 |


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.12.2.dev0
- Tokenizers 0.10.3


model name: wav2vec2-base-ami_multi-tune_0.0001_8


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-tune_0.0005_8
results: []


# wav2vec2-large-lv60-ami_multi-tune_0.0005_8


This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 1.5154
- Wer: 0.4442


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.4248        | 0.86  | 1000  | 1.4213          | 0.4710 |
| 1.2635        | 1.72  | 2000  | 1.2974          | 0.4288 |
| 1.1501        | 2.59  | 3000  | 1.2381          | 0.4075 |
| 1.0783        | 3.45  | 4000  | 1.2177          | 0.4130 |
| 0.9888        | 4.31  | 5000  | 1.2388          | 0.3998 |
| 0.9058        | 5.17  | 6000  | 1.2037          | 0.4010 |
| 0.8678        | 6.03  | 7000  | 1.2275          | 0.4018 |
| 0.8245        | 6.9   | 8000  | 1.2243          | 0.3940 |
| 0.762         | 7.76  | 9000  | 1.2395          | 0.3999 |
| 0.6929        | 8.62  | 10000 | 1.2715          | 0.4065 |
| 0.6617        | 9.48  | 11000 | 1.3128          | 0.4046 |


### Framework versions


model name: wav2vec2-large-lv60-ami_multi-tune_0.0005_8


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, en, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


language:


- en
license: apache-2.0
datasets:
- ami
metrics:
- wer
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-ami_single-vumichien
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-ami_single-vumichien


This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: nan
- Wer: 1.0


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:---:|
| 0.0           | 1.06  | 2500  | nan             | 1.0 |
| 0.0           | 2.12  | 5000  | nan             | 1.0 |
| 0.0           | 3.17  | 7500  | nan             | 1.0 |
| 0.0           | 4.23  | 10000 | nan             | 1.0 |
| 0.0           | 5.29  | 12500 | nan             | 1.0 |
| 0.0           | 6.35  | 15000 | nan             | 1.0 |
| 0.0           | 7.4   | 17500 | nan             | 1.0 |
| 0.0           | 8.46  | 20000 | nan             | 1.0 |
| 0.0           | 9.52  | 22500 | nan             | 1.0 |
| 0.0           | 10.58 | 25000 | nan             | 1.0 |
| 0.0           | 11.63 | 27500 | nan             | 1.0 |
| 0.0           | 12.69 | 30000 | nan             | 1.0 |
| 0.0           | 13.75 | 32500 | nan             | 1.0 |
| 0.0           | 14.81 | 35000 | nan             | 1.0 |
| 0.0           | 15.86 | 37500 | nan             | 1.0 |
| 0.0           | 16.92 | 40000 | nan             | 1.0 |
| 0.0           | 17.98 | 42500 | nan             | 1.0 |
| 0.0           | 19.04 | 45000 | nan             | 1.0 |
| 0.0           | 20.09 | 47500 | nan             | 1.0 |
| 0.0           | 21.15 | 50000 | nan             | 1.0 |
| 0.0           | 22.21 | 52500 | nan             | 1.0 |
| 0.0           | 23.27 | 55000 | nan             | 1.0 |
| 0.0           | 24.32 | 57500 | nan             | 1.0 |
| 0.0           | 25.38 | 60000 | nan             | 1.0 |
| 0.0           | 26.44 | 62500 | nan             | 1.0 |
| 0.0           | 27.5  | 65000 | nan             | 1.0 |
| 0.0           | 28.55 | 67500 | nan             | 1.0 |
| 0.0           | 29.61 | 70000 | nan             | 1.0 |


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.13.3
- Tokenizers 0.10.3


model name: wav2vec2-base-ami_single-vumichien


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, en, dataset:ami, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-tune_0.00005_8
results: []


# wav2vec2-base-tune_0.00005_8


- Loss: 1.5701
- Wer: 0.4927


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.8189        | 1.72  | 1000 | 1.7820          | 0.6588 |
| 1.3459        | 3.45  | 2000 | 1.4136          | 0.4750 |
| 1.2262        | 5.17  | 3000 | 1.3611          | 0.4546 |
| 1.1661        | 6.9   | 4000 | 1.3832          | 0.4610 |
| 1.122         | 8.62  | 5000 | 1.3735          | 0.4485 |


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.12.2.dev0
- Tokenizers 0.10.3


model name: wav2vec2-base-ami_multi-tune_0.00005_8


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-tune_0.0001_8
results: []


# wav2vec2-large-lv60-ami_multi-tune_0.0001_8


This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 1.5037
- Wer: 0.4342


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.5659        | 0.86  | 1000  | 1.4888          | 0.5013 |
| 1.2886        | 1.72  | 2000  | 1.2864          | 0.4171 |
| 1.1701        | 2.59  | 3000  | 1.2319          | 0.3958 |
| 1.108         | 3.45  | 4000  | 1.2009          | 0.4006 |
| 1.0407        | 4.31  | 5000  | 1.2137          | 0.3888 |
| 0.9785        | 5.17  | 6000  | 1.2017          | 0.3927 |
| 0.948         | 6.03  | 7000  | 1.2107          | 0.3952 |
| 0.9191        | 6.9   | 8000  | 1.2195          | 0.3867 |
| 0.8844        | 7.76  | 9000  | 1.2227          | 0.3901 |
| 0.8538        | 8.62  | 10000 | 1.2389          | 0.3968 |
| 0.854         | 9.48  | 11000 | 1.2514          | 0.3939 |


### Framework versions


model name: wav2vec2-large-lv60-ami_multi-tune_0.0001_8


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, en, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-ami_multi-nithin4
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-ami_multi-nithin4


This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 2.0790
- Wer: 0.4478


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.8893        | 1.07  | 2500  | 3.7944          | 1.0000 |
| 2.0331        | 2.13  | 5000  | 2.0323          | 0.5840 |
| 1.9009        | 3.2   | 7500  | 1.8876          | 0.5173 |
| 1.8367        | 4.27  | 10000 | 2.1239          | 0.4847 |
| 1.8007        | 5.33  | 12500 | 1.9126          | 0.4684 |
| 1.743         | 6.4   | 15000 | 2.0750          | 0.4570 |
| 1.7329        | 7.47  | 17500 | 1.9226          | 0.4460 |
| 1.7013        | 8.53  | 20000 | 1.9677          | 0.4392 |
| 1.6674        | 9.6   | 22500 | 1.9064          | 0.4360 |
| 1.6568        | 10.67 | 25000 | 1.8144          | 0.4304 |
| 1.6507        | 11.73 | 27500 | 1.8881          | 0.4248 |
| 1.5973        | 12.8  | 30000 | 1.7907          | 0.4267 |
| 1.6316        | 13.87 | 32500 | 1.7567          | 0.4207 |
| 1.6053        | 14.93 | 35000 | 1.7838          | 0.4192 |
| 1.599         | 16.0  | 37500 | 1.8054          | 0.4181 |
| 1.5629        | 17.06 | 40000 | 1.7739          | 0.4135 |
| 1.6124        | 18.13 | 42500 | 2.0690          | 0.4138 |
| 1.5623        | 19.2  | 45000 | 1.9308          | 0.4144 |
| 1.5524        | 20.26 | 47500 | 1.8130          | 0.4121 |
| 1.5654        | 21.33 | 50000 | 1.8344          | 0.4131 |
| 1.5552        | 22.4  | 52500 | 1.9365          | 0.4116 |
| 1.5357        | 23.46 | 55000 | 1.9330          | 0.4114 |
| 1.534         | 24.53 | 57500 | 1.8155          | 0.4079 |
| 1.5333        | 25.6  | 60000 | 1.7895          | 0.4069 |
| 1.5315        | 26.66 | 62500 | 1.7903          | 0.4082 |
| 1.5174        | 27.73 | 65000 | 1.8356          | 0.4080 |
| 1.5209        | 28.8  | 67500 | 1.8147          | 0.4077 |
| 1.5696        | 29.86 | 70000 | 1.8219          | 0.4076 |


### Framework versions


model name: wav2vec2-base-ami_multi-nithin4


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-tune_0.0005_16
results: []


# wav2vec2-large-lv60-ami_multi-tune_0.0005_16


- Loss: 1.4670
- Wer: 0.4379


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.2775        | 1.72  | 1000 | 1.2890          | 0.4219 |
| 1.0728        | 3.45  | 2000 | 1.2016          | 0.4005 |
| 0.9245        | 5.17  | 3000 | 1.1885          | 0.3961 |
| 0.8506        | 6.9   | 4000 | 1.2045          | 0.3909 |
| 0.7202        | 8.62  | 5000 | 1.2507          | 0.3944 |


### Framework versions


- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.12.2.dev0
- Tokenizers 0.10.3


model name: wav2vec2-large-lv60-ami_multi-tune_0.0005_16


model author: ami-wav2vec2


task: automatic-speech-recognition


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-tune_dropout_0.0003_16
results: []


# wav2vec2-large-lv60-ami_multi-tune_dropout_0.0003_16


This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 1.4765
- Wer: 0.4223


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.5073        | 1.72  | 1000 | 1.3621          | 0.4369 |
| 1.3054        | 3.45  | 2000 | 1.2400          | 0.4000 |
| 1.2056        | 5.17  | 3000 | 1.2068          | 0.3876 |
| 1.1534        | 6.9   | 4000 | 1.1915          | 0.3816 |
| 1.1094        | 8.62  | 5000 | 1.1872          | 0.3770 |


### Framework versions


model name: wav2vec2-large-lv60-ami_multi-tune_dropout_0.0003_16


model author: ami-wav2vec2


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, en, transformers, license:apache-2.0, ami, generated_from_trainer, model-index


language:


- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-tune_0.0005_4
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# wav2vec2-base-tune_0.0005_4


This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:


- Loss: 3.9286
- Wer: 1.0


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:---:|
| 3.0115        | 0.86  | 1000  | 3.8103          | 1.0 |
| 2.9818        | 1.72  | 2000  | 3.6096          | 1.0 |
| 2.9991        | 2.59  | 3000  | 3.6555          | 1.0 |
| 2.9914        | 3.45  | 4000  | 3.6829          | 1.0 |
| 2.9958        | 4.31  | 5000  | 3.5873          | 1.0 |
| 2.9921        | 5.17  | 6000  | 3.5026          | 1.0 |
| 3.0256        | 6.03  | 7000  | 3.5531          | 1.0 |
| 2.9892        | 6.9   | 8000  | 3.6803          | 1.0 |
| 2.9994        | 7.76  | 9000  | 3.5720          | 1.0 |
| 2.9796        | 8.62  | 10000 | 3.6583          | 1.0 |
| 2.9837        | 9.48  | 11000 | 3.6397          | 1.0 |


### Framework versions


model name: wav2vec2-base-ami_multi-tune_0.0005_4


model author: ami-wav2vec2


task: automatic-speech-recognition


The uploaded model is from epoch 4 with Matthews Correlation of 61.05


"best_metric": 0.4796141982078552,

"best_model_checkpoint": "/content/output_dir/checkpoint-268",

"epoch": 10.0,

"global_step": 2680,

"is_hyper_param_search": false,

"is_local_process_zero": true,

"is_world_process_zero": true,

"max_steps": 2680,

"num_train_epochs": 10,

"total_flos": 7113018526540800.0,

"trial_name": null,

"trial_params": null


epoch
eval_loss
eval_matthews_correlation
eval_runtime
eval_samples_per_second
eval_steps_per_second
step
learning_rate
loss

1
0.4796141982078552
0.5351033849356494
8.8067
118.433
14.875
268
0.000018067415730337083
0.4913

2
0.5334435701370239
0.5178799252679331
8.9439
116.616
14.647
536
0.00001605992509363296
0.2872

3
0.5544090270996094
0.5649788851042796
8.9467
116.58
14.642
804
0.000014052434456928841
0.1777

4
0.5754779577255249
0.6105374636148787
8.8982
117.215
14.722
1072
0.000012044943820224718
0.1263

5
0.7263916730880737
0.5807606001872874
8.9705
116.27
14.603
1340
0.000010037453183520601
0.0905

6
0.8121512532234192
0.5651092792103851
8.9924
115.987
14.568
1608
0.00000802996254681648
0.0692

7
0.941014289855957
0.5632084517291658
8.9583
116.428
14.623
1876
0.000006022471910112359
0.0413

8
1.0095174312591553
0.5856531698367675
9.0029
115.851
14.551
2144
0.00000401498127340824
0.0327

9
1.0425965785980225
0.5941395545037332
8.9217
116.906
14.683
2412
0.00000200749063670412
0.0202

10
1.0782166719436646
0.5956649094312695
8.9472
116.572
14.641
2680
0
0.0104


model name: bert-base-cased-avg-cola


model author: TehranNLP-org


task: text-classification


tags: pytorch, bert, text-classification, transformers, infinity_compatible


The uploaded model is from epoch 9 with Matthews Correlation of 66.77


"best_metric": 0.667660908939119,

"best_model_checkpoint": "/content/output_dir/checkpoint-2412",

"epoch": 10.0,

"global_step": 2680,

"is_hyper_param_search": false,

"is_local_process_zero": true,

"is_world_process_zero": true,

"max_steps": 2680,

"num_train_epochs": 10,

"total_flos": 7189983634007040.0,

"trial_name": null,

"trial_params": null


epoch
eval_loss
eval_matthews_correlation
eval_runtime
eval_samples_per_second
eval_steps_per_second
step
learning_rate
loss

1
0.5115634202957153
0.5385290213636863
7.985
130.62
16.406
268
0.00009280492497114274
0.4622

2
0.4201788902282715
0.6035894895952164
8.0283
129.916
16.317
536
0.00008249326664101577
0.2823

3
0.580650806427002
0.5574138665741355
8.1314
128.268
16.11
804
0.00007218160831088881
0.1804

4
0.4439031779766083
0.6557697896854868
8.1435
128.078
16.087
1072
0.00006186994998076183
0.1357

5
0.5736830830574036
0.6249925495853809
8.0533
129.512
16.267
1340
0.00005155829165063486
0.0913

6
0.7729296684265137
0.6188970025554703
8.081
129.068
16.211
1608
0.000041246633320507885
0.065

7
0.7351673245429993
0.6405767700619004
8.1372
128.176
16.099
1876
0.00003093497499038092
0.0433

8
0.7900031208992004
0.6565021466238845
8.1095
128.615
16.154
2144
0.000020623316660253942
0.0199

9
0.8539554476737976
0.667660908939119
8.1204
128.442
16.132
2412
0.000010311658330126971
0.0114

10
0.9261117577552795
0.660301076782038
8.0088
130.231
16.357
2680
0
0.0066


model name: electra-base-avg-cola


tags: pytorch, electra, text-classification, transformers, infinity_compatible


ktrain predictor for NER of ADR in patient forum discussions. Created in transformers > 3.0 and < 4. 0 & ktrain 0.19.9.


model name: ADR_extraction_patient_forum


model author: annedirkson


tags: tf


language: it
license: mit
tags:


- sentiment
- Italian


# FEEL-IT: Emotion and Sentiment Classification for the Italian Language


## FEEL-IT Python Package


You can find the package that uses this model for emotion and sentiment classification **[here](https://github.com/MilaNLProc/feel-it)** it is meant to be a very simple interface over HuggingFace models.


## Abstract


Sentiment analysis is a common task to understand people's reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad?


An abundance of approaches has been introduced for tackling both tasks. However, at least for Italian, they all treat only one of the tasks at a time. We introduce *FEEL-IT*, a novel benchmark corpus of Italian Twitter posts annotated with four basic emotions: **anger, fear, joy, sadness**. By collapsing them, we can also do **sentiment analysis**. We evaluate our corpus on benchmark datasets for both emotion and sentiment classification, obtaining competitive results.


We release an [open-source Python library](https://github.com/MilaNLProc/feel-it), so researchers can use a model trained on FEEL-IT for inferring both sentiments and emotions from Italian text.


| Model                       | Download |
| ------                      | -------------------------|
| `feel-it-italian-sentiment` | [Link](https://huggingface.co/MilaNLProc/feel-it-italian-sentiment) |
| `feel-it-italian-emotion`   | [Link](https://huggingface.co/MilaNLProc/feel-it-italian-emotion) |


## Model


The *feel-it-italian-sentiment* model performs **sentiment analysis** on Italian. We fine-tuned the [UmBERTo model](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1) on our new dataset (i.e., FEEL-IT) obtaining state-of-the-art performances on different benchmark corpora.


## Data


Our data has been collected by annotating tweets from a broad range of topics. In total, we have 2037 tweets annotated with an emotion label. More details can be found in our paper (preprint available soon).


## Performance


We evaluate our performance using [SENTIPOLC16 Evalita](http://www.di.unito.it/~tutreeb/sentipolc-evalita16/). We collapsed the FEEL-IT classes into 2 by mapping joy to the *positive* class and anger, fear and sadness into the *negative* class. We compare three different experimental configurations training on FEEL-IT, SENTIPOLC16, or both by testing on the SENTIPOLC16 test set.
The results show that training on FEEL-IT can provide better results on the SENTIPOLC16 test set than those that can be obtained with the SENTIPOLC16 training set.


| Training Dataset | Macro-F1 | Accuracy
| ------ | ------ |------ |
| SENTIPOLC16 | 0.80 | 0.81 |
| FEEL-IT | **0.81** | **0.84**  |
| FEEL-IT+SentiPolc | 0.81 | 0.82


## Usage


```python
import torch
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-sentiment")

sentence = 'Oggi sono proprio contento!'
inputs = tokenizer(sentence, return_tensors="pt")

# Call the model and get the logits
labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
outputs = model(**inputs, labels=labels)
loss, logits = outputs[:2]
logits = logits.squeeze(0)

# Extract probabilities
proba = torch.nn.functional.softmax(logits, dim=0)

# Unpack the tensor to obtain negative and positive probabilities
negative, positive = proba
print(f"Probabilities: Negative {np.round(negative.item(),4)} - Positive {np.round(positive.item(),4)}")
```


## Citation


Please use the following bibtex entry if you use this model in your project:


```
@inproceedings{bianchi2021feel,
    title = {{"FEEL-IT: Emotion and Sentiment Classification for the Italian Language"}},
    author = "Bianchi, Federico and Nozza, Debora and Hovy, Dirk",
    booktitle = "Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    year = "2021",
    publisher = "Association for Computational Linguistics",
}
```


model name: feel-it-italian-sentiment


model author: MilaNLProc


task: text-classification


tags: pytorch, tf, camembert, text-classification, it, transformers, license:mit, sentiment, Italian


language: it


license: mit


tags:


- sentiment
- emotion
- Italian


## FEEL-IT Python Package


## Abstract


## Model


The *feel-it-italian-emotion* model performs **emotion classification (joy, fear, anger, sadness)** on Italian. We fine-tuned the [UmBERTo model](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1) on our new dataset (i.e., FEEL-IT) obtaining state-of-the-art performances on different benchmark corpora.


## Data


## Performance


We evaluate our performance using [MultiEmotions-It](http://ceur-ws.org/Vol-2769/paper_08.pdf). This dataset differs from FEEL-IT both in terms of topic variety and considered social media (i.e., YouTube and Facebook). We considered only the subset of emotions present in FEEL-IT. To give a point of reference, we also show the Most Frequent Class (MFC) baseline results. The results show that training on FEEL-IT brings stable performance even on datasets from different contexts.


| Training Dataset | Macro-F1 | Accuracy
| ------ | ------ |------ |
| MFC | 0.20 | 0.64  |
| FEEL-IT | **0.57** | **0.73**  |


## Usage


```python

import torch
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-emotion")
model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-emotion")

sentence = 'Oggi sono proprio contento!'
inputs = tokenizer(sentence, return_tensors="pt")

# Call the model and get the logits
labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
outputs = model(**inputs, labels=labels)
loss, logits = outputs[:2]
logits = logits.squeeze(0)

# Extract probabilities
proba = torch.nn.functional.softmax(logits, dim=0)
proba = np.array(proba.detach().numpy())

# Obtain emotion probabilities (sorted)
label_names = ['anger', 'fear', 'joy', 'sadness']
ranking = np.argsort(proba)
ranking = ranking[::-1]
for i in range(proba.shape[0]):
    l = label_names[ranking[i]]
    s = proba[ranking[i]]
    print(f"{l} {np.round(float(s), 4)}")

```


## Citation


model name: feel-it-italian-emotion


model author: MilaNLProc


task: text-classification


tags: pytorch, tf, camembert, text-classification, it, transformers, license:mit, sentiment, emotion, Italian


tags:


- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3


# TODO: Fill this model card


model name: ppo-coinrun-1M


model author: ThomasSimonini


tags: stable-baselines3, deep-reinforcement-learning, reinforcement-learning


# ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4


This is a pre-trained model of a PPO agent playing SpaceInvadersNoFrameskip using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library. It is taken from [RL-trained-agents](https://github.com/DLR-RM/rl-trained-agents)


<video src="https://huggingface.co/ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4/resolve/main/output.mp4" controls autoplay loop></video>


### Usage (with Stable-baselines3)


Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:


```
pip install stable-baselines3
pip install huggingface_sb3
```


Then, you can use the model like this:


```python
import gym

from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_atari_env
from stable_baselines3.common.vec_env import VecFrameStack

# Retrieve the model from the hub
## repo_id =  id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(repo_id="ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4", filename="ppo-SpaceInvadersNoFrameskip-v4.zip")
model = PPO.load(checkpoint)
```


### Evaluation Results


Mean_reward: 627.160 (162 eval episodes)


model name: ppo-SpaceInvadersNoFrameskip-v4


license: apache-2.0
tags:


- deep-reinforcement-learning
- reinforcement-learning
- mlagents


environment:


- MLAgents: Snowballfight-1vs1-ppo


model-index:


- name: mlagents-snowballfight-1vs1-ppo


# mlagents-snowballfight-1vs1-ppo ☃


This is a saved model of a PPO 1vs1 agent playing Snowball Fight.


model name: mlagents-snowballfight-1vs1-ppo


model author: ThomasSimonini


tags: license:apache-2.0, deep-reinforcement-learning, reinforcement-learning, mlagents, model-index


- deep-reinforcement-learning
- reinforcement-learning


- OpenAI Gym: CartPole-v1


- name: stable-baselines3-ppo-CartPole-v1


# stable-baselines3-ppo-CartPole-v1


This is a saved model of a PPO agent playing [CartPole-v1](https://gym.openai.com/envs/CartPole-v1/). The model is taken from [rl-baselines3-zoo](https://github.com/DLR-RM/rl-trained-agents)


The pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remains upright. The episode ends when the pole is more than 15 degrees from vertical, or the cart moves more than 2.4 units from the center.


<iframe width="560" height="315" src="https://www.youtube.com/embed/D9sQT_R_k88" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>


👉 You can watch the agent playing by using this [notebook](https://colab.research.google.com/drive/19OonMRkMyCH6Dg0ECFQi7evxMRqkW3U0?usp=sharing)


## Use the Model


### Install the dependencies


You need to use the [Stable Baselines 3 Hugging Face version](https://github.com/simoninithomas/stable-baselines3) of the library (this version contains the function to load saved models directly from the Hugging Face Hub):


```
pip install git+https://github.com/simoninithomas/stable-baselines3.git
```


### Evaluate the agent


```
# Import the libraries
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy

# Load the environment
env = gym.make('CartPole-v1')

model = PPO.load_from_huggingface(hf_model_id="ThomasSimonini/stable-baselines3-ppo-CartPole-v1",hf_model_filename="CartPole-v1")

# Evaluate the agent
eval_env = gym.make('CartPole-v1')
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

# Watch the agent play
obs = env.reset()
for i in range(1000):
    action, _state = model.predict(obs)
    obs, reward, done, info = env.step(action)
    env.render()
    if done:
      obs = env.reset()
```


## Results


Mean Reward (10 evaluation episodes): 500.00 +/- 0.0


model name: stable-baselines3-ppo-CartPole-v1


model author: ThomasSimonini


tags: license:apache-2.0, deep-reinforcement-learning, reinforcement-learning, model-index


license: apache-2.0
tags:


environment:


- OpenAI Gym Atari: SpaceInvadersNoFrameskip-v4


model-index:


- name: stable-baselines3-ppo-SpaceInvadersNoFrameskip-v4


# stable-baselines3-ppo-SpaceInvadersNoFrameskip-v4 👾


This is a saved model of a PPO agent playing Space Invaders. The model is taken from [rl-baselines3-zoo](https://github.com/DLR-RM/rl-trained-agents)


<iframe width="560" height="315" src="https://www.youtube.com/embed/CLCC5QNKr-w" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>


👉 You can watch the agent playing by using this [notebook](https://colab.research.google.com/drive/1kRzq3jxihVcA6YOzHqJTzdy0ge9ph01N?usp=sharing)


## Use the Model


### Install the dependencies


You also need to add the Atari ROMS (game files) to be able to run the environment.


```
wget http://www.atarimania.com/roms/Roms.rar
mkdir /content/ROM/
unrar e /content/Roms.rar /content/ROM/
python -m atari_py.import_roms /content/ROM/
```


👉 To visualize the agent playing, use the [notebook](https://colab.research.google.com/drive/1kRzq3jxihVcA6YOzHqJTzdy0ge9ph01N?usp=sharing)


model name: stable-baselines3-ppo-SpaceInvadersNoFrameskip-v4


model author: ThomasSimonini


- deep-reinforcement-learning
- reinforcement-learning
- ml-agents


- SnowballFight-1vs1


# Snowball Fight ☃, a multi-agent environment for ML-Agents made by Hugging Face


![Snowball Fight 1vs1](http://simoninithomas.com/hf/snowballfight.gif)
A multi-agent environment using Unity ML-Agents Toolkit where two agents compete in a 1vs1 snowball fight game.


👉 You can [play  it online at this link](https://huggingface.co/spaces/ThomasSimonini/SnowballFight).


⚠ You need to have some skills in ML-Agents if you want to use it if it's not the case [check the documentation](https://github.com/Unity-Technologies/ml-agents/tree/main/docs)


## The Environment


- Two agents compete **in a 1 vs 1 snowball fight game**.
- The goal is to **hit the opponent team while avoiding the opponent's snowballs ❄**.


### Observation Space


- Ray-casts:
  - **10 ray-casts forward** distributed over 100 degrees: detecting opponent.
  - **10 ray-casts forward** distributed over 100 degrees: detecting walls, shelter and frontier.
  - **10 ray-casts forward** distributed over 100 degrees: detecting snowballs.
  - **3 ray-casts backward** distributed over 45 degrees: detecting wall and shelter.
- Vector Observations:
  - **Bool canShoot** (you can only shoot a snowball every 2 seconds).
  - **Float currentHealth**: normalized [0, 1]
  - **Vector3 vertical speed**
  - **Vector3 horizontal speed**
  - **Vector3 "home" position**


### Action Space (Discrete)


- Vector Action space:
  - **Four branched actions** corresponding to forward, backward, sideways movement, rotation, and snowball shoot.


### Agent Reward Function (dependant):


- If the team is **injured**:
  - 0.1 to the shooter.
- If the team is **dead**:
  - (1 - accumulated time penalty): when a snowball hits the
opponent, the accumulated time penalty decreases by (1 / MaxStep) every fixed update and is reset to 0 at the beginning of an episode.
  - (-1) When a snowball hit our team.


### Addendum


- There **is no friendly fire**, which means that an agent can't shoot himself, or in the future, in a 2vs2 game can't shoot a teammate.


## How to use it


### Set-up the environment


1. Clone this project `git clone https://huggingface.co/ThomasSimonini/ML-Agents-SnowballFight-1vs1`
1. Open Unity Hub and create a new 3D Project
1. In the cloned project folder, open `.\ML-Agents-SnowballFight-1vs1\packages` and copy manifest.json and package.lock.json
1. Paste these two files in `Your Unity Project\Packages` => this will install the required packages.
1. Drop the SnowballFight-1vs1 unity package to your Unity Project.


### Watch the trained agents


6. If you want to watch the trained agents, open `Assets\1vs1\Scenes\1vs1_v2_Training.` place the `\ML-Agents-SnowballFight-1vs1\saved_model\SnowballFight1vs1-4999988.onnx` into BlueAgent and PurpleAgent Model.


### Train, the agent


6. If you want to train it again, the scene is `Assets\1vs1\Scenes\1vs1_v2_Training.`


## Training info


- SnowballFight1vs1 was trained with 5100000 steps.
- The final ELO score was 1766.452.


### Config File


`behaviors: SnowballFight1vs1: trainer_type: ppo hyperparameters: batch_size: 2048 buffer_size: 20480 learning_rate: 0.0003 beta: 0.005 epsilon: 0.2 lambd: 0.95 num_epoch: 3 learning_rate_schedule: constant network_settings: normalize: false hidden_units: 512 num_layers: 2 vis_encode_type: simple reward_signals: extrinsic: gamma: 0.99 strength: 1.0 keep_checkpoints: 40 checkpoint_interval: 200000 max_steps: 50000000 time_horizon: 1000 summary_freq: 50000 self_play: save_steps: 50000 team_change: 200000 swap_steps: 2000 window: 10 play_against_latest_model_ratio: 0.5 initial_elo: 1200.0 `


model name: ML-Agents-SnowballFight-1vs1


model author: ThomasSimonini


tags: onnx, license:apache-2.0, deep-reinforcement-learning, reinforcement-learning, ml-agents


tags:


- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3


# ppo-Walker2DBulletEnv-v0


This is a pre-trained model of a PPO agent playing Walker2DBulletEnv-v0 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.


<video src="https://huggingface.co/ThomasSimonini/ppo-Walker2DBulletEnv-v0/resolve/main/output.mp4" controls autoplay loop></video>


### Usage (with Stable-baselines3)


Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:


```
pip install stable-baselines3
pip install huggingface_sb3
```


Then, you can use the model like this:


```python

import gym
import pybullet_envs

from huggingface_sb3 import load_from_hub

from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
from stable_baselines3.common.evaluation import evaluate_policy

# Retrieve the model from the hub
## repo_id =  id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
repo_id = "ThomasSimonini/ppo-Walker2DBulletEnv-v0"
checkpoint = load_from_hub(repo_id = repo_id, filename="ppo-Walker2DBulletEnv-v0.zip")
model = PPO.load(checkpoint)

# Load the saved statistics
stats_path = load_from_hub(repo_id = repo_id, filename="vec_normalize.pkl")

eval_env = DummyVecEnv([lambda: gym.make("Walker2DBulletEnv-v0")])
eval_env = VecNormalize.load(stats_path, eval_env)
#  do not update them at test time
eval_env.training = False
# reward normalization is not needed at test time
eval_env.norm_reward = False

from stable_baselines3.common.evaluation import evaluate_policy

mean_reward, std_reward = evaluate_policy(model, eval_env)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")

```


### Evaluation Results


Mean_reward: 2371.90 +/- 16.50


model name: ppo-Walker2DBulletEnv-v0


tags: stable-baselines3, deep-reinforcement-learning, reinforcement-learning


# TODO: Fill this model card


model name: demo-hf-CartPole-v1


model author: ThomasSimonini


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-end2end-question-generation
results:
  - task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: squad
type: squad
args: plain_text


# t5-end2end-question-generation


This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad dataset to generate questions based on a context.


👉 If you want to learn how to fine-tune the t5 model to do the same, you can follow this [tutorial](https://colab.research.google.com/drive/1z-Zl2hftMrFXabYfmz8o9YZpgYx6sGeW?usp=sharing)


For instance:


```
Context: "Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace."
```


```
Questions:

    Who created Python?,
    When was Python first released?
    What is Python's design philosophy?
```


It achieves the following results on the evaluation set:


- Loss: 1.5691


## Use the Model


```
from transformers import T5ForConditionalGeneration, T5TokenizerFast

hfmodel = T5ForConditionalGeneration.from_pretrained("ThomasSimonini/t5-end2end-question-generation")

text= "The abolition of feudal privileges by the National Constituent Assembly on 4 August 1789 and the Declaration \\nof the Rights of Man and of the Citizen (La Déclaration des Droits de l'Homme et du Citoyen), drafted by Lafayette \\nwith the help of Thomas Jefferson and adopted on 26 August, paved the way to a Constitutional Monarchy \\n(4 September 1791 – 21 September 1792). Despite these dramatic changes, life at the court continued, while the situation \\nin Paris was becoming critical because of bread shortages in September. On 5 October 1789, a crowd from Paris descended upon Versailles \\nand forced the royal family to move to the Tuileries Palace in Paris, where they lived under a form of house arrest under \\nthe watch of Lafayette's Garde Nationale, while the Comte de Provence and his wife were allowed to reside in the \\nPetit Luxembourg, where they remained until they went into exile on 20 June 1791."

def run_model(input_string, **generator_args):
  generator_args = {
  "max_length": 256,
  "num_beams": 4,
  "length_penalty": 1.5,
  "no_repeat_ngram_size": 3,
  "early_stopping": True,
  }
  input_string = "generate questions: " + input_string + " </s>"
  input_ids = tokenizer.encode(input_string, return_tensors="pt")
  res = hfmodel.generate(input_ids, **generator_args)
  output = tokenizer.batch_decode(res, skip_special_tokens=True)
  output = [item.split("<sep>") for item in output]
  return output

run_model(text)

=> [['When did the National Constituent Assembly abolish feudal privileges?',
  ' Who drafted the Declaration of the Rights of Man and of the Citizen?',
  ' When was the Constitutional Monarchy established?',
  ' What was the name of the Declaration that paved the way to a constitutional monarchy?',
  '']]

```


### 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: 7


### Training results


| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5834        | 0.34  | 100  | 1.9107          |
| 1.9642        | 0.68  | 200  | 1.7227          |
| 1.8526        | 1.02  | 300  | 1.6627          |
| 1.7383        | 1.36  | 400  | 1.6354          |
| 1.7223        | 1.69  | 500  | 1.6154          |
| 1.6871        | 2.03  | 600  | 1.6096          |
| 1.6309        | 2.37  | 700  | 1.6048          |
| 1.6242        | 2.71  | 800  | 1.5923          |
| 1.6226        | 3.05  | 900  | 1.5855          |
| 1.5645        | 3.39  | 1000 | 1.5874          |
| 1.5705        | 3.73  | 1100 | 1.5822          |
| 1.5543        | 4.07  | 1200 | 1.5817          |
| 1.5284        | 4.41  | 1300 | 1.5841          |
| 1.5275        | 4.75  | 1400 | 1.5741          |
| 1.5269        | 5.08  | 1500 | 1.5715          |
| 1.5079        | 5.42  | 1600 | 1.5701          |
| 1.4876        | 5.76  | 1700 | 1.5754          |
| 1.498         | 6.1   | 1800 | 1.5699          |
| 1.4852        | 6.44  | 1900 | 1.5693          |
| 1.4776        | 6.78  | 2000 | 1.5691          |


### Framework versions


- Transformers 4.10.3
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


model name: t5-end2end-question-generation


model author: ThomasSimonini


task: text2text-generation


tags: pytorch, t5, text2text-generation, dataset:squad, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible


tags:


- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3


# ppo-LunarLander-v2


This is a pre-trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
<video src="https://huggingface.co/ThomasSimonini/ppo-LunarLander-v2/resolve/main/output.mp4" controls autoplay loop/>


### Usage (with Stable-baselines3)


Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:


```
pip install stable-baselines3
pip install huggingface_sb3
```


Then, you can use the model like this:


```python
import gym

from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy

# Retrieve the model from the hub
## repo_id =  id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(repo_id="ThomasSimonini/ppo-LunarLander-v2", filename="ppo-LunarLander-v2.zip")
model = PPO.load(checkpoint)

# Evaluate the agent
eval_env = gym.make('LunarLander-v2')
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

# Watch the agent play
obs = eval_env.reset()
for i in range(1000):
    action, _state = model.predict(obs)
    obs, reward, done, info = eval_env.step(action)
    eval_env.render()
    if done:
        obs = eval_env.reset()
eval_env.close()
```


### Evaluation Results


Mean_reward: 241.94 +/- 23.6


model name: ppo-LunarLander-v2


tags: stable-baselines3, deep-reinforcement-learning, reinforcement-learning


# ppo-Walker2DBulletEnv-v0


This is a pre-trained model of a PPO agent playing AntBulletEnv-v0 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.


<video src="https://huggingface.co/ThomasSimonini/ppo-AntBulletEnv-v0/resolve/main/output.mp4" controls autoplay loop></video>


```python

import gym
import pybullet_envs

from huggingface_sb3 import load_from_hub

from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
from stable_baselines3.common.evaluation import evaluate_policy

# Retrieve the model from the hub
## repo_id =  id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
repo_id = "ThomasSimonini/ppo-AntBulletEnv-v0"
checkpoint = load_from_hub(repo_id = repo_id, filename="ppo-AntBulletEnv-v0.zip")
model = PPO.load(checkpoint)

# Load the saved statistics
stats_path = load_from_hub(repo_id = repo_id, filename="vec_normalize.pkl")

eval_env = DummyVecEnv([lambda: gym.make("AntBulletEnv-v0")])
eval_env = VecNormalize.load(stats_path, eval_env)
#  do not update them at test time
eval_env.training = False
# reward normalization is not needed at test time
eval_env.norm_reward = False

from stable_baselines3.common.evaluation import evaluate_policy

mean_reward, std_reward = evaluate_policy(model, eval_env)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")

```


Mean_reward: 3547.01 +/- 33.32


model name: ppo-AntBulletEnv-v0


model author: ThomasSimonini


license: apache-2.0
tags:


- deep-reinforcement-learning
- reinforcement-learning


environment:


- OpenAI Gym Box2D: LunarLander-v2


model-index:


- name: stable-baselines3-ppo-LunarLander-v2


# stable-baselines3-ppo-LunarLander-v2 🚀👩🚀


This is a saved model of a PPO agent playing [LunarLander-v2](https://gym.openai.com/envs/LunarLander-v2/). The model is taken from [rl-baselines3-zoo](https://github.com/DLR-RM/rl-trained-agents)


The goal is to correctly land the lander by controlling firing engines (fire left orientation engine, fire main engine and fire right orientation engine).


<iframe width="560" height="315" src="https://www.youtube.com/embed/kE-Fvht81I0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>


👉 You can watch the agent playing by using this [notebook](https://colab.research.google.com/drive/19OonMRkMyCH6Dg0ECFQi7evxMRqkW3U0?usp=sharing)


## Use the Model


### Install the dependencies


You need to use the [Stable Baselines 3 Hugging Face version](https://github.com/simoninithomas/stable-baselines3) of the library (this version contains the function to load saved models directly from the Hugging Face Hub):


```
pip install git+https://github.com/simoninithomas/stable-baselines3.git
```


### Evaluate the agent


⚠You need to have Linux or MacOS to be able to use this environment. If it's not the case you can use the [colab notebook](https://colab.research.google.com/drive/19OonMRkMyCH6Dg0ECFQi7evxMRqkW3U0#scrollTo=Qbzj9quh0FsP)


```
# Import the libraries
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy

# Load the environment
env = gym.make('LunarLander-v2')

model = PPO.load_from_huggingface(hf_model_id="ThomasSimonini/stable-baselines3-ppo-LunarLander-v2",hf_model_filename="LunarLander-v2")

# Evaluate the agent
eval_env = gym.make('LunarLander-v2')
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

# Watch the agent play
obs = env.reset()
for i in range(1000):
    action, _state = model.predict(obs)
    obs, reward, done, info = env.step(action)
    env.render()
    if done:
      obs = env.reset()
```


## Results


Mean Reward (10 evaluation episodes): 245.63 +/- 10.02


model name: stable-baselines3-ppo-LunarLander-v2


model author: ThomasSimonini


tags: license:apache-2.0, deep-reinforcement-learning, reinforcement-learning, model-index


tags:


- conversational


# tests


model name: DialoGPT-medium-MentorDealerGuy


model author: AIDynamics


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


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
metric:
name: Accuracy
type: accuracy
value: 0.9180645161290323


<!-- 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: 0.7528
- Accuracy: 0.9181


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 318  | 3.3044          | 0.7623   |
| 3.7959        | 2.0   | 636  | 1.8674          | 0.8597   |
| 3.7959        | 3.0   | 954  | 1.1377          | 0.8948   |
| 1.6819        | 4.0   | 1272 | 0.8351          | 0.9126   |
| 0.8804        | 5.0   | 1590 | 0.7528          | 0.9181   |


### Framework versions


- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.3


model name: distilbert-base-uncased-finetuned-clinc


model author: msavel-prnt


task: text-classification


tags: pytorch, tensorboard, distilbert, text-classification, dataset:clinc_oos, transformers, license:apache-2.0, generated_from_trainer, infinity_compatible


# Cross-Encoder for MS Marco


This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.


This model uses [electra-base](https://huggingface.co/google/electra-base-discriminator).


## Training Data


This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) dataset. The model will predict a score between 0 and 1: Given a question and paragraph, can the question be answered by the paragraph?.


## Usage and Performance


Pre-trained models can be used like this:


```python
from sentence_transformers import CrossEncoder

model = CrossEncoder('model_name')
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2')])

print(scores)
```


model name: ms-marco-electra-base


model author: navteca


tags: pytorch, electra, text-classification, transformers, infinity_compatible


# Cross-Encoder for QNLI


Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the [GLUE QNLI](https://arxiv.org/abs/1804.07461) dataset, which transformed the [SQuAD dataset](https://rajpurkar.github.io/SQuAD-explorer/) into an NLI task.


model name: qnli-electra-base


tags: pytorch, electra, text-classification, arxiv:1804.07461, transformers, infinity_compatible


datasets:


- quora
language: en
license: mit
pipeline_tag: text-classification
tags:
- roberta
- text-classification


# Cross-Encoder for Quora Duplicate Questions Detection


This model uses [roberta-large](https://huggingface.co/roberta-large).


This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) dataset.


The model will predict a score between 0 and 1: How likely the two given questions are duplicates.


Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates.


## Usage and Performance


The trained model can be used like this:


```python
from sentence_transformers import CrossEncoder

model = CrossEncoder('model_name')
scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')])

print(scores)
```


model name: quora-roberta-large


model author: navteca


task: text-classification


tags: pytorch, jax, roberta, text-classification, en, dataset:quora, transformers, license:mit, infinity_compatible


- squad_v2
language: en
license: mit
pipeline_tag: question-answering
tags:
- roberta
- question-answering


# Roberta large model for QA (SQuAD 2.0)


## Training Data


The models have been trained on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.


It can be used for question answering task.


```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

# Load model & tokenizer
roberta_model = AutoModelForQuestionAnswering.from_pretrained('navteca/roberta-large-squad2')
roberta_tokenizer = AutoTokenizer.from_pretrained('navteca/roberta-large-squad2')

# Get predictions
nlp = pipeline('question-answering', model=roberta_model, tokenizer=roberta_tokenizer)

result = nlp({
    'question': 'How many people live in Berlin?',
    'context': 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'
})

print(result)

#{
#  "answer": "3,520,031"
#  "end": 36,
#  "score": 0.96186668,
#  "start": 27,
#}
```


model name: roberta-large-squad2


task: question-answering


tags: pytorch, jax, roberta, question-answering, en, dataset:squad_v2, transformers, license:mit, autonlp_compatible, infinity_compatible


datasets:


- squad_v2
language: en
license: mit
pipeline_tag: question-answering
tags:
- electra
- question-answering


# Electra base model for QA (SQuAD 2.0)


This model uses [electra-base](https://huggingface.co/google/electra-base-discriminator).


## Usage and Performance


```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

# Load model & tokenizer
electra_model = AutoModelForQuestionAnswering.from_pretrained('navteca/electra-base-squad2')
electra_tokenizer = AutoTokenizer.from_pretrained('navteca/electra-base-squad2')

# Get predictions
nlp = pipeline('question-answering', model=electra_model, tokenizer=electra_tokenizer)

result = nlp({
    'question': 'How many people live in Berlin?',
    'context': 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'
})

print(result)

#{
#  "answer": "3,520,031"
#  "end": 36,
#  "score": 0.99983448,
#  "start": 27,
#}
```


model name: electra-base-squad2


model author: navteca


tags: pytorch, electra, question-answering, en, dataset:squad_v2, transformers, license:mit, autonlp_compatible, infinity_compatible


- sqa
- wikisql
- wtq
language: en
license: mit
pipeline_tag: table-question-answering
tags:
- tapas
- table-question-answering


# TAPAS large model fine-tuned on WikiTable Questions (WTQ)


TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:


- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of a table and associated text.
- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.