---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-robust-ami_multi-nithin9
  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-robust-ami_multi-nithin9

This model is a fine-tuned version of [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4380
- Wer: 0.4318

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 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


---
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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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

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


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

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: 25.1441
- Wer: 1.0

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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

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


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

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: 1.9953
- Wer: 0.4577

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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

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


---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-tune_0.00005_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_0.00005_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: 1.5405
- Wer: 0.4744

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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


---
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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 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

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


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

# 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

More information needed

## Training and evaluation data

More information needed

## 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: 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


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


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

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

More information needed

## Training and evaluation data

More information needed

## 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

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


---
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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 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


---
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: []
---

<!-- 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_0.00005_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: 1.5257
- Wer: 0.4840

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 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

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


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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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

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


---
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

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: 1.7654
- Wer: 0.4952

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 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


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

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: 1.5392
- Wer: 0.4481

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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

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


---
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: []
---

<!-- 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_0.00005_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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 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

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


---
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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 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


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

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: 1.4945
- Wer: 0.4291

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 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

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


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

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: 11.0605
- Wer: 1.0

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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

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


---
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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 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


---
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: []
---

<!-- 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_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: 1.5093
- Wer: 0.4409

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 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

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


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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 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

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


---
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

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: 94.6519
- Wer: 1.0

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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


---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-tune_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-base-tune_0.0001_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: 1.5750
- Wer: 0.4813

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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


---
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: []
---

<!-- 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_0.0005_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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 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

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


---
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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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


---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-base-tune_0.00005_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_0.00005_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: 1.5701
- Wer: 0.4927

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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


---
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: []
---

<!-- 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_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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 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

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


---
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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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

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


---
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: []
---

<!-- 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_0.0005_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: 1.4670
- Wer: 0.4379

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 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


---
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: []
---

<!-- 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_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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 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

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


---
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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 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

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


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


---
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",
}
```


---

language: it

license: mit

tags:

- sentiment

- emotion

- 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-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

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 [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

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",
}
```


---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# TODO: Fill this model card


---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# 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)


---
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.


---
license: apache-2.0
tags:
- deep-reinforcement-learning
- reinforcement-learning

environment:
- OpenAI Gym: CartPole-v1

model-index:
- 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


---
license: apache-2.0
tags:
- deep-reinforcement-learning
- reinforcement-learning

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 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
```

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)


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

environment:
- 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`
2. Open Unity Hub and create a new 3D Project
3. In the cloned project folder, open `.\ML-Agents-SnowballFight-1vs1\packages` and copy manifest.json and package.lock.json
4. Paste these two files in `Your Unity Project\Packages` => this will install the required packages.
5. 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
`


---
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


---
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


---
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


---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# 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>

### 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-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}")

```

### Evaluation Results
Mean_reward: 3547.01 +/- 33.32


---
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


---
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

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 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


# 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)
```


# Cross-Encoder for QNLI

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

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.

## 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)
```


---
datasets:
- quora
language: en
license: mit
pipeline_tag: text-classification
tags:
- roberta
- text-classification
---
# Cross-Encoder for Quora Duplicate Questions Detection

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

## Training Data

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)
```