---
license: apache-2.0
tags:
- summarization
metrics:
- rouge
model-index:
- name: POCTS
  results:
  - task:
      name: Summarization
      type: summarization
    metrics:
    - name: Rouge1
      type: rouge
      value: 26.1391
---

<!-- 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. -->

# POCTS

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0970
- Rouge1: 26.1391
- Rouge2: 7.3101
- Rougel: 19.1217
- Rougelsum: 21.9706
- Gen Len: 46.2245

## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.3259        | 1.0   | 33875  | 3.2535          | 17.942  | 4.5143 | 14.2766 | 15.582    | 19.3901 |
| 2.9764        | 2.0   | 67750  | 3.1278          | 18.6558 | 5.1844 | 15.0939 | 16.3367   | 19.9174 |
| 2.5889        | 3.0   | 101625 | 3.0970          | 19.1763 | 5.4517 | 15.5342 | 16.7186   | 19.8855 |

### Framework versions

- Transformers 4.10.2
- Pytorch 1.7.1+cu110
- Datasets 1.11.0
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mbarthez-copy_mechanism-hal_articles
  results:
  - task:
      name: Summarization
      type: summarization
    metrics:
    - name: Rouge1
      type: rouge
      value: 36.548
---

<!-- 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. -->

# mbarthez-davide_articles-copy_enhanced

This model is a fine-tuned version of [moussaKam/mbarthez](https://huggingface.co/moussaKam/mbarthez) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4905
- Rouge1: 36.548
- Rouge2: 19.6282
- Rougel: 30.2513
- Rougelsum: 30.2765
- Gen Len: 25.7238

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.6706        | 1.0   | 33552  | 1.5690          | 31.2477 | 16.5455 | 26.9855 | 26.9754   | 18.6217 |
| 1.3446        | 2.0   | 67104  | 1.5060          | 32.1108 | 17.1408 | 27.7833 | 27.7703   | 18.9115 |
| 1.3245        | 3.0   | 100656 | 1.4905          | 32.9084 | 17.7027 | 28.2912 | 28.2975   | 18.9801 |

### Framework versions

- Transformers 4.10.2
- Pytorch 1.7.1+cu110
- Datasets 1.11.0
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab-1
  results: []
---

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

# wav2vec2-base-timit-demo-colab-1

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3857
- Wer: 0.3874

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4285        | 2.01  | 500  | 1.4732          | 0.9905 |
| 0.7457        | 4.02  | 1000 | 0.5278          | 0.4960 |
| 0.3463        | 6.02  | 1500 | 0.4245          | 0.4155 |
| 0.2034        | 8.03  | 2000 | 0.3857          | 0.3874 |

### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-guarani-small
  results: []
---

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

# wav2vec2-large-xls-r-300m-guarani-small

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4964
- Wer: 0.5957

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 6.65  | 100  | 1.1326          | 1.0    |
| 1.6569        | 13.32 | 200  | 0.5264          | 0.6478 |
| 1.6569        | 19.97 | 300  | 0.5370          | 0.6261 |
| 0.2293        | 26.65 | 400  | 0.4964          | 0.5957 |

### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


---
license: apache-2.0
tags:
- generated_from_trainer
- "es"
- "robust-speech-event"
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-spanish-large
  results: []

---

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

# wav2vec2-large-xls-r-300m-spanish-large

This model is a fine-tuned version of [tomascufaro/xls-r-es-test](https://huggingface.co/tomascufaro/xls-r-es-test) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1431
- Wer: 0.1197

## 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: 0.0002
- train_batch_size: 10
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1769        | 0.15  | 400   | 0.1795          | 0.1698 |
| 0.217         | 0.3   | 800   | 0.2000          | 0.1945 |
| 0.2372        | 0.45  | 1200  | 0.1985          | 0.1859 |
| 0.2351        | 0.6   | 1600  | 0.1901          | 0.1772 |
| 0.2269        | 0.75  | 2000  | 0.1968          | 0.1783 |
| 0.2284        | 0.9   | 2400  | 0.1873          | 0.1771 |
| 0.2014        | 1.06  | 2800  | 0.1840          | 0.1696 |
| 0.1988        | 1.21  | 3200  | 0.1904          | 0.1730 |
| 0.1919        | 1.36  | 3600  | 0.1827          | 0.1630 |
| 0.1919        | 1.51  | 4000  | 0.1788          | 0.1629 |
| 0.1817        | 1.66  | 4400  | 0.1755          | 0.1558 |
| 0.1812        | 1.81  | 4800  | 0.1795          | 0.1638 |
| 0.1808        | 1.96  | 5200  | 0.1762          | 0.1603 |
| 0.1625        | 2.11  | 5600  | 0.1721          | 0.1557 |
| 0.1477        | 2.26  | 6000  | 0.1735          | 0.1504 |
| 0.1508        | 2.41  | 6400  | 0.1708          | 0.1478 |
| 0.157         | 2.56  | 6800  | 0.1644          | 0.1466 |
| 0.1491        | 2.71  | 7200  | 0.1638          | 0.1445 |
| 0.1458        | 2.86  | 7600  | 0.1582          | 0.1426 |
| 0.1387        | 3.02  | 8000  | 0.1607          | 0.1376 |
| 0.1269        | 3.17  | 8400  | 0.1559          | 0.1364 |
| 0.1172        | 3.32  | 8800  | 0.1521          | 0.1335 |
| 0.1203        | 3.47  | 9200  | 0.1534          | 0.1330 |
| 0.1177        | 3.62  | 9600  | 0.1485          | 0.1304 |
| 0.1167        | 3.77  | 10000 | 0.1498          | 0.1302 |
| 0.1194        | 3.92  | 10400 | 0.1463          | 0.1287 |
| 0.1053        | 4.07  | 10800 | 0.1483          | 0.1282 |
| 0.098         | 4.22  | 11200 | 0.1498          | 0.1267 |
| 0.0958        | 4.37  | 11600 | 0.1461          | 0.1233 |
| 0.0946        | 4.52  | 12000 | 0.1444          | 0.1218 |
| 0.094         | 4.67  | 12400 | 0.1434          | 0.1206 |
| 0.0932        | 4.82  | 12800 | 0.1424          | 0.1206 |
| 0.0912        | 4.98  | 13200 | 0.1431          | 0.1197 |

### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0


---
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-spanish-custom
  results: []
---

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

# wav2vec2-large-xls-r-300m-spanish-custom

This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2245
- eval_wer: 0.2082
- eval_runtime: 801.6784
- eval_samples_per_second: 18.822
- eval_steps_per_second: 2.354
- epoch: 0.76
- step: 8400

## 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: 0.0002
- 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: 200
- num_epochs: 10
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0


---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- "es"
- "robust-speech-event"
datasets:
- common_voice_8
model-index:
- name: xls-r-es-test-lm
  results: [WER = 0.094
            CER = 0.031]
---

<!-- 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. -->

# xls-r-es-test-lm

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ES dataset.
It achieves the following results on the test set with lm model:
- Loss: 0.1304
- WER: 0.094
- CER: 0.031

It achieves the following results on the val set with lm model:
- Loss: 0.1304
- WER: 0.081
- CER: 0.025

## 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: 7.5e-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: 2000
- num_epochs: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.9613        | 0.07  | 500   | 2.9647          | 1.0    |
| 2.604         | 0.14  | 1000  | 1.8300          | 0.9562 |
| 1.177         | 0.21  | 1500  | 0.3652          | 0.3077 |
| 1.0745        | 0.28  | 2000  | 0.2707          | 0.2504 |
| 1.0103        | 0.35  | 2500  | 0.2338          | 0.2157 |
| 0.9858        | 0.42  | 3000  | 0.2321          | 0.2129 |
| 0.974         | 0.49  | 3500  | 0.2164          | 0.2031 |
| 0.9699        | 0.56  | 4000  | 0.2078          | 0.1970 |
| 0.9513        | 0.63  | 4500  | 0.2173          | 0.2139 |
| 0.9657        | 0.7   | 5000  | 0.2050          | 0.1979 |
| 0.9484        | 0.77  | 5500  | 0.2008          | 0.1919 |
| 0.9317        | 0.84  | 6000  | 0.2012          | 0.1911 |
| 0.9366        | 0.91  | 6500  | 0.2024          | 0.1976 |
| 0.9242        | 0.98  | 7000  | 0.2062          | 0.2028 |
| 0.9138        | 1.05  | 7500  | 0.1924          | 0.1863 |
| 0.921         | 1.12  | 8000  | 0.1935          | 0.1836 |
| 0.9117        | 1.19  | 8500  | 0.1887          | 0.1815 |
| 0.9064        | 1.26  | 9000  | 0.1909          | 0.1839 |
| 0.9118        | 1.32  | 9500  | 0.1869          | 0.1830 |
| 0.9121        | 1.39  | 10000 | 0.1863          | 0.1802 |
| 0.9048        | 1.46  | 10500 | 0.1845          | 0.1791 |
| 0.8955        | 1.53  | 11000 | 0.1863          | 0.1774 |
| 0.8947        | 1.6   | 11500 | 0.1907          | 0.1814 |
| 0.9073        | 1.67  | 12000 | 0.1892          | 0.1853 |
| 0.8927        | 1.74  | 12500 | 0.1821          | 0.1750 |
| 0.8732        | 1.81  | 13000 | 0.1815          | 0.1768 |
| 0.8761        | 1.88  | 13500 | 0.1822          | 0.1749 |
| 0.8751        | 1.95  | 14000 | 0.1789          | 0.1715 |
| 0.8889        | 2.02  | 14500 | 0.1819          | 0.1791 |
| 0.8864        | 2.09  | 15000 | 0.1826          | 0.1794 |
| 0.886         | 2.16  | 15500 | 0.1788          | 0.1776 |
| 0.8915        | 2.23  | 16000 | 0.1756          | 0.1719 |
| 0.8689        | 2.3   | 16500 | 0.1769          | 0.1711 |
| 0.879         | 2.37  | 17000 | 0.1777          | 0.1739 |
| 0.8692        | 2.44  | 17500 | 0.1765          | 0.1705 |
| 0.8504        | 2.51  | 18000 | 0.1699          | 0.1652 |
| 0.8728        | 2.58  | 18500 | 0.1705          | 0.1694 |
| 0.8523        | 2.65  | 19000 | 0.1674          | 0.1645 |
| 0.8513        | 2.72  | 19500 | 0.1661          | 0.1611 |
| 0.8498        | 2.79  | 20000 | 0.1660          | 0.1631 |
| 0.8432        | 2.86  | 20500 | 0.1636          | 0.1610 |
| 0.8492        | 2.93  | 21000 | 0.1708          | 0.1688 |
| 0.8561        | 3.0   | 21500 | 0.1663          | 0.1604 |
| 0.842         | 3.07  | 22000 | 0.1690          | 0.1625 |
| 0.857         | 3.14  | 22500 | 0.1642          | 0.1605 |
| 0.8518        | 3.21  | 23000 | 0.1626          | 0.1585 |
| 0.8506        | 3.28  | 23500 | 0.1651          | 0.1605 |
| 0.8394        | 3.35  | 24000 | 0.1647          | 0.1585 |
| 0.8431        | 3.42  | 24500 | 0.1632          | 0.1573 |
| 0.8566        | 3.49  | 25000 | 0.1614          | 0.1550 |
| 0.8534        | 3.56  | 25500 | 0.1645          | 0.1589 |
| 0.8386        | 3.63  | 26000 | 0.1632          | 0.1582 |
| 0.8357        | 3.7   | 26500 | 0.1631          | 0.1556 |
| 0.8299        | 3.77  | 27000 | 0.1612          | 0.1550 |
| 0.8421        | 3.84  | 27500 | 0.1602          | 0.1552 |
| 0.8375        | 3.91  | 28000 | 0.1592          | 0.1537 |
| 0.8328        | 3.97  | 28500 | 0.1587          | 0.1537 |
| 0.8155        | 4.04  | 29000 | 0.1587          | 0.1520 |
| 0.8335        | 4.11  | 29500 | 0.1624          | 0.1556 |
| 0.8138        | 4.18  | 30000 | 0.1581          | 0.1547 |
| 0.8195        | 4.25  | 30500 | 0.1560          | 0.1507 |
| 0.8092        | 4.32  | 31000 | 0.1561          | 0.1534 |
| 0.8191        | 4.39  | 31500 | 0.1549          | 0.1493 |
| 0.8008        | 4.46  | 32000 | 0.1540          | 0.1493 |
| 0.8138        | 4.53  | 32500 | 0.1544          | 0.1493 |
| 0.8173        | 4.6   | 33000 | 0.1553          | 0.1511 |
| 0.8081        | 4.67  | 33500 | 0.1541          | 0.1484 |
| 0.8192        | 4.74  | 34000 | 0.1560          | 0.1506 |
| 0.8068        | 4.81  | 34500 | 0.1540          | 0.1503 |
| 0.8105        | 4.88  | 35000 | 0.1529          | 0.1483 |
| 0.7976        | 4.95  | 35500 | 0.1507          | 0.1451 |
| 0.8143        | 5.02  | 36000 | 0.1505          | 0.1462 |
| 0.8053        | 5.09  | 36500 | 0.1517          | 0.1476 |
| 0.785         | 5.16  | 37000 | 0.1526          | 0.1478 |
| 0.7936        | 5.23  | 37500 | 0.1489          | 0.1421 |
| 0.807         | 5.3   | 38000 | 0.1483          | 0.1420 |
| 0.8092        | 5.37  | 38500 | 0.1481          | 0.1435 |
| 0.793         | 5.44  | 39000 | 0.1503          | 0.1438 |
| 0.814         | 5.51  | 39500 | 0.1495          | 0.1480 |
| 0.807         | 5.58  | 40000 | 0.1472          | 0.1424 |
| 0.7913        | 5.65  | 40500 | 0.1471          | 0.1422 |
| 0.7844        | 5.72  | 41000 | 0.1473          | 0.1422 |
| 0.7888        | 5.79  | 41500 | 0.1445          | 0.1385 |
| 0.7806        | 5.86  | 42000 | 0.1435          | 0.1394 |
| 0.7773        | 5.93  | 42500 | 0.1461          | 0.1424 |
| 0.786         | 6.0   | 43000 | 0.1450          | 0.1413 |
| 0.7784        | 6.07  | 43500 | 0.1463          | 0.1424 |
| 0.7937        | 6.14  | 44000 | 0.1438          | 0.1386 |
| 0.7738        | 6.21  | 44500 | 0.1437          | 0.1383 |
| 0.7728        | 6.28  | 45000 | 0.1424          | 0.1371 |
| 0.7681        | 6.35  | 45500 | 0.1416          | 0.1376 |
| 0.776         | 6.42  | 46000 | 0.1415          | 0.1380 |
| 0.7773        | 6.49  | 46500 | 0.1416          | 0.1371 |
| 0.7692        | 6.56  | 47000 | 0.1398          | 0.1345 |
| 0.7642        | 6.62  | 47500 | 0.1381          | 0.1341 |
| 0.7692        | 6.69  | 48000 | 0.1392          | 0.1334 |
| 0.7667        | 6.76  | 48500 | 0.1392          | 0.1348 |
| 0.7712        | 6.83  | 49000 | 0.1398          | 0.1333 |
| 0.7628        | 6.9   | 49500 | 0.1392          | 0.1344 |
| 0.7622        | 6.97  | 50000 | 0.1377          | 0.1329 |
| 0.7639        | 7.04  | 50500 | 0.1361          | 0.1316 |
| 0.742         | 7.11  | 51000 | 0.1376          | 0.1327 |
| 0.7526        | 7.18  | 51500 | 0.1387          | 0.1342 |
| 0.7606        | 7.25  | 52000 | 0.1363          | 0.1316 |
| 0.7626        | 7.32  | 52500 | 0.1365          | 0.1313 |
| 0.752         | 7.39  | 53000 | 0.1354          | 0.1309 |
| 0.7562        | 7.46  | 53500 | 0.1362          | 0.1312 |
| 0.7557        | 7.53  | 54000 | 0.1358          | 0.1325 |
| 0.7588        | 7.6   | 54500 | 0.1343          | 0.1311 |
| 0.7485        | 7.67  | 55000 | 0.1346          | 0.1301 |
| 0.7466        | 7.74  | 55500 | 0.1354          | 0.1314 |
| 0.7558        | 7.81  | 56000 | 0.1359          | 0.1325 |
| 0.7578        | 7.88  | 56500 | 0.1363          | 0.1334 |
| 0.7411        | 7.95  | 57000 | 0.1346          | 0.1301 |
| 0.7478        | 8.02  | 57500 | 0.1355          | 0.1305 |
| 0.7451        | 8.09  | 58000 | 0.1349          | 0.1302 |
| 0.7383        | 8.16  | 58500 | 0.1349          | 0.1294 |
| 0.7482        | 8.23  | 59000 | 0.1341          | 0.1293 |
| 0.742         | 8.3   | 59500 | 0.1338          | 0.1296 |
| 0.7343        | 8.37  | 60000 | 0.1348          | 0.1307 |
| 0.7385        | 8.44  | 60500 | 0.1324          | 0.1282 |
| 0.7567        | 8.51  | 61000 | 0.1334          | 0.1281 |
| 0.7342        | 8.58  | 61500 | 0.1338          | 0.1289 |
| 0.7401        | 8.65  | 62000 | 0.1331          | 0.1285 |
| 0.7362        | 8.72  | 62500 | 0.1329          | 0.1283 |
| 0.7241        | 8.79  | 63000 | 0.1323          | 0.1277 |
| 0.7244        | 8.86  | 63500 | 0.1317          | 0.1269 |
| 0.7274        | 8.93  | 64000 | 0.1308          | 0.1260 |
| 0.7411        | 9.0   | 64500 | 0.1309          | 0.1256 |
| 0.7255        | 9.07  | 65000 | 0.1316          | 0.1265 |
| 0.7406        | 9.14  | 65500 | 0.1315          | 0.1270 |
| 0.7418        | 9.21  | 66000 | 0.1315          | 0.1269 |
| 0.7301        | 9.27  | 66500 | 0.1315          | 0.1273 |
| 0.7248        | 9.34  | 67000 | 0.1323          | 0.1274 |
| 0.7423        | 9.41  | 67500 | 0.1309          | 0.1267 |
| 0.7152        | 9.48  | 68000 | 0.1312          | 0.1271 |
| 0.7295        | 9.55  | 68500 | 0.1306          | 0.1262 |
| 0.7231        | 9.62  | 69000 | 0.1308          | 0.1263 |
| 0.7344        | 9.69  | 69500 | 0.1313          | 0.1267 |
| 0.7264        | 9.76  | 70000 | 0.1305          | 0.1263 |
| 0.7309        | 9.83  | 70500 | 0.1303          | 0.1262 |
| 0.73          | 9.9   | 71000 | 0.1303          | 0.1261 |
| 0.7353        | 9.97  | 71500 | 0.1304          | 0.1260 |

### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0


---
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-spanish-small
  results: []
---

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

# wav2vec2-large-xls-r-300m-spanish-small

This model is a fine-tuned version of [jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom](https://huggingface.co/jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3596
- Wer: 0.2105

## 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1971        | 0.79  | 400   | 0.2169          | 0.2077 |
| 0.2293        | 1.58  | 800   | 0.2507          | 0.2418 |
| 0.2065        | 2.37  | 1200  | 0.2703          | 0.2459 |
| 0.1842        | 3.16  | 1600  | 0.2716          | 0.2495 |
| 0.1634        | 3.95  | 2000  | 0.2695          | 0.2510 |
| 0.1443        | 4.74  | 2400  | 0.2754          | 0.2435 |
| 0.1345        | 5.53  | 2800  | 0.3119          | 0.2654 |
| 0.1267        | 6.32  | 3200  | 0.3154          | 0.2573 |
| 0.1237        | 7.11  | 3600  | 0.3251          | 0.2666 |
| 0.1118        | 7.91  | 4000  | 0.3139          | 0.2503 |
| 0.1051        | 8.7   | 4400  | 0.3286          | 0.2573 |
| 0.0964        | 9.49  | 4800  | 0.3348          | 0.2587 |
| 0.0946        | 10.28 | 5200  | 0.3357          | 0.2587 |
| 0.0897        | 11.07 | 5600  | 0.3408          | 0.2590 |
| 0.0812        | 11.86 | 6000  | 0.3380          | 0.2560 |
| 0.079         | 12.65 | 6400  | 0.3304          | 0.2415 |
| 0.0753        | 13.44 | 6800  | 0.3557          | 0.2540 |
| 0.0717        | 14.23 | 7200  | 0.3507          | 0.2519 |
| 0.0691        | 15.02 | 7600  | 0.3554          | 0.2587 |
| 0.0626        | 15.81 | 8000  | 0.3619          | 0.2520 |
| 0.0661        | 16.6  | 8400  | 0.3609          | 0.2564 |
| 0.0582        | 17.39 | 8800  | 0.3818          | 0.2520 |
| 0.0556        | 18.18 | 9200  | 0.3685          | 0.2410 |
| 0.0515        | 18.97 | 9600  | 0.3658          | 0.2367 |
| 0.0478        | 19.76 | 10000 | 0.3701          | 0.2413 |
| 0.0486        | 20.55 | 10400 | 0.3681          | 0.2371 |
| 0.0468        | 21.34 | 10800 | 0.3607          | 0.2370 |
| 0.0452        | 22.13 | 11200 | 0.3499          | 0.2286 |
| 0.0399        | 22.92 | 11600 | 0.3647          | 0.2282 |
| 0.0393        | 23.72 | 12000 | 0.3638          | 0.2255 |
| 0.0381        | 24.51 | 12400 | 0.3359          | 0.2202 |
| 0.0332        | 25.3  | 12800 | 0.3488          | 0.2177 |
| 0.033         | 26.09 | 13200 | 0.3628          | 0.2175 |
| 0.0311        | 26.88 | 13600 | 0.3695          | 0.2195 |
| 0.0294        | 27.67 | 14000 | 0.3624          | 0.2164 |
| 0.0281        | 28.46 | 14400 | 0.3688          | 0.2113 |
| 0.0274        | 29.25 | 14800 | 0.3596          | 0.2105 |

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0


---
tags:
- conversational
---

# Harry Potter DialoGPT Model


---
tags:
- generated_from_trainer
model-index:
- name: out
  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. -->

# out

This model is a fine-tuned version of [/1TB_SSD/SB_AI/out_epoch1/out/checkpoint-1115000/](https://huggingface.co//1TB_SSD/SB_AI/out_epoch1/out/checkpoint-1115000/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0645

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 2518227880
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step    | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| 0.0867        | 0.07  | 75000   | 0.0742          |
| 0.0783        | 0.13  | 150000  | 0.0695          |
| 0.0719        | 0.2   | 225000  | 0.0732          |
| 0.0743        | 0.27  | 300000  | 0.0663          |
| 0.0659        | 0.34  | 375000  | 0.0686          |
| 0.0664        | 0.4   | 450000  | 0.0683          |
| 0.0637        | 0.47  | 525000  | 0.0680          |
| 0.0655        | 0.54  | 600000  | 0.0641          |
| 0.0676        | 0.6   | 675000  | 0.0644          |
| 0.0704        | 0.67  | 750000  | 0.0645          |
| 0.0687        | 0.74  | 825000  | 0.0610          |
| 0.059         | 0.81  | 900000  | 0.0652          |
| 0.0666        | 0.87  | 975000  | 0.0619          |
| 0.0624        | 0.94  | 1050000 | 0.0619          |
| 0.0625        | 1.01  | 1125000 | 0.0667          |
| 0.0614        | 1.03  | 1150000 | 0.0658          |
| 0.0597        | 1.05  | 1175000 | 0.0683          |
| 0.0629        | 1.07  | 1200000 | 0.0691          |
| 0.0603        | 1.1   | 1225000 | 0.0678          |
| 0.0601        | 1.12  | 1250000 | 0.0746          |
| 0.0606        | 1.14  | 1275000 | 0.0691          |
| 0.0671        | 1.16  | 1300000 | 0.0702          |
| 0.0625        | 1.19  | 1325000 | 0.0661          |
| 0.0617        | 1.21  | 1350000 | 0.0688          |
| 0.0579        | 1.23  | 1375000 | 0.0679          |
| 0.0663        | 1.25  | 1400000 | 0.0634          |
| 0.0583        | 1.28  | 1425000 | 0.0638          |
| 0.0623        | 1.3   | 1450000 | 0.0681          |
| 0.0615        | 1.32  | 1475000 | 0.0670          |
| 0.0592        | 1.34  | 1500000 | 0.0666          |
| 0.0626        | 1.37  | 1525000 | 0.0666          |
| 0.063         | 1.39  | 1550000 | 0.0647          |
| 0.0648        | 1.41  | 1575000 | 0.0653          |
| 0.0611        | 1.43  | 1600000 | 0.0700          |
| 0.0622        | 1.46  | 1625000 | 0.0634          |
| 0.0617        | 1.48  | 1650000 | 0.0651          |
| 0.0613        | 1.5   | 1675000 | 0.0634          |
| 0.0639        | 1.52  | 1700000 | 0.0661          |
| 0.0615        | 1.54  | 1725000 | 0.0644          |
| 0.0605        | 1.57  | 1750000 | 0.0662          |
| 0.0622        | 1.59  | 1775000 | 0.0656          |
| 0.0585        | 1.61  | 1800000 | 0.0633          |
| 0.0628        | 1.63  | 1825000 | 0.0625          |
| 0.0638        | 1.66  | 1850000 | 0.0662          |
| 0.0599        | 1.68  | 1875000 | 0.0664          |
| 0.0583        | 1.7   | 1900000 | 0.0668          |
| 0.0543        | 1.72  | 1925000 | 0.0631          |
| 0.06          | 1.75  | 1950000 | 0.0629          |
| 0.0615        | 1.77  | 1975000 | 0.0644          |
| 0.0587        | 1.79  | 2000000 | 0.0663          |
| 0.0647        | 1.81  | 2025000 | 0.0654          |
| 0.0604        | 1.84  | 2050000 | 0.0639          |
| 0.0641        | 1.86  | 2075000 | 0.0636          |
| 0.0604        | 1.88  | 2100000 | 0.0636          |
| 0.0654        | 1.9   | 2125000 | 0.0652          |
| 0.0588        | 1.93  | 2150000 | 0.0638          |
| 0.0616        | 1.95  | 2175000 | 0.0657          |
| 0.0598        | 1.97  | 2200000 | 0.0646          |
| 0.0633        | 1.99  | 2225000 | 0.0645          |

### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3


---
tags:
- conversational
---

# Snape DialoGPT Model


---
tags:
- asteroid
- audio
- ConvTasNet
datasets:
- LibriMix
- enh_single
license: cc-by-sa-4.0
---

## Asteroid model
Imported from this Zenodo [model page](https://zenodo.org/record/3970768).

## Description:
This model was trained by  Brij Mohan using the Librimix/ConvTasNet recipe in Asteroid.
It was trained on the `enh_single` task of the Libri3Mix dataset.

## Training config:
```yaml
data:
    n_src: 1
    sample_rate: 8000
    segment: 3
    task: enh_single
    train_dir: data/wav8k/min/train-360
    valid_dir: data/wav8k/min/dev
filterbank:
    kernel_size: 16
    n_filters: 512
    stride: 8
masknet:
    bn_chan: 128
    hid_chan: 512
    mask_act: relu
    n_blocks: 8
    n_repeats: 3
    n_src: 1
    skip_chan: 128
optim:
    lr: 0.001
    optimizer: adam
    weight_decay: 0.0
training:
    batch_size: 24
    early_stop: True
    epochs: 200
    half_lr: True
```

## Results:
```yaml
si_sdr: 14.783675142685572
si_sdr_imp: 11.464625198953202
sdr: 15.497505907983102
sdr_imp: 12.07230150154914
sar: 15.497505907983102
sar_imp: 12.07230150154914
stoi: 0.9270030254700518
stoi_imp: 0.1320547197597893
```

## License notice:
This work "ConvTasNet_Libri1Mix_enhsingle_8k"
is a derivative of [LibriSpeech ASR corpus](http://www.openslr.org/12) by
[Vassil Panayotov](https://github.com/vdp),
used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
"ConvTasNet_Libri1Mix_enhsingle_8k"
is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Manuel Pariente.


---
tags:
- asteroid
- audio
- DPRNNTasNet
- audio-to-audio
datasets:
- wham
- sep_clean
license: cc-by-sa-4.0
---

## Asteroid model `mpariente/DPRNNTasNet-ks2_WHAM_sepclean`
Imported from [Zenodo](https://zenodo.org/record/3862942)

### Description:
This model was trained by Manuel Pariente
using the wham/DPRNN recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the WHAM! dataset.

### Training config:
```yaml
data:
    mode: min
    nondefault_nsrc: None
    sample_rate: 8000
    segment: 2.0
    task: sep_clean
    train_dir: data/wav8k/min/tr
    valid_dir: data/wav8k/min/cv
filterbank:
    kernel_size: 2
    n_filters: 64
    stride: 1
main_args:
    exp_dir: exp/train_dprnn_new/
    gpus: -1
    help: None
masknet:
    bidirectional: True
    bn_chan: 128
    chunk_size: 250
    dropout: 0
    hid_size: 128
    hop_size: 125
    in_chan: 64
    mask_act: sigmoid
    n_repeats: 6
    n_src: 2
    out_chan: 64
optim:
    lr: 0.001
    optimizer: adam
    weight_decay: 1e-05
positional arguments:
training:
    batch_size: 3
    early_stop: True
    epochs: 200
    gradient_clipping: 5
    half_lr: True
    num_workers: 8
```

### Results:
```yaml
si_sdr: 19.316743490695334
si_sdr_imp: 19.317895273889842
sdr: 19.68085347190952
sdr_imp: 19.5298092932871
sir: 30.362213998701232
sir_imp: 30.21116982007881
sar: 20.15553251343315
sar_imp: -129.02091762351188
stoi: 0.97772664309074
stoi_imp: 0.23968091518217424
```

### License notice:
This work "DPRNNTasNet-ks2_WHAM_sepclean" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A)
by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for
Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only).
"DPRNNTasNet-ks2_WHAM_sepclean" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Manuel Pariente.


---
tags:
- asteroid
- audio
- ConvTasNet
- audio-to-audio
datasets:
- wham
- sep_clean
license: cc-by-sa-4.0
widget:
- example_title: Librispeech sample 1
  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
---

## Asteroid model `mpariente/ConvTasNet_WHAM_sepclean`
Imported from [Zenodo](https://zenodo.org/record/3862942)

### Description:
This model was trained by Manuel Pariente
using the wham/ConvTasNet recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the WHAM! dataset.

### Training config:
```yaml
data:
    n_src: 2
    mode: min
    nondefault_nsrc: None
    sample_rate: 8000
    segment: 3
    task: sep_clean
    train_dir: data/wav8k/min/tr/
    valid_dir: data/wav8k/min/cv/
filterbank:
    kernel_size: 16
    n_filters: 512
    stride: 8
main_args:
    exp_dir: exp/wham
    gpus: -1
    help: None
masknet:
    bn_chan: 128
    hid_chan: 512
    mask_act: relu
    n_blocks: 8
    n_repeats: 3
    n_src: 2
    skip_chan: 128
optim:
    lr: 0.001
    optimizer: adam
    weight_decay: 0.0
positional arguments:
training:
    batch_size: 24
    early_stop: True
    epochs: 200
    half_lr: True
    num_workers: 4
```

### Results:
```yaml
si_sdr: 16.21326632846293
si_sdr_imp: 16.21441705664987
sdr: 16.615180021738933
sdr_imp: 16.464137807433435
sir: 26.860503975131923
sir_imp: 26.709461760826414
sar: 17.18312813480803
sar_imp: -131.99332048277296
stoi: 0.9619940905157323
stoi_imp: 0.2239480672473015
```

### License notice:
This work "ConvTasNet_WHAM!_sepclean" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A)
by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for
Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only).
"ConvTasNet_WHAM!_sepclean" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Manuel Pariente.


---
tags:
- asteroid
- audio
- ConvTasNet
- audio-to-audio
datasets:
- LibriMix
- sep_noisy
license: cc-by-sa-4.0
---

## Asteroid model
Imported from this Zenodo [model page](https://zenodo.org/record/4020529).

## Description:
This model was trained by  Takhir Mirzaev using the Librimix/ConvTasNet recipe in Asteroid.
It was trained on the `sep_noisy` task of the Libri3Mix dataset.

## Training config:
```yaml
data:
    n_src: 3
    sample_rate: 8000
    segment: 3
    task: sep_noisy
    train_dir: data/wav8k/min/train-360
    valid_dir: data/wav8k/min/dev
filterbank:
    kernel_size: 16
    n_filters: 512
    stride: 8
masknet:
    bn_chan: 128
    hid_chan: 512
    mask_act: relu
    n_blocks: 8
    n_repeats: 3
    skip_chan: 128
optim:
    lr: 0.001
    optimizer: adam
    weight_decay: 0.0
positional arguments:
training:
    batch_size: 4
    early_stop: True
    epochs: 200
    half_lr: True
    num_workers: 4
```

## Results:
```yaml
si_sdr: 6.824750632456865
si_sdr_imp: 11.234803761803752
sdr: 7.715799858488098
sdr_imp: 11.778681386239114
sir: 16.442141130818637
sir_imp: 19.527535070051055
sar: 8.757864265661263
sar_imp: -0.15657258049670303
stoi: 0.7854554136619554
stoi_imp: 0.22267957718163015
```

## License notice:
This work "ConvTasNet_Libri3Mix_sepnoisy"
is a derivative of [LibriSpeech ASR corpus](http://www.openslr.org/12) by
[Vassil Panayotov](https://github.com/vdp),
used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
"ConvTasNet_Libri3Mix_sepnoisy"
is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Manuel Pariente.


---
tags:
- conversational
---

# Steins Gate DialoGPT Model


---
language: en
tags:
- pytorch
- question-answering
datasets:
- squad_v2
- squad2
license: cc-by-4.0
metrics:
- squad_v2
- exact
- f1
widget:
- text: "By what main attribute are computational problems classified utilizing computational complexity theory?"
  context: "Computational complexity theory is a branch of the theory of computation in theoretical computer science that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm."
---

# Performance
This ensemble was evaluated on [SQuAD 2.0](https://huggingface.co/datasets/squad_v2) with the following results:

```
{'HasAns_exact': 52.5472334682861,
 'HasAns_f1': 67.94939813758602,
 'HasAns_total': 5928,
 'NoAns_exact': 91.75777964676199,
 'NoAns_f1': 91.75777964676199,
 'NoAns_total': 5945,
 'best_exact': 72.16373283921503,
 'best_exact_thresh': 0.0,
 'best_f1': 79.85378860941708,
 'best_f1_thresh': 0.0,
 'exact': 72.1805777815211,
 'f1': 79.87063355172326,
 'total': 11873
 }
 ```


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9255649091714665
    - name: Recall
      type: recall
      value: 0.9347801767535519
    - name: F1
      type: f1
      value: 0.9301497189291478
    - name: Accuracy
      type: accuracy
      value: 0.9837164598789457
---

<!-- 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-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0613
- Precision: 0.9256
- Recall: 0.9348
- F1: 0.9301
- Accuracy: 0.9837

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2414        | 1.0   | 878  | 0.0702          | 0.9097    | 0.9200 | 0.9148 | 0.9804   |
| 0.0521        | 2.0   | 1756 | 0.0609          | 0.9190    | 0.9327 | 0.9258 | 0.9828   |
| 0.0308        | 3.0   | 2634 | 0.0613          | 0.9256    | 0.9348 | 0.9301 | 0.9837   |

### Framework versions

- Transformers 4.11.0
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: all-mpnet-base-v2-finetuned-mydata
  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. -->

# all-mpnet-base-v2-finetuned-mydata

This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0441

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 416  | 4.5566          |
| 5.5599        | 2.0   | 832  | 3.9093          |
| 4.1188        | 3.0   | 1248 | 3.6204          |
| 3.6951        | 4.0   | 1664 | 3.4622          |
| 3.4406        | 5.0   | 2080 | 3.3143          |
| 3.4406        | 6.0   | 2496 | 3.2054          |
| 3.3001        | 7.0   | 2912 | 3.1572          |
| 3.2002        | 8.0   | 3328 | 3.0510          |
| 3.1467        | 9.0   | 3744 | 3.0717          |
| 3.0763        | 10.0  | 4160 | 3.0017          |

### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
  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. -->

# distilgpt2-finetuned-wikitext2

This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6424

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7608        | 1.0   | 2334 | 3.6655          |
| 3.6335        | 2.0   | 4668 | 3.6455          |
| 3.6066        | 3.0   | 7002 | 3.6424          |

### Framework versions

- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


---
language: ga-IE
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: cpierse/wav2vec2-large-xlsr-53-irish
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice ga-IE
      type: common_voice
      args: ga-IE
    metrics:
       - name: Test WER
         type: wer
         value: 43.06
---

# Wav2Vec2-Large-XLSR-53-Irish

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Irish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.

When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

The model can be used directly (without a language model) as follows:

```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "ga-IE", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.

processor = Wav2Vec2Processor.from_pretrained("cpierse/wav2vec2-large-xlsr-53-irish")
model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-irish")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
   speech_array, sampling_rate = torchaudio.load(batch["path"])
   batch["speech"] = resampler(speech_array).squeeze().numpy()
   return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
   logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```

## Evaluation

The model can be evaluated as follows on the Irish test data of Common Voice.

```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "ga-IE", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("cpierse/wav2vec2-large-xlsr-53-irish")
model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-irish")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\„\«\(\»\)\’\']'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
   batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
   speech_array, sampling_rate = torchaudio.load(batch["path"])
   batch["speech"] = resampler(speech_array).squeeze().numpy()
   return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
   inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

   with torch.no_grad():
      logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

   pred_ids = torch.argmax(logits, dim=-1)
   batch["pred_strings"] = processor.batch_decode(pred_ids)
   return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```

**Test Result**: 43.06 %


---
language: eo
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name:  XLSR Wav2Vec2 Esperanto by Charles Pierse
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice eo
      type: common_voice
      args: eo
    metrics:
       - name: Test WER
         type: wer
         value: 12.31
---

# Wav2Vec2-Large-XLSR-53-eo

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on esperanto using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.

When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

The model can be used directly (without a language model) as follows:

```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "eo", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto")
model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
   speech_array, sampling_rate = torchaudio.load(batch["path"])
   batch["speech"] = resampler(speech_array).squeeze().numpy()
   return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
   logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```

## Evaluation

The model can be evaluated as follows on the Esperanto test data of Common Voice.

```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import jiwer

def chunked_wer(targets, predictions, chunk_size=None):
    if chunk_size is None: return jiwer.wer(targets, predictions)
    start = 0
    end = chunk_size
    H, S, D, I = 0, 0, 0, 0
    while start < len(targets):
        chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
        H = H + chunk_metrics["hits"]
        S = S + chunk_metrics["substitutions"]
        D = D + chunk_metrics["deletions"]
        I = I + chunk_metrics["insertions"]
        start += chunk_size
        end += chunk_size
    return float(S + D + I) / float(H + S + D)

test_dataset = load_dataset("common_voice", "eo", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto")
model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\„\«\(\»\)\’\']'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
   batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
   speech_array, sampling_rate = torchaudio.load(batch["path"])
   batch["speech"] = resampler(speech_array).squeeze().numpy()
   return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
   inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

   with torch.no_grad():
      logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

   pred_ids = torch.argmax(logits, dim=-1)
   batch["pred_strings"] = processor.batch_decode(pred_ids)
   return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * chunked_wer(predictions=result["pred_strings"], targets=result["sentence"],chunk_size=2000)))
```

**Test Result**: 12.31 %

## Training

The Common Voice `train`, `validation` datasets were used for training.


---
tags:
- conversational
---

# A conversational model based on the character of Sheldon Cooper from Big Bang Theory.


---
tags:
- conversational
---

# I attempted to train a bot from technoblade quotes


---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2_e
  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. -->

# layoutlmv2_e

This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 300
- mixed_precision_training: Native AMP

### Training results

### Framework versions

- Transformers 4.12.0.dev0
- Pytorch 1.8.0+cu101
- Tokenizers 0.10.3


---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2_roige
  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. -->

# layoutlmv2_roige

This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

### Framework versions

- Transformers 4.12.0.dev0
- Pytorch 1.8.0+cu101
- Datasets 1.14.0
- Tokenizers 0.10.3


# bert-base-multilingual-cased-language-detection
A model for language detection with support for 45 languages
## Model description
This model was created by fine-tuning
[bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the [common language](https://huggingface.co/datasets/common_language) dataset.
This dataset has support for 45 languages, which are listed below:
```
Arabic, Basque, Breton, Catalan, Chinese_China, Chinese_Hongkong, Chinese_Taiwan, Chuvash, Czech, Dhivehi, Dutch, English, Esperanto, Estonian, French, Frisian, Georgian, German, Greek, Hakha_Chin, Indonesian, Interlingua, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Maltese, Mongolian, Persian, Polish, Portuguese, Romanian, Romansh_Sursilvan, Russian, Sakha, Slovenian, Spanish, Swedish, Tamil, Tatar, Turkish, Ukranian, Welsh
```

## Evaluation
This model was evaluated on the test split of the [common language](https://huggingface.co/datasets/common_language) dataset, and achieved the following metrics:
* Accuracy: 97.8%


# 经典昆曲欣赏 期末作业
## KunquChat
Author: 1900012921 俞跃江


---
tags:
- conversational
---

#EmmyBot


---
language: id
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Indonesian by Galuh
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice id
      type: common_voice
      args: id
    metrics:
       - name: Test WER
         type: wer
         value: 21.07
---

# Wav2Vec2-Large-XLSR-Indonesian

This is the model for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.

## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "id", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("Galuh/wav2vec2-large-xlsr-indonesian")
model = Wav2Vec2ForCTC.from_pretrained("Galuh/wav2vec2-large-xlsr-indonesian")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
  speech_array, sampling_rate = torchaudio.load(batch["path"])
  batch["speech"] = resampler(speech_array).squeeze().numpy()
  return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
  logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```

## Evaluation

The model can be evaluated as follows on the Indonesian test data of Common Voice.

```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "id", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("Galuh/wav2vec2-large-xlsr-indonesian")
model = Wav2Vec2ForCTC.from_pretrained("Galuh/wav2vec2-large-xlsr-indonesian")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]'

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
  speech_array, sampling_rate = torchaudio.load(batch["path"])
  resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
  batch["speech"] = resampler(speech_array).squeeze().numpy()
  return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
  inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

  with torch.no_grad():
    logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

  pred_ids = torch.argmax(logits, dim=-1)
  batch["pred_strings"] = processor.batch_decode(pred_ids)
  return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```

**Test Result**: 18.32 %

## Training

The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ...  # TODO

The script used for training can be found [here](https://github.com/galuhsahid/wav2vec2-indonesian)
(will be available soon)


---
language: id
widget:
- text: "Penelitian ini bertujuan untuk menentukan identitas invertebrata laut dari Perairan Papua dengan teknik DNA barcoding"
---

# Indonesian GPT-2 finetuned on Indonesian academic journals
This is the [Indonesian gpt2-small model](https://huggingface.co/flax-community/gpt2-small-indonesian) fine-tuned to abstracts of Indonesian academic journals. All training was done on a TPUv2-8 VM sponsored by [TPU Research Cloud](https://sites.research.google/trc/).

The demo can be found [here](https://huggingface.co/spaces/flax-community/gpt2-indonesian).

## How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness,
we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='Galuh/id-journal-gpt2')
>>> set_seed(42)
>>> generator("Penelitian ini menggunakan teknik DNA barcoding untuk", max_length=30, num_return_sequences=5)

[{'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk mendeteksi perubahan genetik bakteri pada udang windu. Empat tahap telah dilakukan, meliputi preparasi media untuk larva,'},
 {'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk identifikasi gen pengasil flavonoid.  Data yang diperoleh dari hasil PCR diidentifikasi dengan teknik sekuensing'},
 {'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk mengekstraksi fragmen DNA dari sampel kulit buaya dan tulang anjing, di mana proses ini melibatkan karakterisasi enzim yang'},
 {'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk melakukan transformasi. Tahapan transformasi meliputi seleksi sel dengan urutan (2, 8, 16,..., 18) dan'},
 {'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk amplifikasi genom DNA dengan menggunakan primer TG8226 dan TG806. Metode pol'}]
```

Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Galuh/id-journal-gpt2')
model = GPT2Model.from_pretrained('Galuh/id-journal-gpt2')
text = "Ubah dengan teks apa saja."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Galuh/id-journal-gpt2')
model = TFGPT2Model.from_pretrained('Galuh/id-journal-gpt2')
text = "Ubah dengan teks apa saja."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```

## Limitations and bias
This model is originally the [Indonesian gpt2-small model](https://huggingface.co/flax-community/gpt2-small-indonesian), thus this model is also subject to the same [limitations and bias as the original model](https://huggingface.co/flax-community/gpt2-small-indonesian#limitations-and-bias). More detailed bias and analysis on this specific model is coming soon.

## Training data
The model was trained on a dataset of Indonesian journals. We only trained this model on the abstracts. We extract the abstract by writing a script to find any text that is located between the word "Abstrak" (abstract) and "Kata kunci" (keywords). The extraction script can be found [here](https://github.com/galuhsahid/id-journal-gpt2/). To separate each abstract, we also add an end of text token (`<|endoftext|>`) between each abstract.

The information of the sub-dataset and the distribution of the training and evaluation dataset are as follows:

| split | count | percentage |
| ---------- | ---------- | -------------- |
| train    | 146,248      | 90%         |
| validation    | 16,250      | 10%         |

## Training procedure
The model was trained on a TPUv2-8 VM provided by [TPU Research Cloud](https://sites.research.google/trc/). The training duration was `2h 30m 57s`.

### Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):

| dataset | train loss | eval loss | eval perplexity |
| ---------- | ---------- | -------------- | ---------- |
| Indonesian journals dataset (abstract only)    | 2.913      | 2.855         | 17.37   |

### Tracking
The training process was tracked in [TensorBoard](https://huggingface.co/Galuh/id-journal-gpt2/tensorboard).


---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sbert_large-finetuned-sent_in_news_sents_3lab
  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. -->

# sbert_large-finetuned-sent_in_news_sents_3lab

This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9443
- Accuracy: 0.8580
- F1: 0.6199

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 17

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 264  | 0.6137          | 0.8608   | 0.3084 |
| 0.524         | 2.0   | 528  | 0.6563          | 0.8722   | 0.4861 |
| 0.524         | 3.0   | 792  | 0.7110          | 0.8494   | 0.4687 |
| 0.2225        | 4.0   | 1056 | 0.7323          | 0.8608   | 0.6015 |
| 0.2225        | 5.0   | 1320 | 0.9604          | 0.8551   | 0.6185 |
| 0.1037        | 6.0   | 1584 | 0.8801          | 0.8523   | 0.5535 |
| 0.1037        | 7.0   | 1848 | 0.9443          | 0.8580   | 0.6199 |
| 0.0479        | 8.0   | 2112 | 1.0048          | 0.8608   | 0.6168 |
| 0.0479        | 9.0   | 2376 | 0.9757          | 0.8551   | 0.6097 |
| 0.0353        | 10.0  | 2640 | 1.0743          | 0.8580   | 0.6071 |
| 0.0353        | 11.0  | 2904 | 1.1216          | 0.8580   | 0.6011 |

### Framework versions

- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: rubert-base-cased-sentence-finetuned-sent_in_ru
  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. -->

# rubert-base-cased-sentence-finetuned-sent_in_ru

This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3503
- Accuracy: 0.6884
- F1: 0.6875

## 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: 15
- eval_batch_size: 15
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 441   | 0.7397          | 0.6630   | 0.6530 |
| 0.771         | 2.0   | 882   | 0.7143          | 0.6909   | 0.6905 |
| 0.5449        | 3.0   | 1323  | 0.8385          | 0.6897   | 0.6870 |
| 0.3795        | 4.0   | 1764  | 0.8851          | 0.6939   | 0.6914 |
| 0.3059        | 5.0   | 2205  | 1.0728          | 0.6933   | 0.6953 |
| 0.2673        | 6.0   | 2646  | 1.0673          | 0.7060   | 0.7020 |
| 0.2358        | 7.0   | 3087  | 1.5200          | 0.6830   | 0.6829 |
| 0.2069        | 8.0   | 3528  | 1.3439          | 0.7024   | 0.7016 |
| 0.2069        | 9.0   | 3969  | 1.3545          | 0.6830   | 0.6833 |
| 0.1724        | 10.0  | 4410  | 1.5591          | 0.6927   | 0.6902 |
| 0.1525        | 11.0  | 4851  | 1.6425          | 0.6818   | 0.6823 |
| 0.131         | 12.0  | 5292  | 1.8999          | 0.6836   | 0.6775 |
| 0.1253        | 13.0  | 5733  | 1.6959          | 0.6884   | 0.6877 |
| 0.1132        | 14.0  | 6174  | 1.9561          | 0.6776   | 0.6803 |
| 0.0951        | 15.0  | 6615  | 2.0356          | 0.6763   | 0.6754 |
| 0.1009        | 16.0  | 7056  | 1.7995          | 0.6842   | 0.6741 |
| 0.1009        | 17.0  | 7497  | 2.0638          | 0.6884   | 0.6811 |
| 0.0817        | 18.0  | 7938  | 2.1686          | 0.6884   | 0.6859 |
| 0.0691        | 19.0  | 8379  | 2.0874          | 0.6878   | 0.6889 |
| 0.0656        | 20.0  | 8820  | 2.1772          | 0.6854   | 0.6817 |
| 0.0652        | 21.0  | 9261  | 2.4018          | 0.6872   | 0.6896 |
| 0.0608        | 22.0  | 9702  | 2.2074          | 0.6770   | 0.6656 |
| 0.0677        | 23.0  | 10143 | 2.2101          | 0.6848   | 0.6793 |
| 0.0559        | 24.0  | 10584 | 2.2920          | 0.6848   | 0.6835 |
| 0.0524        | 25.0  | 11025 | 2.3503          | 0.6884   | 0.6875 |

### Framework versions

- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: rubert-base-cased-sentence-finetuned-headlines_X
  results:
  - task:
      name: Text Classification
      type: text-classification
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.952
---

<!-- 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. -->

# rubert-base-cased-sentence-finetuned-headlines_X

This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2535
- Accuracy: 0.952

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 157  | 0.2759          | 0.912    |
| No log        | 2.0   | 314  | 0.2538          | 0.936    |
| No log        | 3.0   | 471  | 0.2556          | 0.945    |
| 0.1908        | 4.0   | 628  | 0.2601          | 0.95     |
| 0.1908        | 5.0   | 785  | 0.2535          | 0.952    |

### Framework versions

- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-large-finetuned-sent_in_news
  results: []
---

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

# xlm-roberta-large-finetuned-sent_in_news

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8872
- Accuracy: 0.7273
- F1: 0.5125

## Model description

Модель ассиметрична, реагирует на метку X в тексте новости.
Попробуйте следующие примеры:

a) Агентство X понизило рейтинг банка Fitch.
b) Агентство Fitch понизило рейтинг банка X.

a) Компания Финам показала рекордную прибыль, говорят аналитики компании X.
b) Компания X показала рекордную прибыль, говорят аналитики компании Финам.

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 106  | 1.2526          | 0.6108   | 0.1508 |
| No log        | 2.0   | 212  | 1.1553          | 0.6648   | 0.1141 |
| No log        | 3.0   | 318  | 1.1150          | 0.6591   | 0.1247 |
| No log        | 4.0   | 424  | 1.0007          | 0.6705   | 0.1383 |
| 1.1323        | 5.0   | 530  | 0.9267          | 0.6733   | 0.2027 |
| 1.1323        | 6.0   | 636  | 1.0869          | 0.6335   | 0.4084 |
| 1.1323        | 7.0   | 742  | 1.1224          | 0.6932   | 0.4586 |
| 1.1323        | 8.0   | 848  | 1.2535          | 0.6307   | 0.3424 |
| 1.1323        | 9.0   | 954  | 1.4288          | 0.6932   | 0.4881 |
| 0.5252        | 10.0  | 1060 | 1.5856          | 0.6932   | 0.4739 |
| 0.5252        | 11.0  | 1166 | 1.7101          | 0.6733   | 0.4530 |
| 0.5252        | 12.0  | 1272 | 1.7330          | 0.6903   | 0.4750 |
| 0.5252        | 13.0  | 1378 | 1.8872          | 0.7273   | 0.5125 |
| 0.5252        | 14.0  | 1484 | 1.8797          | 0.7301   | 0.5033 |
| 0.1252        | 15.0  | 1590 | 1.9339          | 0.7330   | 0.5024 |
| 0.1252        | 16.0  | 1696 | 1.9632          | 0.7301   | 0.4967 |

### Framework versions

- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: rubert-base-cased-sentence-finetuned-sent_in_news_sents
  results:
  - task:
      name: Text Classification
      type: text-classification
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7224199288256228
    - name: F1
      type: f1
      value: 0.5137303178348194
---

<!-- 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. -->

# rubert-base-cased-sentence-finetuned-sent_in_news_sents

This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9506
- Accuracy: 0.7224
- F1: 0.5137

## 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: 14
- eval_batch_size: 14
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 81   | 1.0045          | 0.6690   | 0.1388 |
| No log        | 2.0   | 162  | 0.9574          | 0.6228   | 0.2980 |
| No log        | 3.0   | 243  | 1.0259          | 0.6477   | 0.3208 |
| No log        | 4.0   | 324  | 1.1262          | 0.6619   | 0.4033 |
| No log        | 5.0   | 405  | 1.3377          | 0.6299   | 0.3909 |
| No log        | 6.0   | 486  | 1.5716          | 0.6868   | 0.3624 |
| 0.6085        | 7.0   | 567  | 1.6286          | 0.6762   | 0.4130 |
| 0.6085        | 8.0   | 648  | 1.6450          | 0.6940   | 0.4775 |
| 0.6085        | 9.0   | 729  | 1.7108          | 0.7224   | 0.4920 |
| 0.6085        | 10.0  | 810  | 1.8792          | 0.7046   | 0.5028 |
| 0.6085        | 11.0  | 891  | 1.8670          | 0.7153   | 0.4992 |
| 0.6085        | 12.0  | 972  | 1.8856          | 0.7153   | 0.4934 |
| 0.0922        | 13.0  | 1053 | 1.9506          | 0.7224   | 0.5137 |
| 0.0922        | 14.0  | 1134 | 2.0363          | 0.7189   | 0.4761 |
| 0.0922        | 15.0  | 1215 | 2.0601          | 0.7224   | 0.5053 |
| 0.0922        | 16.0  | 1296 | 2.0813          | 0.7153   | 0.5038 |
| 0.0922        | 17.0  | 1377 | 2.0960          | 0.7189   | 0.5065 |
| 0.0922        | 18.0  | 1458 | 2.1060          | 0.7224   | 0.5098 |
| 0.0101        | 19.0  | 1539 | 2.1153          | 0.7260   | 0.5086 |
| 0.0101        | 20.0  | 1620 | 2.1187          | 0.7260   | 0.5086 |

### Framework versions

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


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model_index:
- name: bert-base-multilingual-cased-finetuned-cola
  results:
  - task:
      name: Text Classification
      type: text-classification
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9755
---

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

# bert-base-multilingual-cased-finetuned-cola

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1729
- Accuracy: 0.9755

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5119        | 1.0   | 625  | 0.2386          | 0.922    |
| 0.2536        | 2.0   | 1250 | 0.2055          | 0.949    |
| 0.1718        | 3.0   | 1875 | 0.1733          | 0.969    |
| 0.0562        | 4.0   | 2500 | 0.1661          | 0.974    |
| 0.0265        | 5.0   | 3125 | 0.1729          | 0.9755   |

### Framework versions

- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sbert_large-finetuned-sent_in_news_sents
  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. -->

# sbert_large-finetuned-sent_in_news_sents

This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7056
- Accuracy: 0.7301
- F1: 0.5210

## Model examples
Model responds to label X in news text. For exaple:

For 'Газпром отозвал лицензию у X, сообщает Финам' the model will return negative label -3

For 'X отозвал лицензию у Сбербанка, сообщает Финам' the model will return neutral label 0

For 'Газпром отозвал лицензию у Сбербанка, сообщает X' the model will return neutral label 0

For 'X демонстрирует высокую прибыль, сообщает Финам' the model will return positive label 1

##  Simple example of News preprocessing for Russian before BERT
```
from natasha import (
    Segmenter,
    MorphVocab,
    NewsEmbedding,
    NewsMorphTagger,
    NewsSyntaxParser,
    NewsNERTagger,
    PER,
    NamesExtractor,
    Doc
)
segmenter = Segmenter()
emb = NewsEmbedding()
morph_tagger = NewsMorphTagger(emb)
syntax_parser = NewsSyntaxParser(emb)
morph_vocab = MorphVocab()

### ----------------------------- key sentences block -----------------------------

def find_synax_tokens_with_order(doc, start, tokens, text_arr, full_str):
    ''' Находит все синтаксические токены, соответствующие заданному набору простых токенов (найденные
        для определенной NER другими функциями).
        Возвращает словарь найденных синтаксических токенов (ключ - идентификатор токена, состоящий
        из номера предложения и номера токена внутри предложения).
        Начинает поиск с указанной позиции в списке синтаксических токенов, дополнительно возвращает
        позицию остановки, с которой нужно продолжить поиск следующей NER.
    '''
    found = []
    in_str = False
    str_candidate = ''
    str_counter = 0
    if len(text_arr) == 0:
        return [], start
    for i in range(start, len(doc.syntax.tokens)):
        t = doc.syntax.tokens[i]
        if in_str:
            str_counter += 1
            if str_counter < len(text_arr) and t.text == text_arr[str_counter]:
                str_candidate += t.text
                found.append(t)
                if str_candidate == full_str:
                    return found, i+1
            else:
                in_str = False
                str_candidate = ''
                str_counter = 0
                found = []
        if t.text == text_arr[0]:
            found.append(t)
            str_candidate = t.text
            if str_candidate == full_str:
                return found, i+1
            in_str = True
    return [], len(doc.syntax.tokens)

def find_tokens_in_diap_with_order(doc, start_token, diap):
    ''' Находит все простые токены (без синтаксической информации), которые попадают в
        указанный диапазон. Эти диапазоны мы получаем из разметки NER.
        Возвращает набор найденных токенов и в виде массива токенов, и в виде массива строчек.
        Начинает поиск с указанной позиции в строке и дополнительно возвращает позицию остановки.
    '''
    found_tokens = []
    found_text = []
    full_str = ''
    next_i = 0
    for i in range(start_token, len(doc.tokens)):
        t = doc.tokens[i]
        if t.start > diap[-1]:
            next_i = i
            break
        if t.start in diap:
            found_tokens.append(t)
            found_text.append(t.text)
            full_str += t.text
    return found_tokens, found_text, full_str, next_i

def add_found_arr_to_dict(found, dict_dest):
    for synt in found:
        dict_dest.update({synt.id: synt})
    return dict_dest

def make_all_syntax_dict(doc):
    all_syntax = {}
    for synt in doc.syntax.tokens:
        all_syntax.update({synt.id: synt})
    return all_syntax

def is_consiquent(id_1, id_2):
    ''' Проверяет идут ли токены друг за другом без промежутка по ключам. '''
    id_1_list = id_1.split('_')
    id_2_list = id_2.split('_')
    if id_1_list[0] != id_2_list[0]:
        return False
    return int(id_1_list[1]) + 1 == int(id_2_list[1])

def replace_found_to(found, x_str):
    ''' Заменяет последовательность токенов NER на «заглушку». '''
    prev_id = '0_0'
    for synt in found:
        if is_consiquent(prev_id, synt.id):
            synt.text = ''
        else:
            synt.text = x_str
        prev_id = synt.id

def analyze_doc(text):
    ''' Запускает Natasha для анализа документа. '''
    doc = Doc(text)
    doc.segment(segmenter)
    doc.tag_morph(morph_tagger)
    doc.parse_syntax(syntax_parser)
    ner_tagger = NewsNERTagger(emb)
    doc.tag_ner(ner_tagger)
    return doc

def find_non_sym_syntax_short(entity_name, doc, add_X=False, x_str='X'):
    ''' Отыскивает заданную сущность в тексте, среди всех NER (возможно, в другой грамматической форме).

        entity_name - сущность, которую ищем;
        doc - документ, в котором сделан препроцессинг Natasha;
        add_X - сделать ли замену сущности на «заглушку»;
        x_str - текст замены.

        Возвращает:
        all_found_syntax - словарь всех подходящих токенов образующих искомые сущности, в котором
        в случае надобности произведена замена NER на «заглушку»;
        all_syntax - словарь всех токенов.
    '''
    all_found_syntax = {}
    current_synt_number = 0
    current_tok_number = 0

    # идем по всем найденным NER
    for span in doc.spans:
        span.normalize(morph_vocab)
        if span.type != 'ORG':
            continue
        diap = range(span.start, span.stop)
        # создаем словарь всех синтаксических элементов (ключ -- id из номера предложения и номера внутри предложения)
        all_syntax = make_all_syntax_dict(doc)
        # находим все простые токены внутри NER
        found_tokens, found_text, full_str, current_tok_number = find_tokens_in_diap_with_order(doc, current_tok_number,
                                                                                diap)
        # по найденным простым токенам находим все синтаксические токены внутри данного NER
        found, current_synt_number = find_synax_tokens_with_order(doc, current_synt_number, found_tokens, found_text,
                                                                  full_str)
        # если текст NER совпадает с указанной сущностью, то делаем замену
        if entity_name.find(span.normal) >= 0 or span.normal.find(entity_name) >= 0:
            if add_X:
                replace_found_to(found, x_str)
            all_found_syntax = add_found_arr_to_dict(found, all_found_syntax)
    return all_found_syntax, all_syntax

def key_sentences(all_found_syntax):
    ''' Находит номера предложений с искомой NER. '''
    key_sent_numb = {}
    for synt in all_found_syntax.keys():
        key_sent_numb.update({synt.split('_')[0]: 1})
    return key_sent_numb

def openinig_punct(x):
    opennings = ['«', '(']
    return x in opennings

def key_sentences_str(entitiy_name, doc, add_X=False, x_str='X', return_all=True):
    ''' Составляет окончательный текст, в котором есть только предложения, где есть ключевая сущность,
        эта сущность, если указано, заменяется на «заглушку».
    '''
    all_found_syntax, all_syntax = find_non_sym_syntax_short(entitiy_name, doc, add_X, x_str)
    key_sent_numb = key_sentences(all_found_syntax)
    str_ret = ''

    for s in all_syntax.keys():
        if (s.split('_')[0] in key_sent_numb.keys()) or (return_all):
            to_add = all_syntax[s]

            if s in all_found_syntax.keys():
                to_add = all_found_syntax[s]
            else:
                if to_add.rel == 'punct' and not openinig_punct(to_add.text):
                    str_ret = str_ret.rstrip()

            str_ret += to_add.text
            if (not openinig_punct(to_add.text)) and (to_add.text != ''):
                str_ret += ' '

    return str_ret

### ----------------------------- key entities block -----------------------------

def find_synt(doc, synt_id):
    for synt in doc.syntax.tokens:
        if synt.id == synt_id:
            return synt
    return None

def is_subj(doc, synt, recursion_list=[]):
    ''' Сообщает является ли слово подлежащим или частью сложного подлежащего. '''
    if synt.rel == 'nsubj':
        return True
    if synt.rel == 'appos':
        found_head = find_synt(doc, synt.head_id)
        if found_head.id in recursion_list:
            return False
        return is_subj(doc, found_head, recursion_list + [synt.id])
    return False

def find_subjects_in_syntax(doc):
    ''' Выдает словарик, в котором для каждой NER написано, является ли он
        подлежащим в предложении.
        Выдает стартовую позицию NER и было ли оно подлежащим (или appos)
    '''
    found_subjects = {}
    current_synt_number = 0
    current_tok_number = 0

    for span in doc.spans:
        span.normalize(morph_vocab)
        if span.type != 'ORG':
            continue

        found_subjects.update({span.start: 0})
        diap = range(span.start, span.stop)

        found_tokens, found_text, full_str, current_tok_number = find_tokens_in_diap_with_order(doc,
                                                                                current_tok_number,
                                                                                diap)

        found, current_synt_number = find_synax_tokens_with_order(doc, current_synt_number, found_tokens,
                                                                  found_text, full_str)

        found_subjects.update({span.start: 0})
        for synt in found:
            if is_subj(doc, synt):
                found_subjects.update({span.start: 1})
    return found_subjects

def entity_weight(lst, c=1):
    return c*lst[0]+lst[1]

def determine_subject(found_subjects, doc, new_agency_list, return_best=True, threshold=0.75):
    ''' Определяет ключевую NER и список самых важных NER, основываясь на том, сколько
        раз каждая из них встречается в текста вообще и сколько раз в роли подлежащего '''
    objects_arr = []
    objects_arr_ners = []
    should_continue = False
    for span in doc.spans:
        should_continue = False
        span.normalize(morph_vocab)
        if span.type != 'ORG':
            continue
        if span.normal in new_agency_list:
            continue
        for i in range(len(objects_arr)):
            t, lst = objects_arr[i]

            if t.find(span.normal) >= 0:
                lst[0] += 1
                lst[1] += found_subjects[span.start]
                should_continue = True
                break

            if span.normal.find(t) >= 0:
                objects_arr[i] = (span.normal, [lst[0]+1, lst[1]+found_subjects[span.start]])
                should_continue = True
                break

        if should_continue:
            continue
        objects_arr.append((span.normal, [1, found_subjects[span.start]]))
        objects_arr_ners.append(span.normal)

    max_weight = 0
    opt_ent = 0
    for obj in objects_arr:
        t, lst = obj
        w = entity_weight(lst)
        if max_weight < w:
            max_weight = w
            opt_ent = t

    if not return_best:
        return opt_ent, objects_arr_ners

    bests = []
    for obj in objects_arr:
        t, lst = obj
        w = entity_weight(lst)
        if max_weight*threshold < w:
            bests.append(t)

    return opt_ent, bests

text = '''В офисах Сбера начали тестировать технологию помощи посетителям в экстренных ситуациях. «Зеленая кнопка» будет
 в зонах круглосуточного обслуживания офисов банка в Воронеже, Санкт-Петербурге, Подольске, Пскове, Орле и Ярославле.
 В них находятся стенды с сенсорными кнопками, обеспечивающие связь с операторами центра мониторинга службы безопасности
 банка. Получив сигнал о помощи, оператор центра может подключиться к объекту по голосовой связи. С помощью камер
 видеонаблюдения он оценит обстановку и при необходимости вызовет полицию или скорую помощь. «Зеленой кнопкой» можно
 воспользоваться в нерабочее для отделения время, если возникла угроза жизни или здоровью. В остальных случаях помочь
 клиентам готовы сотрудники отделения банка. «Одно из направлений нашей работы в области ESG и устойчивого развития
 — это забота об обществе. И здоровье людей как высшая ценность является его основой. Поэтому задача банка в области
 безопасности гораздо масштабнее, чем обеспечение только финансовой безопасности клиентов. Этот пилотный проект
 приурочен к 180-летию Сбербанка: мы хотим, чтобы, приходя в банк, клиент чувствовал, что его жизнь и безопасность
 — наша ценность», — отметил заместитель председателя правления Сбербанка Станислав Кузнецов.'''

doc = analyze_doc(text)
key_entity = determine_subject(find_subjects_in_syntax(doc), doc, [])[0]
text_for_model = key_sentences_str(key_entity, doc, add_X=True, x_str='X', return_all=False)
```

## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 176  | 0.9504          | 0.6903   | 0.2215 |
| No log        | 2.0   | 352  | 0.9065          | 0.7159   | 0.4760 |
| 0.8448        | 3.0   | 528  | 0.9687          | 0.7045   | 0.4774 |
| 0.8448        | 4.0   | 704  | 1.2436          | 0.7045   | 0.4686 |
| 0.8448        | 5.0   | 880  | 1.4809          | 0.7273   | 0.4630 |
| 0.2074        | 6.0   | 1056 | 1.5866          | 0.7330   | 0.5185 |
| 0.2074        | 7.0   | 1232 | 1.7056          | 0.7301   | 0.5210 |
| 0.2074        | 8.0   | 1408 | 1.6982          | 0.7415   | 0.5056 |
| 0.0514        | 9.0   | 1584 | 1.8088          | 0.7273   | 0.5203 |
| 0.0514        | 10.0  | 1760 | 1.9250          | 0.7102   | 0.4879 |

### Framework versions

- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


---
language:
- ru
tags:
- PyTorch
- Transformers
license: mit

---
# Medical History Model based on ruGPT2 by @sberbank-ai

A simple model for helping medical staff to complete patient's medical histories.
Model used pretrained [sberbank-ai/rugpt3small_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2)


---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-finetune-spanish-col
  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-xlsr-finetune-spanish-col

This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7105
- Wer: 0.9824

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 7.2829        | 3.25  | 400  | 2.9632          | 1.0    |
| 2.9664        | 6.5   | 800  | 2.8494          | 1.0542 |
| 2.8353        | 9.76  | 1200 | 2.8352          | 1.0101 |
| 2.7863        | 13.01 | 1600 | 2.7421          | 0.9837 |
| 2.762         | 16.26 | 2000 | 2.7254          | 0.9861 |
| 2.7483        | 19.51 | 2400 | 2.7228          | 0.9874 |
| 2.7482        | 22.76 | 2800 | 2.7228          | 0.9999 |
| 2.7373        | 26.02 | 3200 | 2.7163          | 0.9824 |
| 2.7328        | 29.27 | 3600 | 2.7105          | 0.9824 |

### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3


---
language:
- ne
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- openslr
model-index:
- name: wav2vec2-large-xlsr-300m-nepali
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: openslr
      type: openslr
      args: ne
    metrics:
       - name: Test WER
         type: wer
         value: 25.02
---


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-demo-colab
  results: []
---

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

# wav2vec2-xls-r-300m-demo-colab

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9475
- eval_wer: 1.0377
- eval_runtime: 70.5646
- eval_samples_per_second: 25.239
- eval_steps_per_second: 3.16
- epoch: 21.05
- step: 2000

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 300
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0