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# Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE-Aug-NoEWC

This model is a fine-tuned version of [StivenLancheros/Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE2-NoEWC](https://huggingface.co/StivenLancheros/Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE2-NoEWC) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4245
- Precision: 0.7158
- Recall: 0.7763
- F1: 0.7448
- Accuracy: 0.9230

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4287        | 1.0   | 1360  | 0.4776          | 0.6125    | 0.7809 | 0.6865 | 0.8944   |
| 0.274         | 2.0   | 2720  | 0.4727          | 0.6370    | 0.7705 | 0.6974 | 0.9026   |
| 0.2219        | 3.0   | 4080  | 0.4405          | 0.6790    | 0.7695 | 0.7215 | 0.9134   |
| 0.1964        | 4.0   | 5440  | 0.4245          | 0.7158    | 0.7763 | 0.7448 | 0.9230   |
| 0.1599        | 5.0   | 6800  | 0.4378          | 0.7029    | 0.7732 | 0.7364 | 0.9204   |
| 0.1489        | 6.0   | 8160  | 0.4324          | 0.7018    | 0.7749 | 0.7365 | 0.9196   |
| 0.1342        | 7.0   | 9520  | 0.4534          | 0.7131    | 0.7867 | 0.7481 | 0.9216   |
| 0.12          | 8.0   | 10880 | 0.4532          | 0.7230    | 0.7865 | 0.7534 | 0.9243   |
| 0.1135        | 9.0   | 12240 | 0.4688          | 0.7155    | 0.7705 | 0.7420 | 0.9206   |
| 0.107         | 10.0  | 13600 | 0.4713          | 0.7214    | 0.7863 | 0.7524 | 0.9241   |
| 0.1004        | 11.0  | 14960 | 0.4594          | 0.7278    | 0.7823 | 0.7541 | 0.9251   |
| 0.093         | 12.0  | 16320 | 0.4564          | 0.7343    | 0.7937 | 0.7628 | 0.9275   |
| 0.0862        | 13.0  | 17680 | 0.4699          | 0.7242    | 0.7970 | 0.7588 | 0.9262   |
| 0.0788        | 14.0  | 19040 | 0.4745          | 0.7397    | 0.7935 | 0.7657 | 0.9285   |
| 0.0785        | 15.0  | 20400 | 0.4822          | 0.7389    | 0.7998 | 0.7682 | 0.9283   |
| 0.0711        | 16.0  | 21760 | 0.5031          | 0.7255    | 0.7964 | 0.7593 | 0.9264   |
| 0.073         | 17.0  | 23120 | 0.5038          | 0.7317    | 0.7976 | 0.7632 | 0.9274   |
| 0.0695        | 18.0  | 24480 | 0.4860          | 0.7411    | 0.8016 | 0.7702 | 0.9296   |
| 0.0649        | 19.0  | 25840 | 0.4978          | 0.7376    | 0.7977 | 0.7665 | 0.9285   |
| 0.0605        | 20.0  | 27200 | 0.4983          | 0.7371    | 0.7974 | 0.7661 | 0.9286   |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE-Aug-NoEWC", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE-Aug-NoEWC
====================================================================


This model is a fine-tuned version of StivenLancheros/Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE2-NoEWC on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4245
* Precision: 0.7158
* Recall: 0.7763
* F1: 0.7448
* Accuracy: 0.9230


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2