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# bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner

This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the bc2gm_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1505
- Precision: 0.7854
- Recall: 0.8158
- F1: 0.8003
- Accuracy: 0.9759

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0981        | 1.0   | 782  | 0.0712          | 0.7228    | 0.7948 | 0.7571 | 0.9724   |
| 0.0509        | 2.0   | 1564 | 0.0687          | 0.7472    | 0.8199 | 0.7818 | 0.9746   |
| 0.0121        | 3.0   | 2346 | 0.0740          | 0.7725    | 0.8011 | 0.7866 | 0.9747   |
| 0.0001        | 4.0   | 3128 | 0.1009          | 0.7618    | 0.8251 | 0.7922 | 0.9741   |
| 0.0042        | 5.0   | 3910 | 0.1106          | 0.7757    | 0.8185 | 0.7965 | 0.9754   |
| 0.0015        | 6.0   | 4692 | 0.1182          | 0.7812    | 0.8111 | 0.7958 | 0.9758   |
| 0.0001        | 7.0   | 5474 | 0.1283          | 0.7693    | 0.8275 | 0.7973 | 0.9753   |
| 0.0072        | 8.0   | 6256 | 0.1376          | 0.7863    | 0.8158 | 0.8008 | 0.9762   |
| 0.0045        | 9.0   | 7038 | 0.1468          | 0.7856    | 0.8180 | 0.8015 | 0.9761   |
| 0.0           | 10.0  | 7820 | 0.1505          | 0.7854    | 0.8158 | 0.8003 | 0.9759   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["bc2gm_corpus"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "bc2gm_corpus", "type": "bc2gm_corpus", "args": "bc2gm_corpus"}, "metrics": [{"type": "precision", "value": 0.7853881278538812, "name": "Precision"}, {"type": "recall", "value": 0.8158102766798419, "name": "Recall"}, {"type": "f1", "value": 0.8003101977510663, "name": "F1"}, {"type": "accuracy", "value": 0.9758965601366187, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:bc2gm_corpus license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-bc2gm_corpus #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

bc2gm\_corpus-Bio\_ClinicalBERT-finetuned-ner
=============================================


This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the bc2gm\_corpus dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1505
* Precision: 0.7854
* Recall: 0.8158
* F1: 0.8003
* Accuracy: 0.9759


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: 10


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-bc2gm_corpus #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-bc2gm_corpus #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1