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# biobert-base-cased-v1.2-finetuned-ner

This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3655
- Precision: 0.7151
- Recall: 0.8301
- F1: 0.7683
- Accuracy: 0.9050

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.257         | 1.0   | 1160 | 0.2889          | 0.7091    | 0.8222 | 0.7615 | 0.9021   |
| 0.1962        | 2.0   | 2320 | 0.3009          | 0.7154    | 0.8259 | 0.7667 | 0.9048   |
| 0.158         | 3.0   | 3480 | 0.3214          | 0.7098    | 0.8228 | 0.7621 | 0.9031   |
| 0.131         | 4.0   | 4640 | 0.3385          | 0.7174    | 0.8292 | 0.7692 | 0.9055   |
| 0.1081        | 5.0   | 5800 | 0.3655          | 0.7151    | 0.8301 | 0.7683 | 0.9050   |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.2
- Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["jnlpba"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobert-base-cased-v1.2-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "jnlpba", "type": "jnlpba", "args": "jnlpba"}, "metrics": [{"type": "precision", "value": 0.7150627220423177, "name": "Precision"}, {"type": "recall", "value": 0.8300729927007299, "name": "Recall"}, {"type": "f1", "value": 0.7682875335686659, "name": "F1"}, {"type": "accuracy", "value": 0.90497239665345, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:jnlpba model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us 

biobert-base-cased-v1.2-finetuned-ner
=====================================


This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the jnlpba dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3655
* Precision: 0.7151
* Recall: 0.8301
* F1: 0.7683
* Accuracy: 0.9050


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



### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1+cu102
* Datasets 1.13.2
* Tokenizers 0.10.3



### 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 ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1+cu102
* Datasets 1.13.2
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #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: 5 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1+cu102
* Datasets 1.13.2
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #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: 5### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1+cu102
* Datasets 1.13.2
* Tokenizers 0.10.3