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# biobert_model

This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9645
- Accuracy: 0.8711
- F1: 0.8475

## 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: 1e-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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 334  | 0.6463          | 0.6897   | 0.7129 |
| 0.4503        | 2.0   | 668  | 0.3590          | 0.8651   | 0.8269 |
| 0.2715        | 3.0   | 1002 | 0.4549          | 0.8711   | 0.8252 |
| 0.2715        | 4.0   | 1336 | 0.6012          | 0.8681   | 0.8434 |
| 0.1335        | 5.0   | 1670 | 0.6307          | 0.8576   | 0.8313 |
| 0.0746        | 6.0   | 2004 | 0.7658          | 0.8636   | 0.8366 |
| 0.0746        | 7.0   | 2338 | 0.8658          | 0.8666   | 0.8436 |
| 0.0307        | 8.0   | 2672 | 0.8312          | 0.8711   | 0.8453 |
| 0.0148        | 9.0   | 3006 | 0.8922          | 0.8651   | 0.8421 |
| 0.0148        | 10.0  | 3340 | 0.8761          | 0.8726   | 0.8490 |
| 0.0128        | 11.0  | 3674 | 0.9329          | 0.8681   | 0.8462 |
| 0.0105        | 12.0  | 4008 | 0.9512          | 0.8666   | 0.8441 |
| 0.0105        | 13.0  | 4342 | 0.9553          | 0.8711   | 0.8475 |
| 0.0069        | 14.0  | 4676 | 0.9731          | 0.8681   | 0.8445 |
| 0.0046        | 15.0  | 5010 | 0.9645          | 0.8711   | 0.8475 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "biobert_model", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

biobert\_model
==============


This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9645
* Accuracy: 0.8711
* F1: 0.8475


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: 1e-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: 15


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 15 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 15 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3