---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: phibert-finetuned-ner-new-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. -->

# phibert-finetuned-ner-new-1

This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0184
- Precision: 0.9485
- Recall: 0.9533
- F1: 0.9509
- Accuracy: 0.9963

## 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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.044         | 1.0   | 5915  | 0.0236          | 0.9014    | 0.9109 | 0.9061 | 0.9937   |
| 0.0266        | 2.0   | 11830 | 0.0209          | 0.9095    | 0.9271 | 0.9182 | 0.9943   |
| 0.0101        | 3.0   | 17745 | 0.0191          | 0.9335    | 0.9452 | 0.9393 | 0.9955   |
| 0.0104        | 4.0   | 23660 | 0.0181          | 0.9349    | 0.9483 | 0.9415 | 0.9959   |
| 0.0039        | 5.0   | 29575 | 0.0184          | 0.9485    | 0.9533 | 0.9509 | 0.9963   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3