---
license: mit
base_model: microsoft/biogpt
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  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. -->

# bert-finetuned-ner

This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co./microsoft/biogpt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1491
- Precision: 0.4416
- Recall: 0.5578
- F1: 0.4930
- Accuracy: 0.9576

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3004        | 1.0   | 679  | 0.1688          | 0.3313    | 0.4244 | 0.3721 | 0.9476   |
| 0.1682        | 2.0   | 1358 | 0.1706          | 0.3805    | 0.5667 | 0.4553 | 0.9503   |
| 0.0935        | 3.0   | 2037 | 0.1491          | 0.4416    | 0.5578 | 0.4930 | 0.9576   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2