---
license: mit
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MLMA_lab9_task2
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: validation
      args: ncbi_disease
    metrics:
    - name: Precision
      type: precision
      value: 0.015873015873015872
    - name: Recall
      type: recall
      value: 0.14866581956797967
    - name: F1
      type: f1
      value: 0.028683500858053445
    - name: Accuracy
      type: accuracy
      value: 0.6365342039100904
---

<!-- 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. -->

# MLMA_lab9_task2

This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2509
- Precision: 0.0159
- Recall: 0.1487
- F1: 0.0287
- Accuracy: 0.6365

## 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: 5e-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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.153         | 1.0   | 680   | 1.0671          | 0.0122    | 0.1258 | 0.0223 | 0.5452   |
| 1.02          | 2.0   | 1360  | 1.0418          | 0.0098    | 0.0203 | 0.0132 | 0.6791   |
| 0.9552        | 3.0   | 2040  | 1.0269          | 0.0135    | 0.1677 | 0.0250 | 0.5282   |
| 0.926         | 4.0   | 2720  | 1.0390          | 0.0143    | 0.0940 | 0.0248 | 0.6686   |
| 0.9156        | 5.0   | 3400  | 1.0200          | 0.0135    | 0.2046 | 0.0253 | 0.4679   |
| 0.8791        | 6.0   | 4080  | 1.0543          | 0.0131    | 0.2745 | 0.0250 | 0.3149   |
| 0.8672        | 7.0   | 4760  | 1.0545          | 0.0141    | 0.2732 | 0.0267 | 0.3471   |
| 0.8627        | 8.0   | 5440  | 1.0734          | 0.0145    | 0.0826 | 0.0246 | 0.7220   |
| 0.8375        | 9.0   | 6120  | 1.1068          | 0.0156    | 0.1410 | 0.0281 | 0.6451   |
| 0.8235        | 10.0  | 6800  | 1.0796          | 0.0158    | 0.1537 | 0.0286 | 0.6210   |
| 0.8157        | 11.0  | 7480  | 1.1476          | 0.0143    | 0.1690 | 0.0263 | 0.5737   |
| 0.7957        | 12.0  | 8160  | 1.1369          | 0.0143    | 0.1525 | 0.0262 | 0.6155   |
| 0.7937        | 13.0  | 8840  | 1.2014          | 0.0151    | 0.1741 | 0.0278 | 0.5808   |
| 0.7765        | 14.0  | 9520  | 1.2249          | 0.0160    | 0.1449 | 0.0289 | 0.6443   |
| 0.7661        | 15.0  | 10200 | 1.2509          | 0.0159    | 0.1487 | 0.0287 | 0.6365   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3