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---
license: mit
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MLMA_lab9
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: 0.12389380530973451
- name: Recall
type: recall
value: 0.017789072426937738
- name: F1
type: f1
value: 0.031111111111111107
- name: Accuracy
type: accuracy
value: 0.9177455063979887
---
<!-- 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
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: 0.3328
- Precision: 0.1239
- Recall: 0.0178
- F1: 0.0311
- Accuracy: 0.9177
## 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: 0.0001
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2903 | 1.0 | 680 | 0.3328 | 0.1239 | 0.0178 | 0.0311 | 0.9177 |
| 0.2907 | 2.0 | 1360 | 0.3328 | 0.1239 | 0.0178 | 0.0311 | 0.9177 |
| 0.2885 | 3.0 | 2040 | 0.3328 | 0.1239 | 0.0178 | 0.0311 | 0.9177 |
| 0.2861 | 4.0 | 2720 | 0.3328 | 0.1239 | 0.0178 | 0.0311 | 0.9177 |
| 0.2948 | 5.0 | 3400 | 0.3328 | 0.1239 | 0.0178 | 0.0311 | 0.9177 |
| 0.2881 | 6.0 | 4080 | 0.3328 | 0.1239 | 0.0178 | 0.0311 | 0.9177 |
| 0.292 | 7.0 | 4760 | 0.3328 | 0.1239 | 0.0178 | 0.0311 | 0.9177 |
| 0.2882 | 8.0 | 5440 | 0.3328 | 0.1239 | 0.0178 | 0.0311 | 0.9177 |
| 0.2905 | 9.0 | 6120 | 0.3328 | 0.1239 | 0.0178 | 0.0311 | 0.9177 |
| 0.2881 | 10.0 | 6800 | 0.3328 | 0.1239 | 0.0178 | 0.0311 | 0.9177 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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