metadata
license: mit
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NLP-HIBA_BiomedNLP-BiomedBERT-base-pretrained-model
results: []
NLP-HIBA_BiomedNLP-BiomedBERT-base-pretrained-model
This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1766
- Precision: 0.5977
- Recall: 0.5730
- F1: 0.5851
- Accuracy: 0.9539
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 71 | 0.1981 | 0.2581 | 0.2239 | 0.2398 | 0.9266 |
| No log | 2.0 | 142 | 0.1616 | 0.4514 | 0.3692 | 0.4062 | 0.9444 |
| No log | 3.0 | 213 | 0.1514 | 0.5233 | 0.4727 | 0.4967 | 0.9482 |
| No log | 4.0 | 284 | 0.1863 | 0.4522 | 0.5546 | 0.4982 | 0.9352 |
| No log | 5.0 | 355 | 0.1582 | 0.5665 | 0.5245 | 0.5447 | 0.9498 |
| No log | 6.0 | 426 | 0.1571 | 0.5915 | 0.5305 | 0.5593 | 0.9529 |
| No log | 7.0 | 497 | 0.1652 | 0.5849 | 0.5586 | 0.5714 | 0.9527 |
| 0.1311 | 8.0 | 568 | 0.1676 | 0.5858 | 0.5738 | 0.5798 | 0.9528 |
| 0.1311 | 9.0 | 639 | 0.1748 | 0.5990 | 0.5562 | 0.5768 | 0.9537 |
| 0.1311 | 10.0 | 710 | 0.1766 | 0.5977 | 0.5730 | 0.5851 | 0.9539 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1