|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: Yepes_0.0001_250 |
|
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. --> |
|
|
|
# Yepes_0.0001_250 |
|
|
|
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1555 |
|
- Precision: 0.5922 |
|
- Recall: 0.4552 |
|
- F1: 0.5148 |
|
- Accuracy: 0.9768 |
|
|
|
## 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: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- training_steps: 500 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.4065 | 1.39 | 25 | 0.2115 | 0.0 | 0.0 | 0.0 | 0.9672 | |
|
| 0.1995 | 2.78 | 50 | 0.2120 | 0.0 | 0.0 | 0.0 | 0.9672 | |
|
| 0.1995 | 4.17 | 75 | 0.2108 | 0.0 | 0.0 | 0.0 | 0.9672 | |
|
| 0.1694 | 5.56 | 100 | 0.1646 | 0.0 | 0.0 | 0.0 | 0.9672 | |
|
| 0.1493 | 6.94 | 125 | 0.1513 | 0.0 | 0.0 | 0.0 | 0.9672 | |
|
| 0.1266 | 8.33 | 150 | 0.1446 | 0.0 | 0.0 | 0.0 | 0.9672 | |
|
| 0.106 | 9.72 | 175 | 0.1396 | 0.4019 | 0.2139 | 0.2792 | 0.9704 | |
|
| 0.086 | 11.11 | 200 | 0.1162 | 0.5037 | 0.3408 | 0.4065 | 0.9740 | |
|
| 0.0613 | 12.5 | 225 | 0.1230 | 0.5015 | 0.4104 | 0.4514 | 0.9740 | |
|
| 0.047 | 13.89 | 250 | 0.1306 | 0.5333 | 0.4378 | 0.4809 | 0.9753 | |
|
| 0.0351 | 15.28 | 275 | 0.1351 | 0.5629 | 0.4453 | 0.4972 | 0.9757 | |
|
| 0.0266 | 16.67 | 300 | 0.1453 | 0.5617 | 0.4303 | 0.4873 | 0.9765 | |
|
| 0.02 | 18.06 | 325 | 0.1441 | 0.5573 | 0.4478 | 0.4966 | 0.9757 | |
|
| 0.0153 | 19.44 | 350 | 0.1555 | 0.5922 | 0.4552 | 0.5148 | 0.9768 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.27.4 |
|
- Pytorch 1.13.1+cu116 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.2 |
|
|
