|
--- |
|
license: apache-2.0 |
|
base_model: distilbert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
- precision |
|
- recall |
|
- accuracy |
|
model-index: |
|
- name: distil-bert-imeocap |
|
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. --> |
|
|
|
# distil-bert-imeocap |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.8186 |
|
- F1: 0.6341 |
|
- Precision: 0.6365 |
|
- Recall: 0.6365 |
|
- 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: 2e-05 |
|
- train_batch_size: 64 |
|
- eval_batch_size: 64 |
|
- 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 | F1 | Precision | Recall | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| |
|
| 0.1961 | 1.0 | 74 | 1.6080 | 0.6314 | 0.6285 | 0.6385 | 0.6385 | |
|
| 0.1845 | 2.0 | 148 | 1.7125 | 0.6298 | 0.6317 | 0.6385 | 0.6385 | |
|
| 0.1717 | 3.0 | 222 | 1.9402 | 0.6226 | 0.6364 | 0.6385 | 0.6385 | |
|
| 0.176 | 4.0 | 296 | 1.8028 | 0.6169 | 0.6253 | 0.6192 | 0.6192 | |
|
| 0.1679 | 5.0 | 370 | 1.6948 | 0.6243 | 0.6285 | 0.625 | 0.625 | |
|
| 0.168 | 6.0 | 444 | 1.8304 | 0.6317 | 0.6336 | 0.6385 | 0.6385 | |
|
| 0.1617 | 7.0 | 518 | 1.7457 | 0.6286 | 0.6310 | 0.6308 | 0.6308 | |
|
| 0.1677 | 8.0 | 592 | 1.8071 | 0.6422 | 0.6382 | 0.65 | 0.65 | |
|
| 0.171 | 9.0 | 666 | 1.8177 | 0.6323 | 0.6326 | 0.6385 | 0.6385 | |
|
| 0.1683 | 10.0 | 740 | 1.8265 | 0.6347 | 0.6370 | 0.6365 | 0.6365 | |
|
| 0.1808 | 11.0 | 814 | 1.7734 | 0.6304 | 0.6365 | 0.6308 | 0.6308 | |
|
| 0.1757 | 12.0 | 888 | 1.7727 | 0.6244 | 0.6296 | 0.6231 | 0.6231 | |
|
| 0.1897 | 13.0 | 962 | 1.8449 | 0.6374 | 0.6377 | 0.6404 | 0.6404 | |
|
| 0.1674 | 14.0 | 1036 | 1.8244 | 0.6455 | 0.6462 | 0.6481 | 0.6481 | |
|
| 0.1746 | 15.0 | 1110 | 1.8186 | 0.6341 | 0.6365 | 0.6365 | 0.6365 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
