|
|
--- |
|
|
license: apache-2.0 |
|
|
base_model: bert-base-uncased |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
- f1 |
|
|
- precision |
|
|
- recall |
|
|
model-index: |
|
|
- name: TTC4900Model |
|
|
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. --> |
|
|
|
|
|
# TTC4900Model |
|
|
|
|
|
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.0667 |
|
|
- Accuracy: 0.9859 |
|
|
- F1: 0.9418 |
|
|
- Precision: 0.9562 |
|
|
- Recall: 0.9309 |
|
|
|
|
|
## 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: 16 |
|
|
- eval_batch_size: 32 |
|
|
- seed: 42 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- lr_scheduler_warmup_steps: 100 |
|
|
- num_epochs: 3 |
|
|
- mixed_precision_training: Native AMP |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
|
|
| 1.5192 | 0.3289 | 50 | 0.9342 | 0.7575 | 0.1077 | 0.0947 | 0.125 | |
|
|
| 0.6007 | 0.6579 | 100 | 0.4256 | 0.8767 | 0.3189 | 0.2983 | 0.3445 | |
|
|
| 0.2704 | 0.9868 | 150 | 0.2471 | 0.9561 | 0.6877 | 0.6916 | 0.6917 | |
|
|
| 0.1382 | 1.3158 | 200 | 0.1346 | 0.9727 | 0.8789 | 0.9054 | 0.8698 | |
|
|
| 0.1132 | 1.6447 | 250 | 0.0824 | 0.9876 | 0.9350 | 0.9701 | 0.9103 | |
|
|
| 0.0981 | 1.9737 | 300 | 0.0431 | 0.9942 | 0.9749 | 0.9892 | 0.9635 | |
|
|
| 0.0369 | 2.3026 | 350 | 0.0466 | 0.9892 | 0.9376 | 0.9576 | 0.9275 | |
|
|
| 0.0373 | 2.6316 | 400 | 0.0413 | 0.9909 | 0.9602 | 0.9580 | 0.9630 | |
|
|
| 0.0235 | 2.9605 | 450 | 0.0407 | 0.9909 | 0.9613 | 0.9600 | 0.9630 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.41.2 |
|
|
- Pytorch 2.3.0+cu121 |
|
|
- Tokenizers 0.19.1 |
|
|
|
