|
--- |
|
license: apache-2.0 |
|
base_model: bert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- precision |
|
- recall |
|
model-index: |
|
- name: BERT_Text_classification_clean |
|
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. --> |
|
|
|
# BERT_Text_classification_clean |
|
|
|
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: 0.5562 |
|
- Accuracy: 0.8708 |
|
- F1: 0.8634 |
|
- Precision: 0.8673 |
|
- Recall: 0.8632 |
|
|
|
## 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: 150 |
|
- num_epochs: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
|
| 2.9977 | 0.07 | 50 | 2.9491 | 0.0609 | 0.0334 | 0.0689 | 0.0589 | |
|
| 2.6422 | 0.14 | 100 | 2.1816 | 0.4223 | 0.3423 | 0.4771 | 0.4054 | |
|
| 1.8378 | 0.21 | 150 | 1.4580 | 0.6045 | 0.5381 | 0.5940 | 0.5858 | |
|
| 1.2155 | 0.28 | 200 | 1.0783 | 0.7019 | 0.6600 | 0.6722 | 0.6851 | |
|
| 0.8869 | 0.35 | 250 | 0.8923 | 0.7491 | 0.7237 | 0.7474 | 0.7309 | |
|
| 0.821 | 0.42 | 300 | 0.8259 | 0.7667 | 0.7453 | 0.7527 | 0.7494 | |
|
| 0.7908 | 0.49 | 350 | 0.8119 | 0.7694 | 0.7417 | 0.7745 | 0.7510 | |
|
| 0.6891 | 0.56 | 400 | 0.7503 | 0.7827 | 0.7566 | 0.7834 | 0.7659 | |
|
| 0.6517 | 0.64 | 450 | 0.7300 | 0.7840 | 0.7592 | 0.8025 | 0.7668 | |
|
| 0.582 | 0.71 | 500 | 0.7140 | 0.8003 | 0.7874 | 0.7946 | 0.7868 | |
|
| 0.5354 | 0.78 | 550 | 0.7101 | 0.7789 | 0.7730 | 0.7973 | 0.7731 | |
|
| 0.6222 | 0.85 | 600 | 0.6393 | 0.8105 | 0.7917 | 0.8066 | 0.7941 | |
|
| 0.5159 | 0.92 | 650 | 0.6774 | 0.7946 | 0.7771 | 0.8021 | 0.7792 | |
|
| 0.5611 | 0.99 | 700 | 0.6016 | 0.8218 | 0.8030 | 0.8211 | 0.8064 | |
|
| 0.3468 | 1.06 | 750 | 0.6555 | 0.8113 | 0.7963 | 0.8126 | 0.7972 | |
|
| 0.3636 | 1.13 | 800 | 0.6447 | 0.8139 | 0.8015 | 0.8182 | 0.8014 | |
|
| 0.2689 | 1.2 | 850 | 0.5984 | 0.8332 | 0.8230 | 0.8294 | 0.8233 | |
|
| 0.3393 | 1.27 | 900 | 0.6076 | 0.8334 | 0.8253 | 0.8337 | 0.8254 | |
|
| 0.3395 | 1.34 | 950 | 0.5933 | 0.8364 | 0.8253 | 0.8320 | 0.8250 | |
|
| 0.2421 | 1.41 | 1000 | 0.5973 | 0.8371 | 0.8256 | 0.8369 | 0.8254 | |
|
| 0.2708 | 1.48 | 1050 | 0.6241 | 0.8348 | 0.8244 | 0.8311 | 0.8252 | |
|
| 0.2972 | 1.55 | 1100 | 0.6012 | 0.8395 | 0.8292 | 0.8400 | 0.8274 | |
|
| 0.2694 | 1.62 | 1150 | 0.6092 | 0.8425 | 0.8348 | 0.8497 | 0.8352 | |
|
| 0.2738 | 1.69 | 1200 | 0.5839 | 0.8501 | 0.8426 | 0.8474 | 0.8414 | |
|
| 0.2731 | 1.77 | 1250 | 0.5573 | 0.8542 | 0.8446 | 0.8491 | 0.8444 | |
|
| 0.2472 | 1.84 | 1300 | 0.5565 | 0.8546 | 0.8476 | 0.8547 | 0.8469 | |
|
| 0.1901 | 1.91 | 1350 | 0.5555 | 0.8586 | 0.8521 | 0.8568 | 0.8519 | |
|
| 0.217 | 1.98 | 1400 | 0.5737 | 0.8548 | 0.8446 | 0.8549 | 0.8447 | |
|
| 0.1559 | 2.05 | 1450 | 0.5715 | 0.8578 | 0.8494 | 0.8559 | 0.8481 | |
|
| 0.1457 | 2.12 | 1500 | 0.5425 | 0.8650 | 0.8570 | 0.8605 | 0.8566 | |
|
| 0.1067 | 2.19 | 1550 | 0.5734 | 0.8655 | 0.8564 | 0.8644 | 0.8560 | |
|
| 0.1183 | 2.26 | 1600 | 0.5509 | 0.8659 | 0.8587 | 0.8617 | 0.8585 | |
|
| 0.1507 | 2.33 | 1650 | 0.5806 | 0.8609 | 0.8528 | 0.8561 | 0.8529 | |
|
| 0.1446 | 2.4 | 1700 | 0.5629 | 0.8700 | 0.8633 | 0.8683 | 0.8626 | |
|
| 0.1234 | 2.47 | 1750 | 0.5690 | 0.8667 | 0.8595 | 0.8625 | 0.8595 | |
|
| 0.0921 | 2.54 | 1800 | 0.5597 | 0.8640 | 0.8567 | 0.8599 | 0.8566 | |
|
| 0.0866 | 2.61 | 1850 | 0.5759 | 0.8664 | 0.8593 | 0.8631 | 0.8586 | |
|
| 0.0874 | 2.68 | 1900 | 0.5790 | 0.8680 | 0.8599 | 0.8658 | 0.8599 | |
|
| 0.133 | 2.75 | 1950 | 0.5633 | 0.8696 | 0.8625 | 0.8674 | 0.8624 | |
|
| 0.1309 | 2.82 | 2000 | 0.5580 | 0.8712 | 0.8640 | 0.8678 | 0.8640 | |
|
| 0.1011 | 2.9 | 2050 | 0.5584 | 0.8711 | 0.8637 | 0.8680 | 0.8636 | |
|
| 0.0857 | 2.97 | 2100 | 0.5562 | 0.8708 | 0.8634 | 0.8673 | 0.8632 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.36.2 |
|
- Pytorch 2.1.2+cu121 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.2 |
|
|
