|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: google-bert/bert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
model-index: |
|
- name: bert-base-uncased_08112024T161410 |
|
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-base-uncased_08112024T161410 |
|
|
|
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4679 |
|
- F1: 0.8733 |
|
- Learning Rate: 0.0 |
|
|
|
## 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: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 32 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_steps: 600 |
|
- num_epochs: 30 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 | Rate | |
|
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:| |
|
| No log | 0.9942 | 86 | 1.5646 | 0.3444 | 0.0000 | |
|
| No log | 2.0 | 173 | 1.1261 | 0.5492 | 0.0000 | |
|
| No log | 2.9942 | 259 | 0.8378 | 0.6911 | 0.0000 | |
|
| No log | 4.0 | 346 | 0.7016 | 0.7403 | 0.0001 | |
|
| No log | 4.9942 | 432 | 0.5643 | 0.8175 | 0.0001 | |
|
| 0.9953 | 6.0 | 519 | 0.4947 | 0.8366 | 0.0001 | |
|
| 0.9953 | 6.9942 | 605 | 0.5260 | 0.8488 | 0.0001 | |
|
| 0.9953 | 8.0 | 692 | 0.6001 | 0.8525 | 0.0001 | |
|
| 0.9953 | 8.9942 | 778 | 0.4679 | 0.8733 | 0.0001 | |
|
| 0.9953 | 10.0 | 865 | 0.5398 | 0.8779 | 0.0001 | |
|
| 0.9953 | 10.9942 | 951 | 0.5633 | 0.8765 | 0.0001 | |
|
| 0.2162 | 12.0 | 1038 | 0.6918 | 0.8640 | 0.0001 | |
|
| 0.2162 | 12.9942 | 1124 | 0.7215 | 0.8725 | 0.0001 | |
|
| 0.2162 | 14.0 | 1211 | 0.7590 | 0.8755 | 0.0001 | |
|
| 0.2162 | 14.9942 | 1297 | 0.8360 | 0.8775 | 0.0001 | |
|
| 0.2162 | 16.0 | 1384 | 0.8535 | 0.8766 | 0.0001 | |
|
| 0.2162 | 16.9942 | 1470 | 0.8997 | 0.8743 | 0.0001 | |
|
| 0.059 | 18.0 | 1557 | 0.8916 | 0.8770 | 0.0001 | |
|
| 0.059 | 18.9942 | 1643 | 0.8122 | 0.8835 | 0.0000 | |
|
| 0.059 | 20.0 | 1730 | 0.8315 | 0.8852 | 0.0000 | |
|
| 0.059 | 20.9942 | 1816 | 0.8983 | 0.8783 | 0.0000 | |
|
| 0.059 | 22.0 | 1903 | 0.8399 | 0.8886 | 0.0000 | |
|
| 0.059 | 22.9942 | 1989 | 0.8251 | 0.8887 | 0.0000 | |
|
| 0.0109 | 24.0 | 2076 | 0.8765 | 0.8845 | 0.0000 | |
|
| 0.0109 | 24.9942 | 2162 | 0.8813 | 0.8844 | 0.0000 | |
|
| 0.0109 | 26.0 | 2249 | 0.8872 | 0.8850 | 0.0000 | |
|
| 0.0109 | 26.9942 | 2335 | 0.8876 | 0.8851 | 0.0000 | |
|
| 0.0109 | 28.0 | 2422 | 0.8902 | 0.8853 | 0.0000 | |
|
| 0.0025 | 28.9942 | 2508 | 0.8904 | 0.8853 | 4e-07 | |
|
| 0.0025 | 29.8266 | 2580 | 0.8904 | 0.8853 | 0.0 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.5.1+cu124 |
|
- Datasets 3.1.0 |
|
- Tokenizers 0.19.1 |
|
|
