|
|
--- |
|
|
library_name: transformers |
|
|
license: apache-2.0 |
|
|
base_model: albert/albert-base-v2 |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
- f1 |
|
|
- precision |
|
|
- recall |
|
|
model-index: |
|
|
- name: modelsent_test |
|
|
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. --> |
|
|
|
|
|
# modelsent_test |
|
|
|
|
|
This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on the None dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.2379 |
|
|
- Accuracy: 0.9261 |
|
|
- F1: 0.9261 |
|
|
- Precision: 0.9261 |
|
|
- Recall: 0.9261 |
|
|
- Accuracy Label Negative: 0.9242 |
|
|
- Accuracy Label Positive: 0.9278 |
|
|
|
|
|
## 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: 16 |
|
|
- eval_batch_size: 16 |
|
|
- seed: 42 |
|
|
- gradient_accumulation_steps: 2 |
|
|
- total_train_batch_size: 32 |
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: linear |
|
|
- lr_scheduler_warmup_steps: 500 |
|
|
- num_epochs: 3 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Accuracy Label Negative | Accuracy Label Positive | |
|
|
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----------------------:|:-----------------------:| |
|
|
| 0.5403 | 0.2442 | 100 | 0.5274 | 0.7611 | 0.7596 | 0.7728 | 0.7611 | 0.8535 | 0.6746 | |
|
|
| 0.2673 | 0.4884 | 200 | 0.2806 | 0.8980 | 0.8980 | 0.8994 | 0.8980 | 0.9230 | 0.8746 | |
|
|
| 0.247 | 0.7326 | 300 | 0.2610 | 0.9029 | 0.9024 | 0.9074 | 0.9029 | 0.8434 | 0.9586 | |
|
|
| 0.2357 | 0.9768 | 400 | 0.2560 | 0.9084 | 0.9084 | 0.9096 | 0.9084 | 0.9318 | 0.8864 | |
|
|
| 0.2094 | 1.2198 | 500 | 0.3127 | 0.9090 | 0.9089 | 0.9123 | 0.9090 | 0.9508 | 0.8698 | |
|
|
| 0.1695 | 1.4640 | 600 | 0.2298 | 0.9188 | 0.9187 | 0.9189 | 0.9188 | 0.9053 | 0.9314 | |
|
|
| 0.2024 | 1.7082 | 700 | 0.2218 | 0.9206 | 0.9206 | 0.9214 | 0.9206 | 0.9394 | 0.9030 | |
|
|
| 0.1155 | 1.9524 | 800 | 0.2061 | 0.9236 | 0.9236 | 0.9236 | 0.9236 | 0.9192 | 0.9278 | |
|
|
| 0.1361 | 2.1954 | 900 | 0.2299 | 0.9218 | 0.9218 | 0.9226 | 0.9218 | 0.9407 | 0.9041 | |
|
|
| 0.1235 | 2.4396 | 1000 | 0.2668 | 0.9212 | 0.9212 | 0.9246 | 0.9212 | 0.9634 | 0.8817 | |
|
|
| 0.084 | 2.6838 | 1100 | 0.2733 | 0.9218 | 0.9218 | 0.9240 | 0.9218 | 0.9545 | 0.8911 | |
|
|
| 0.1326 | 2.9280 | 1200 | 0.2395 | 0.9249 | 0.9249 | 0.9249 | 0.9249 | 0.9192 | 0.9302 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.49.0 |
|
|
- Pytorch 2.5.1+cu124 |
|
|
- Datasets 3.3.2 |
|
|
- Tokenizers 0.21.0 |
|
|
|
