|
--- |
|
license: mit |
|
base_model: roberta-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- precision |
|
- recall |
|
model-index: |
|
- name: roberta-base-outputs |
|
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. --> |
|
|
|
# roberta-base-outputs |
|
|
|
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5836 |
|
- Accuracy: 0.6636 |
|
- F1: 0.6948 |
|
- Precision: 0.6409 |
|
- Recall: 0.7587 |
|
|
|
## 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: 1e-06 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 2 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
|
| 0.6984 | 0.1778 | 1000 | 0.6931 | 0.5072 | 0.4296 | 0.5167 | 0.3677 | |
|
| 0.6952 | 0.3556 | 2000 | 0.6932 | 0.4956 | 0.0032 | 0.6667 | 0.0016 | |
|
| 0.6931 | 0.5333 | 3000 | 0.6922 | 0.5314 | 0.3417 | 0.5874 | 0.2409 | |
|
| 0.6927 | 0.7111 | 4000 | 0.6901 | 0.5272 | 0.6625 | 0.5179 | 0.9192 | |
|
| 0.6883 | 0.8889 | 5000 | 0.6792 | 0.5714 | 0.6346 | 0.5570 | 0.7373 | |
|
| 0.6756 | 1.0667 | 6000 | 0.6521 | 0.6114 | 0.5702 | 0.6455 | 0.5107 | |
|
| 0.6476 | 1.2444 | 7000 | 0.6317 | 0.627 | 0.6909 | 0.5939 | 0.8257 | |
|
| 0.6278 | 1.4222 | 8000 | 0.6058 | 0.6474 | 0.6799 | 0.6276 | 0.7417 | |
|
| 0.6134 | 1.6 | 9000 | 0.5959 | 0.6564 | 0.6909 | 0.6328 | 0.7607 | |
|
| 0.6119 | 1.7778 | 10000 | 0.5870 | 0.6618 | 0.6933 | 0.6393 | 0.7571 | |
|
| 0.6033 | 1.9556 | 11000 | 0.5836 | 0.6636 | 0.6948 | 0.6409 | 0.7587 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.1 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.19.2 |
|
- Tokenizers 0.19.1 |
|
|
