|
--- |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: finbert-finetuned-FG-SINGLE_SENTENCE-NEWS |
|
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. --> |
|
|
|
# finbert-finetuned-FG-SINGLE_SENTENCE-NEWS |
|
|
|
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 3.2997 |
|
- Accuracy: 0.6414 |
|
- F1: 0.6295 |
|
|
|
## 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: 6e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
|
| No log | 1.0 | 321 | 0.9371 | 0.5699 | 0.4333 | |
|
| 0.9282 | 2.0 | 642 | 0.9135 | 0.5930 | 0.5447 | |
|
| 0.9282 | 3.0 | 963 | 0.9900 | 0.6033 | 0.5823 | |
|
| 0.6743 | 4.0 | 1284 | 1.0802 | 0.6142 | 0.6065 | |
|
| 0.3134 | 5.0 | 1605 | 1.5156 | 0.6183 | 0.5971 | |
|
| 0.3134 | 6.0 | 1926 | 1.3695 | 0.6319 | 0.6183 | |
|
| 0.1709 | 7.0 | 2247 | 1.8746 | 0.6462 | 0.6267 | |
|
| 0.1112 | 8.0 | 2568 | 2.0880 | 0.6176 | 0.6155 | |
|
| 0.1112 | 9.0 | 2889 | 2.3953 | 0.6190 | 0.6087 | |
|
| 0.0811 | 10.0 | 3210 | 2.3792 | 0.6339 | 0.6225 | |
|
| 0.0608 | 11.0 | 3531 | 2.3783 | 0.6360 | 0.6282 | |
|
| 0.0608 | 12.0 | 3852 | 2.5982 | 0.6544 | 0.6351 | |
|
| 0.039 | 13.0 | 4173 | 2.7687 | 0.6346 | 0.6305 | |
|
| 0.039 | 14.0 | 4494 | 2.8980 | 0.6414 | 0.6299 | |
|
| 0.0206 | 15.0 | 4815 | 3.0858 | 0.6319 | 0.6253 | |
|
| 0.0168 | 16.0 | 5136 | 3.2408 | 0.6244 | 0.6170 | |
|
| 0.0168 | 17.0 | 5457 | 3.1809 | 0.6435 | 0.6293 | |
|
| 0.0123 | 18.0 | 5778 | 3.2629 | 0.6449 | 0.6324 | |
|
| 0.0055 | 19.0 | 6099 | 3.2866 | 0.6449 | 0.6308 | |
|
| 0.0055 | 20.0 | 6420 | 3.2997 | 0.6414 | 0.6295 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.16.2 |
|
- Pytorch 1.9.1 |
|
- Datasets 1.18.4 |
|
- Tokenizers 0.11.6 |
|
|
