|
|
--- |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
- f1 |
|
|
- precision |
|
|
- recall |
|
|
model-index: |
|
|
- name: prot_bert_classification_finetuned_no_finetune |
|
|
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. --> |
|
|
|
|
|
# prot_bert_classification_finetuned_no_finetune |
|
|
|
|
|
This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) on an unknown dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.6212 |
|
|
- Accuracy: 0.6473 |
|
|
- F1: 0.6623 |
|
|
- Precision: 0.6201 |
|
|
- Recall: 0.7107 |
|
|
|
|
|
## 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: 1 |
|
|
- eval_batch_size: 1 |
|
|
- seed: 3 |
|
|
- gradient_accumulation_steps: 4 |
|
|
- total_train_batch_size: 4 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- lr_scheduler_warmup_steps: 5 |
|
|
- num_epochs: 6 |
|
|
- mixed_precision_training: Native AMP |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
|
|
| 0.6494 | 1.0 | 3332 | 0.6479 | 0.6439 | 0.6679 | 0.6116 | 0.7357 | |
|
|
| 0.5357 | 2.0 | 6664 | 0.6440 | 0.6148 | 0.6459 | 0.5845 | 0.7218 | |
|
|
| 0.4661 | 3.0 | 9996 | 0.6265 | 0.6283 | 0.6414 | 0.6047 | 0.6829 | |
|
|
| 0.506 | 4.0 | 13328 | 0.6192 | 0.6439 | 0.6567 | 0.6187 | 0.6996 | |
|
|
| 0.4204 | 5.0 | 16660 | 0.6122 | 0.6567 | 0.6752 | 0.6259 | 0.7330 | |
|
|
| 0.6071 | 6.0 | 19992 | 0.6212 | 0.6473 | 0.6623 | 0.6201 | 0.7107 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.18.0 |
|
|
- Pytorch 1.11.0+cu113 |
|
|
- Datasets 2.1.0 |
|
|
- Tokenizers 0.12.1 |
|
|
|
