|
--- |
|
license: mit |
|
library_name: peft |
|
tags: |
|
- generated_from_trainer |
|
base_model: facebook/esm2_t12_35M_UR50D |
|
metrics: |
|
- precision |
|
- recall |
|
- accuracy |
|
model-index: |
|
- name: esm2-t12-35M-lora-64-remote-homology-filtered |
|
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. --> |
|
|
|
# esm2-t12-35M-lora-64-remote-homology-filtered |
|
|
|
This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5657 |
|
- Precision: 0.7166 |
|
- Recall: 0.6986 |
|
- F1-score: 0.7075 |
|
- Accuracy: 0.7141 |
|
|
|
## 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: 3e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 5 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:| |
|
| 0.6191 | 1.0 | 7969 | 0.6185 | 0.6919 | 0.5824 | 0.6325 | 0.6650 | |
|
| 0.5921 | 2.0 | 15938 | 0.5838 | 0.7201 | 0.6339 | 0.6742 | 0.6968 | |
|
| 0.5874 | 3.0 | 23907 | 0.5751 | 0.7439 | 0.6104 | 0.6705 | 0.7032 | |
|
| 0.5593 | 4.0 | 31876 | 0.5664 | 0.7210 | 0.6833 | 0.7016 | 0.7124 | |
|
| 0.576 | 5.0 | 39845 | 0.5657 | 0.7166 | 0.6986 | 0.7075 | 0.7141 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.11.1 |
|
- Transformers 4.39.3 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
