|
--- |
|
license: cc-by-nc-sa-4.0 |
|
base_model: InstaDeepAI/nucleotide-transformer-v2-500m-multi-species |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
- matthews_correlation |
|
- accuracy |
|
model-index: |
|
- name: gut_6000-finetuned-lora-NT-v2-500m-multi-species |
|
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. --> |
|
|
|
# gut_6000-finetuned-lora-NT-v2-500m-multi-species |
|
|
|
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-500m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4765 |
|
- F1: 0.8404 |
|
- Matthews Correlation: 0.5698 |
|
- Accuracy: 0.7956 |
|
- F1 Score: 0.8404 |
|
|
|
## 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: 0.0005 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 64 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- training_steps: 1000 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 | Matthews Correlation | Accuracy | F1 Score | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------:|:--------:|:--------:| |
|
| 0.7596 | 0.02 | 100 | 0.7104 | 0.0744 | -0.1826 | 0.3695 | 0.0744 | |
|
| 0.6692 | 0.04 | 200 | 0.6143 | 0.7828 | 0.3311 | 0.6803 | 0.7828 | |
|
| 0.6022 | 0.05 | 300 | 0.5708 | 0.8180 | 0.4877 | 0.7563 | 0.8180 | |
|
| 0.5577 | 0.07 | 400 | 0.5906 | 0.8080 | 0.5037 | 0.7639 | 0.8080 | |
|
| 0.5743 | 0.09 | 500 | 0.5789 | 0.7710 | 0.2695 | 0.6470 | 0.7710 | |
|
| 0.5052 | 0.11 | 600 | 0.5010 | 0.8273 | 0.5450 | 0.7842 | 0.8273 | |
|
| 0.5012 | 0.12 | 700 | 0.4926 | 0.8409 | 0.5575 | 0.7842 | 0.8409 | |
|
| 0.4757 | 0.14 | 800 | 0.4827 | 0.8368 | 0.5588 | 0.7905 | 0.8368 | |
|
| 0.5166 | 0.16 | 900 | 0.4715 | 0.8470 | 0.5778 | 0.7948 | 0.8470 | |
|
| 0.4667 | 0.18 | 1000 | 0.4765 | 0.8404 | 0.5698 | 0.7956 | 0.8404 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.2 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.17.1 |
|
- Tokenizers 0.15.2 |
|
|
