|
|
--- |
|
|
license: mit |
|
|
base_model: Supabase/gte-small |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
model-index: |
|
|
- name: v_best_model |
|
|
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. --> |
|
|
|
|
|
# v_best_model |
|
|
|
|
|
This model is a fine-tuned version of [Supabase/gte-small](https://huggingface.co/Supabase/gte-small) on an unknown dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.2700 |
|
|
- Accuracy: 0.9437 |
|
|
|
|
|
## 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: 0.0001 |
|
|
- train_batch_size: 64 |
|
|
- eval_batch_size: 64 |
|
|
- seed: 42 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 10 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
|
| 1.639 | 1.0 | 21 | 1.3351 | 0.7606 | |
|
|
| 1.065 | 2.0 | 42 | 0.7793 | 0.8592 | |
|
|
| 0.6055 | 3.0 | 63 | 0.5200 | 0.8873 | |
|
|
| 0.3519 | 4.0 | 84 | 0.3832 | 0.9014 | |
|
|
| 0.2186 | 5.0 | 105 | 0.3277 | 0.9155 | |
|
|
| 0.1573 | 6.0 | 126 | 0.2844 | 0.9296 | |
|
|
| 0.118 | 7.0 | 147 | 0.3185 | 0.9014 | |
|
|
| 0.0948 | 8.0 | 168 | 0.2744 | 0.9437 | |
|
|
| 0.0831 | 9.0 | 189 | 0.2746 | 0.9437 | |
|
|
| 0.0778 | 10.0 | 210 | 0.2700 | 0.9437 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.36.2 |
|
|
- Pytorch 2.0.1+cu117 |
|
|
- Datasets 2.16.1 |
|
|
- Tokenizers 0.15.0 |
|
|
|
