|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: finetuned_minilm |
|
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. --> |
|
|
|
# finetuned_minilm |
|
|
|
This model is a fine-tuned version of [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6736 |
|
- Accuracy: 0.9023 |
|
|
|
## 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: 5e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 12345 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: constant |
|
- num_epochs: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
|
| 0.5371 | 1.0 | 619 | 0.2941 | 0.8782 | |
|
| 0.2763 | 2.0 | 1238 | 0.2590 | 0.8986 | |
|
| 0.1899 | 3.0 | 1857 | 0.3081 | 0.8959 | |
|
| 0.1257 | 4.0 | 2476 | 0.2576 | 0.9177 | |
|
| 0.0929 | 5.0 | 3095 | 0.3949 | 0.9059 | |
|
| 0.0806 | 6.0 | 3714 | 0.3304 | 0.9173 | |
|
| 0.0629 | 7.0 | 4333 | 0.4214 | 0.9073 | |
|
| 0.0474 | 8.0 | 4952 | 0.4625 | 0.9145 | |
|
| 0.0498 | 9.0 | 5571 | 0.4227 | 0.9236 | |
|
| 0.049 | 10.0 | 6190 | 0.5549 | 0.8945 | |
|
| 0.0411 | 11.0 | 6809 | 0.3340 | 0.9341 | |
|
| 0.0272 | 12.0 | 7428 | 0.3317 | 0.9291 | |
|
| 0.0264 | 13.0 | 8047 | 0.4099 | 0.9305 | |
|
| 0.0279 | 14.0 | 8666 | 0.4092 | 0.9268 | |
|
| 0.0242 | 15.0 | 9285 | 0.4418 | 0.9318 | |
|
| 0.0241 | 16.0 | 9904 | 0.4352 | 0.9273 | |
|
| 0.0238 | 17.0 | 10523 | 0.5306 | 0.9259 | |
|
| 0.0216 | 18.0 | 11142 | 0.4267 | 0.9241 | |
|
| 0.0166 | 19.0 | 11761 | 0.5134 | 0.9255 | |
|
| 0.0182 | 20.0 | 12380 | 0.6736 | 0.9023 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.1 |
|
- Pytorch 1.13.1+cu116 |
|
- Datasets 2.10.1 |
|
- Tokenizers 0.13.2 |
|
|
