|
--- |
|
license: mit |
|
base_model: microsoft/MiniLM-L12-H384-uncased |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: minilm-finetuned-emotion-class-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. --> |
|
|
|
# minilm-finetuned-emotion-class-model |
|
|
|
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co./microsoft/MiniLM-L12-H384-uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.1026 |
|
- F1 Score: 0.6649 |
|
|
|
## 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: 2e-05 |
|
- train_batch_size: 128 |
|
- eval_batch_size: 128 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 20 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 Score | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 1.8502 | 1.0 | 270 | 1.4798 | 0.5071 | |
|
| 1.3541 | 2.0 | 540 | 1.2377 | 0.5836 | |
|
| 1.1809 | 3.0 | 810 | 1.1675 | 0.6202 | |
|
| 1.0891 | 4.0 | 1080 | 1.1081 | 0.6522 | |
|
| 1.0205 | 5.0 | 1350 | 1.0815 | 0.6603 | |
|
| 0.9624 | 6.0 | 1620 | 1.0640 | 0.6645 | |
|
| 0.9185 | 7.0 | 1890 | 1.0572 | 0.6689 | |
|
| 0.8811 | 8.0 | 2160 | 1.0433 | 0.6693 | |
|
| 0.8531 | 9.0 | 2430 | 1.0479 | 0.6746 | |
|
| 0.8208 | 10.0 | 2700 | 1.0536 | 0.6697 | |
|
| 0.8014 | 11.0 | 2970 | 1.0564 | 0.6713 | |
|
| 0.7798 | 12.0 | 3240 | 1.0634 | 0.6716 | |
|
| 0.7568 | 13.0 | 3510 | 1.0744 | 0.6698 | |
|
| 0.7414 | 14.0 | 3780 | 1.0782 | 0.6704 | |
|
| 0.7265 | 15.0 | 4050 | 1.0810 | 0.6694 | |
|
| 0.7128 | 16.0 | 4320 | 1.0885 | 0.6684 | |
|
| 0.7054 | 17.0 | 4590 | 1.0917 | 0.6631 | |
|
| 0.6927 | 18.0 | 4860 | 1.0961 | 0.6678 | |
|
| 0.6848 | 19.0 | 5130 | 1.1005 | 0.6644 | |
|
| 0.6742 | 20.0 | 5400 | 1.1026 | 0.6649 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
