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---
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
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