| --- |
| license: mit |
| library_name: peft |
| tags: |
| - generated_from_trainer |
| base_model: microsoft/phi-2 |
| model-index: |
| - name: hate-phi |
| 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. --> |
|
|
| # hate-phi |
|
|
| This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3268 |
| - Classification Report: precision recall f1-score support |
|
|
| 0 0.57 0.08 0.14 438 |
| 1 0.91 0.97 0.93 5755 |
| 2 0.80 0.79 0.80 1242 |
| |
| accuracy 0.89 7435 |
| macro avg 0.76 0.61 0.62 7435 |
| weighted avg 0.87 0.89 0.87 7435 |
| |
|
|
| ## 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: 0.0002 |
| - train_batch_size: 64 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 256 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 1 |
| - num_epochs: 1 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Classification Report | |
| |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
| | 0.8106 | 0.37 | 25 | 0.4551 | precision recall f1-score support |
|
|
| 0 0.18 0.03 0.04 438 |
| 1 0.85 0.97 0.91 5755 |
| 2 0.75 0.46 0.57 1242 |
| |
| accuracy 0.83 7435 |
| macro avg 0.59 0.49 0.51 7435 |
| weighted avg 0.79 0.83 0.80 7435 |
| | |
| | 0.3677 | 0.74 | 50 | 0.3374 | precision recall f1-score support |
| |
| 0 0.51 0.09 0.16 438 |
| 1 0.91 0.95 0.93 5755 |
| 2 0.77 0.83 0.80 1242 |
| |
| accuracy 0.88 7435 |
| macro avg 0.73 0.63 0.63 7435 |
| weighted avg 0.87 0.88 0.87 7435 |
| | |
| |
|
|
| ### Framework versions |
|
|
| - PEFT 0.11.1 |
| - Transformers 4.39.3 |
| - Pytorch 2.1.2 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
