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--- |
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license: mit |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: microsoft/phi-2 |
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model-index: |
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- name: hate-phi |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# hate-phi |
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This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3268 |
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- Classification Report: precision recall f1-score support |
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0 0.57 0.08 0.14 438 |
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1 0.91 0.97 0.93 5755 |
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2 0.80 0.79 0.80 1242 |
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accuracy 0.89 7435 |
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macro avg 0.76 0.61 0.62 7435 |
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weighted avg 0.87 0.89 0.87 7435 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 64 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Classification Report | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 0.8106 | 0.37 | 25 | 0.4551 | precision recall f1-score support |
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0 0.18 0.03 0.04 438 |
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1 0.85 0.97 0.91 5755 |
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2 0.75 0.46 0.57 1242 |
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accuracy 0.83 7435 |
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macro avg 0.59 0.49 0.51 7435 |
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weighted avg 0.79 0.83 0.80 7435 |
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| 0.3677 | 0.74 | 50 | 0.3374 | precision recall f1-score support |
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0 0.51 0.09 0.16 438 |
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1 0.91 0.95 0.93 5755 |
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2 0.77 0.83 0.80 1242 |
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accuracy 0.88 7435 |
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macro avg 0.73 0.63 0.63 7435 |
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weighted avg 0.87 0.88 0.87 7435 |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
