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--- |
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license: mit |
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base_model: microsoft/Phi-3-mini-4k-instruct |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: Phi-3-mini-4k-instruct-mbti |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zmhzmh/huggingface/runs/h3uz5tey) |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zmhzmh/huggingface/runs/h3uz5tey) |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zmhzmh/huggingface/runs/h3uz5tey) |
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# Phi-3-mini-4k-instruct-mbti |
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This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6615 |
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- Accuracy: 0.6220 |
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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: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| 0.7155 | 0.1977 | 500 | 0.7196 | 0.5898 | |
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| 0.6873 | 0.3955 | 1000 | 0.6776 | 0.5931 | |
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| 0.6841 | 0.5932 | 1500 | 0.6620 | 0.6058 | |
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| 0.6746 | 0.7909 | 2000 | 0.6615 | 0.6220 | |
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| 0.6655 | 0.9886 | 2500 | 0.6647 | 0.6133 | |
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| 0.6092 | 1.1864 | 3000 | 0.6873 | 0.5716 | |
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| 0.5661 | 1.3841 | 3500 | 0.7262 | 0.6092 | |
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| 0.5565 | 1.5818 | 4000 | 0.6938 | 0.6185 | |
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| 0.5308 | 1.7795 | 4500 | 0.7100 | 0.6060 | |
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| 0.5236 | 1.9773 | 5000 | 0.7046 | 0.6127 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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