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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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- trl |
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- sft |
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- generated_from_trainer |
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base_model: microsoft/phi-1_5 |
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model-index: |
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- name: phi-1_5-finetuned-news-events |
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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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# phi-1_5-finetuned-news-events |
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This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9322 |
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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: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- training_steps: 800 |
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### Training results |
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### How to use |
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``` |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("Abinaya/phi-1_5-finetuned-news-events", trust_remote_code=True, torch_dtype=torch.float32) |
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tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True) |
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``` |
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And test |
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``` |
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inputs = tokenizer([f"extract events from news.\n News: {test_data[0]['text']}"], return_tensors="pt", return_attention_mask=False) |
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outputs = model.generate(**inputs, max_length=512) |
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text = tokenizer.batch_decode(outputs)[0] |
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print(text) |
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``` |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
