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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-3-mini-4k-instruct |
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datasets: |
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- generator |
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model-index: |
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- name: checkpoint_update |
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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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# phi3nedtuned-ner-json |
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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 the dataset: https://huggingface.co/datasets/shujatoor/ner_instruct-json. |
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## For Inference |
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```python |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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config = PeftConfig.from_pretrained("shujatoor/phi3nedtuned-ner-json") |
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model = AutoModelForCausalLM.from_pretrained( |
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"microsoft/Phi-3-mini-4k-instruct", |
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device_map="cuda", |
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torch_dtype="auto", |
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trust_remote_code=True, |
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) |
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model = PeftModel.from_pretrained(model, "shujatoor/phi3nedtuned-ner-json") |
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model.config.to_json_file('adapter_config.json') |
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torch.random.manual_seed(0) |
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tokenizer = AutoTokenizer.from_pretrained("shujatoor/phi3nedtuned-ner-json") |
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text = "Tehzeeb Bakers STRN3277876134234 Block A. Police Foundation,PwD Islamabad 051-5170713-4.051-5170501 STRN#3277876134234 NTN#7261076-2 Sales Receipt 05/04/202405:56:40PM CashierM J Payment:Cash Rate Qty. Total # Descriptlon 80.512.000 190.00 1.VEGETABLESAMOSA Sub Total 161.02 Total Tax: 28.98 POS Service Fee 1.00 Total 191.00 Cash 200.00 Change Due 9.00 SR#th007-220240405175640730 Goods Once Sold Can Not Be Taken Back or Replaced All Prices Are Inclusive Sales Tax 134084240405175640553" |
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q_json = "extracted_data': {'store_name': '', 'address': '', 'receipt_number': '', 'drug_license_number': '', 'gst_number': '', 'vat_number': '', 'date': '', 'time': '', 'items': [], 'total_items': '', 'gst_tax': '', 'vat_tax': '', 'gross_total': '', 'discount': '', 'net_total': '', 'contact': ''}" |
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qs = f'{text}. {q_json}' |
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print('Question:',qs, '\n') |
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messages = [ |
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#{"role": "system", "content": ""}, |
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{"role": "user", "content": qs}, |
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] |
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pipe = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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) |
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generation_args = { |
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"max_new_tokens": 512, |
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"return_full_text": False, |
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#"temperature": 0.0, |
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"do_sample": False, |
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} |
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output = pipe(messages, **generation_args) |
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print('Answer:', output[0]['generated_text'], '\n') |
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``` |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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Named Entity Recognition (NER) |
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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: 1 |
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- eval_batch_size: 1 |
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- seed: 0 |
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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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- lr_scheduler_warmup_ratio: 0.2 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.1904 | 0.5618 | 500 | 1.0617 | |
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| 0.765 | 1.1236 | 1000 | 0.9442 | |
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| 0.782 | 1.6854 | 1500 | 0.8690 | |
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| 0.5591 | 2.2472 | 2000 | 0.8647 | |
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| 0.5669 | 2.8090 | 2500 | 0.8296 | |
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| 0.4205 | 3.3708 | 3000 | 0.8820 | |
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| 0.3812 | 3.9326 | 3500 | 0.8859 | |
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| 0.3323 | 4.4944 | 4000 | 0.9360 | |
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### Framework versions |
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- PEFT 0.10.1.dev0 |
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- Transformers 4.41.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
