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--- |
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library_name: peft |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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base_model: NousResearch/Llama-2-7b-hf |
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model-index: |
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- name: billm-llama-7b-conll03-ner |
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results: [] |
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license: mit |
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datasets: |
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- conll2003 |
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language: |
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- en |
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pipeline_tag: token-classification |
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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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# billm-llama-7b-conll03-ner |
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<a href="https://arxiv.org/abs/2310.01208"> |
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<img src="https://img.shields.io/badge/Arxiv-2310.01208-blue.svg?style=flat-square" alt="https://arxiv.org/abs/2310.01208" /> |
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</a> |
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<a href="https://arxiv.org/abs/2311.05296"> |
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<img src="https://img.shields.io/badge/Arxiv-2311.05296-yellow.svg?style=flat-square" alt="https://arxiv.org/abs/2311.05296" /> |
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</a> |
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This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) using [BiLLM](https://github.com/WhereIsAI/BiLLM). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1664 |
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- Precision: 0.9243 |
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- Recall: 0.9395 |
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- F1: 0.9319 |
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- Accuracy: 0.9860 |
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## Inference |
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```bash |
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python -m pip install -U billm==0.1.1 |
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``` |
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```python |
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from transformers import AutoTokenizer, pipeline |
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from peft import PeftModel, PeftConfig |
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from billm import MistralForTokenClassification |
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label2id = {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8} |
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id2label = {v: k for k, v in label2id.items()} |
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model_id = 'WhereIsAI/billm-llama-7b-conll03-ner' |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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peft_config = PeftConfig.from_pretrained(model_id) |
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model = MistralForTokenClassification.from_pretrained( |
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peft_config.base_model_name_or_path, |
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num_labels=len(label2id), id2label=id2label, label2id=label2id |
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) |
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model = PeftModel.from_pretrained(model, model_id) |
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# merge_and_unload is necessary for inference |
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model = model.merge_and_unload() |
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token_classifier = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple") |
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sentence = "I live in Hong Kong. I am a student at Hong Kong PolyU." |
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tokens = token_classifier(sentence) |
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print(tokens) |
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``` |
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## Training Details |
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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: 8 |
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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: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.048 | 1.0 | 1756 | 0.0971 | 0.8935 | 0.9082 | 0.9008 | 0.9813 | |
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| 0.0217 | 2.0 | 3512 | 0.0963 | 0.9182 | 0.9301 | 0.9241 | 0.9852 | |
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| 0.0113 | 3.0 | 5268 | 0.1081 | 0.9265 | 0.9348 | 0.9306 | 0.9858 | |
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| 0.0038 | 4.0 | 7024 | 0.1477 | 0.9216 | 0.9379 | 0.9297 | 0.9858 | |
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| 0.0016 | 5.0 | 8780 | 0.1617 | 0.9199 | 0.9370 | 0.9284 | 0.9855 | |
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| 0.0007 | 6.0 | 10536 | 0.1618 | 0.9235 | 0.9390 | 0.9312 | 0.9859 | |
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| 0.0005 | 7.0 | 12292 | 0.1644 | 0.9245 | 0.9395 | 0.9319 | 0.9860 | |
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| 0.0004 | 8.0 | 14048 | 0.1662 | 0.9248 | 0.9393 | 0.9320 | 0.9861 | |
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| 0.0003 | 9.0 | 15804 | 0.1664 | 0.9248 | 0.9395 | 0.9321 | 0.9861 | |
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| 0.0003 | 10.0 | 17560 | 0.1664 | 0.9243 | 0.9395 | 0.9319 | 0.9860 | |
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### Framework versions |
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- PEFT 0.9.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
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## Citation |
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```bibtex |
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@inproceedings{li2024bellm, |
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title = "BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings", |
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author = "Li, Xianming and Li, Jing", |
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booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics", |
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year = "2024", |
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publisher = "Association for Computational Linguistics" |
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} |
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@article{li2023label, |
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title={Label supervised llama finetuning}, |
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author={Li, Zongxi and Li, Xianming and Liu, Yuzhang and Xie, Haoran and Li, Jing and Wang, Fu-lee and Li, Qing and Zhong, Xiaoqin}, |
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journal={arXiv preprint arXiv:2310.01208}, |
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year={2023} |
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} |
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``` |
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