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---
library_name: peft
metrics:
- precision
- recall
- f1
- accuracy
base_model: NousResearch/Llama-2-7b-hf
model-index:
- name: billm-llama-7b-conll03-ner
results: []
license: mit
datasets:
- conll2003
language:
- en
pipeline_tag: token-classification
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# billm-llama-7b-conll03-ner
<a href="https://arxiv.org/abs/2310.01208">
<img src="https://img.shields.io/badge/Arxiv-2310.01208-blue.svg?style=flat-square" alt="https://arxiv.org/abs/2310.01208" />
</a>
<a href="https://arxiv.org/abs/2311.05296">
<img src="https://img.shields.io/badge/Arxiv-2311.05296-yellow.svg?style=flat-square" alt="https://arxiv.org/abs/2311.05296" />
</a>
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).
It achieves the following results on the evaluation set:
- Loss: 0.1664
- Precision: 0.9243
- Recall: 0.9395
- F1: 0.9319
- Accuracy: 0.9860
## Inference
```bash
python -m pip install -U billm==0.1.1
```
```python
from transformers import AutoTokenizer, pipeline
from peft import PeftModel, PeftConfig
from billm import MistralForTokenClassification
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}
id2label = {v: k for k, v in label2id.items()}
model_id = 'WhereIsAI/billm-llama-7b-conll03-ner'
tokenizer = AutoTokenizer.from_pretrained(model_id)
peft_config = PeftConfig.from_pretrained(model_id)
model = MistralForTokenClassification.from_pretrained(
peft_config.base_model_name_or_path,
num_labels=len(label2id), id2label=id2label, label2id=label2id
)
model = PeftModel.from_pretrained(model, model_id)
# merge_and_unload is necessary for inference
model = model.merge_and_unload()
token_classifier = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
sentence = "I live in Hong Kong. I am a student at Hong Kong PolyU."
tokens = token_classifier(sentence)
print(tokens)
```
## Training Details
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.048 | 1.0 | 1756 | 0.0971 | 0.8935 | 0.9082 | 0.9008 | 0.9813 |
| 0.0217 | 2.0 | 3512 | 0.0963 | 0.9182 | 0.9301 | 0.9241 | 0.9852 |
| 0.0113 | 3.0 | 5268 | 0.1081 | 0.9265 | 0.9348 | 0.9306 | 0.9858 |
| 0.0038 | 4.0 | 7024 | 0.1477 | 0.9216 | 0.9379 | 0.9297 | 0.9858 |
| 0.0016 | 5.0 | 8780 | 0.1617 | 0.9199 | 0.9370 | 0.9284 | 0.9855 |
| 0.0007 | 6.0 | 10536 | 0.1618 | 0.9235 | 0.9390 | 0.9312 | 0.9859 |
| 0.0005 | 7.0 | 12292 | 0.1644 | 0.9245 | 0.9395 | 0.9319 | 0.9860 |
| 0.0004 | 8.0 | 14048 | 0.1662 | 0.9248 | 0.9393 | 0.9320 | 0.9861 |
| 0.0003 | 9.0 | 15804 | 0.1664 | 0.9248 | 0.9395 | 0.9321 | 0.9861 |
| 0.0003 | 10.0 | 17560 | 0.1664 | 0.9243 | 0.9395 | 0.9319 | 0.9860 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.0.1
- Datasets 2.16.0
- Tokenizers 0.15.0
## Citation
```bibtex
@inproceedings{li2024bellm,
title = "BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings",
author = "Li, Xianming and Li, Jing",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics",
year = "2024",
publisher = "Association for Computational Linguistics"
}
@article{li2023label,
title={Label supervised llama finetuning},
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},
journal={arXiv preprint arXiv:2310.01208},
year={2023}
}
```
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