Update README.md

README.md

@@ -1,3 +1,78 @@
 ---
 license: apache-2.0
+datasets:
+- Universal-NER/Pile-NER-type
+language:
+- en
+metrics:
+- f1
+library_name: transformers
+pipeline_tag: text-generation
 ---
+
+<p align="center"><h2 align="center">Rethinking Negative Instances for Generative Named Entity Recognition</h2></p>
+
+# Model Card for GNER-LLaMA-7B
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+We introduce GNER, a **G**enerative **N**amed **E**ntity **R**ecognition framework, which demonstrates enhanced zero-shot capabilities across unseen entity domains. Experiments on two representative generative models, i.e., LLaMA and Flan-T5, show that the integration of negative instances into the training process yields substantial performance enhancements. The resulting models, GNER-LLaMA and GNER-T5, outperform state-of-the-art (SoTA) approaches by a large margin, achieving improvements of 8 and 11 points in $F_1$ score, respectively. Code and models are publicly available.
+
+* 💻 Code: [https://github.com/yyDing1/GNER/](https://github.com/yyDing1/GNER/)
+* 📖 Paper: [Rethinking Negative Instances for Generative Named Entity Recognition](https://arxiv.org/abs/2402.16602)
+* 💾 Models in the 🤗 HuggingFace Hub: [GNER-Models](https://huggingface.co/collections/dyyyyyyyy/gner-65dda2cb96c6e35c814dea56)
+* 🔁 Reproduction Materials: [Reproduction Materials](https://drive.google.com/drive/folders/1m2FqDgItEbSoeUVo-i18AwMvBcNkZD46?usp=drive_link)
+
+<p align="center">
+<img src="https://github.com/yyDing1/GNER/blob/main/assets/zero_shot_results.png">
+</p>
+
+## PreTrained Models
+
+We release five GNER models based on LLaMA (7B) and Flan-T5 (base, large, xl and xxl).
+
+| Model         | # Params | Zero-shot Average $F_1$ | Supervised Average $F_1$ |          🤗 HuggingFace<br />Download Link          |
+| ------------- | -------: | :----------------------: | :-----------------------: | :-------------------------------------------------: |
+| GNER-LLaMA    |       7B |           66.1           |           86.09           | [link](https://huggingface.co/dyyyyyyyy/GNER-LLaMA-7B) |
+| GNER-T5-base  |     248M |           59.5           |           83.21           | [link](https://huggingface.co/dyyyyyyyy/GNER-T5-base) |
+| GNER-T5-large |     783M |           63.5           |           85.45           | [link](https://huggingface.co/dyyyyyyyy/GNER-T5-large) |
+| GNER-T5-xl    |       3B |           66.1           |           85.94           |  [link](https://huggingface.co/dyyyyyyyy/GNER-T5-xl)  |
+| GNER-T5-xxl   |      11B |           69.1           |           86.15           |  [link](https://huggingface.co/dyyyyyyyy/GNER-T5-xxl)  |
+
+## Demo usage
+
+You should install the dependencies:
+```bash
+pip install torch>=2.1.0 datasets>=2.17.0 deepspeed>=0.13.4 accelerate>=0.27.2 transformers>=4.38.1 protobuf>=4.25.3 
+```
+
+Below is an using `GNER-LLaMA`
+```python
+>>> from transformers import AutoTokenizer, AutoModelForCasualLM
+>>> tokenizer = AutoTokenizer.from_pretrained("dyyyyyyyy/GNER-LLaMA-7B")
+>>> model = AutoModelForSeq2SeqLM.from_pretrained("dyyyyyyyy/GNER-LLaMA-7B")
+>>> model = model.eval()
+>>> instruction_template = "Please analyze the sentence provided, identifying the type of entity for each word on a token-by-token basis.\nOutput format is: word_1(label_1), word_2(label_2), ...\nWe'll use the BIO-format to label the entities, where:\n1. B- (Begin) indicates the start of a named entity.\n2. I- (Inside) is used for words within a named entity but are not the first word.\n3. O (Outside) denotes words that are not part of a named entity.\n"
+>>> entity_labels = ["genre", "rating", "review", "plot", "song", "average ratings", "director", "character", "trailer", "year", "actor", "title"]
+>>> instruction = f"{instruction_template}\nUse the specific entity tags: {', '.join(label_list)} and O.\n"
+>>> instruction = f"[INST] {instruction} [/INST]"
+>>> inputs = tokenizer(instruction, return_tensors="pt").to("cuda")
+>>> outputs = model.generate(**inputs, max_new_tokens=640)
+>>> response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+>>> response = response[preds.find("[/INST]") + len("[/INST]"):].strip()
+>>> print(response)
+"did(O) george(B-actor) clooney(I-actor) make(O) a(O) musical(B-genre) in(O) the(O) 1980s(B-year)"
+
+
+## Citation
+
+```bibtex
+@misc{ding2024rethinking,
+      title={Rethinking Negative Instances for Generative Named Entity Recognition}, 
+      author={Yuyang Ding and Juntao Li and Pinzheng Wang and Zecheng Tang and Bowen Yan and Min Zhang},
+      year={2024},
+      eprint={2402.16602},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```