| Quantization made by Richard Erkhov. | |
| [Github](https://github.com/RichardErkhov) | |
| [Discord](https://discord.gg/pvy7H8DZMG) | |
| [Request more models](https://github.com/RichardErkhov/quant_request) | |
| GNER-LLaMA-7B - GGUF | |
| - Model creator: https://huggingface.co/dyyyyyyyy/ | |
| - Original model: https://huggingface.co/dyyyyyyyy/GNER-LLaMA-7B/ | |
| | Name | Quant method | Size | | |
| | ---- | ---- | ---- | | |
| | [GNER-LLaMA-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q2_K.gguf) | Q2_K | 2.36GB | | |
| | [GNER-LLaMA-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.IQ3_XS.gguf) | IQ3_XS | 2.6GB | | |
| | [GNER-LLaMA-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.IQ3_S.gguf) | IQ3_S | 2.75GB | | |
| | [GNER-LLaMA-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q3_K_S.gguf) | Q3_K_S | 2.75GB | | |
| | [GNER-LLaMA-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.IQ3_M.gguf) | IQ3_M | 2.9GB | | |
| | [GNER-LLaMA-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q3_K.gguf) | Q3_K | 3.07GB | | |
| | [GNER-LLaMA-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q3_K_M.gguf) | Q3_K_M | 3.07GB | | |
| | [GNER-LLaMA-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q3_K_L.gguf) | Q3_K_L | 3.35GB | | |
| | [GNER-LLaMA-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.IQ4_XS.gguf) | IQ4_XS | 3.4GB | | |
| | [GNER-LLaMA-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q4_0.gguf) | Q4_0 | 3.56GB | | |
| | [GNER-LLaMA-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.IQ4_NL.gguf) | IQ4_NL | 3.58GB | | |
| | [GNER-LLaMA-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q4_K_S.gguf) | Q4_K_S | 3.59GB | | |
| | [GNER-LLaMA-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q4_K.gguf) | Q4_K | 3.8GB | | |
| | [GNER-LLaMA-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q4_K_M.gguf) | Q4_K_M | 3.8GB | | |
| | [GNER-LLaMA-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q4_1.gguf) | Q4_1 | 3.95GB | | |
| | [GNER-LLaMA-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q5_0.gguf) | Q5_0 | 4.33GB | | |
| | [GNER-LLaMA-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q5_K_S.gguf) | Q5_K_S | 4.33GB | | |
| | [GNER-LLaMA-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q5_K.gguf) | Q5_K | 4.45GB | | |
| | [GNER-LLaMA-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q5_K_M.gguf) | Q5_K_M | 4.45GB | | |
| | [GNER-LLaMA-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q5_1.gguf) | Q5_1 | 4.72GB | | |
| | [GNER-LLaMA-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q6_K.gguf) | Q6_K | 5.15GB | | |
| | [GNER-LLaMA-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/dyyyyyyyy_-_GNER-LLaMA-7B-gguf/blob/main/GNER-LLaMA-7B.Q8_0.gguf) | Q8_0 | 6.67GB | | |
| Original model description: | |
| --- | |
| 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) | |
| * 🎨 Example Jupyter Notebooks: [GNER Notebook](https://github.com/yyDing1/GNER/blob/main/notebook.ipynb) | |
| <p align="center"> | |
| <img src="https://github.com/yyDing1/GNER/raw/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 datasets deepspeed accelerate transformers protobuf | |
| ``` | |
| Please check out [Example Jupyter Notebooks](https://github.com/yyDing1/GNER/blob/main/notebook.ipynb) for guidance on utilizing GNER models. | |
| A simple inference example is as follows: | |
| Below is an example using `GNER-LLaMA` | |
| ```python | |
| >>> import torch | |
| >>> from transformers import AutoTokenizer, AutoModelForCausalLM | |
| >>> tokenizer = AutoTokenizer.from_pretrained("dyyyyyyyy/GNER-LLaMA-7B") | |
| >>> model = AutoModelForCausalLM.from_pretrained("dyyyyyyyy/GNER-LLaMA-7B", torch_dtype=torch.bfloat16).cuda() | |
| >>> 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" | |
| >>> sentence = "did george clooney make a musical in the 1980s" | |
| >>> 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(entity_labels)} and O.\nSentence: {sentence}" | |
| >>> 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[response.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} | |
| } | |
| ``` | |