UniNER-7B-all-GGUF

UniNER-7B-all-GGUF

• Model creator: Universal-NER
• Original model: UniNER-7B-all

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Description

This repo contains GGUF format model files for UniNER-7B-all.

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About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF:

• llama.cpp. The source project for GGUF. Offers a CLI and a server option.
• text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
• KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
• GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
• LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
• LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
• Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
• llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
• candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
• ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

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Original README.MD is as follows.

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UniNER-7B-all

Description: This model is the best UniNER model. It is trained on the combinations of three data splits: (1) ChatGPT-generated Pile-NER-type data, (2) ChatGPT-generated Pile-NER-definition data, and (3) 40 supervised datasets in the Universal NER benchmark (see Fig. 4 in paper), where we randomly sample up to 10K instances from the train split of each dataset. Note that CrossNER and MIT datasets are excluded from training for OOD evaluation.

Check our paper for more information. Check our repo about how to use the model.

Inference

The template for inference instances is as follows:

<div style="background-color: #f6f8fa; padding: 20px; border-radius: 10px; border: 1px solid #e1e4e8; box-shadow: 0 2px 5px rgba(0,0,0,0.1);"> <strong>Prompting template:</strong><br/> A virtual assistant answers questions from a user based on the provided text.<br/> USER: Text: <span style="color: #d73a49;">{Fill the input text here}</span><br/> ASSISTANT: I’ve read this text.<br/> USER: What describes <span style="color: #d73a49;">{Fill the entity type here}</span> in the text?<br/> ASSISTANT: <span style="color: #0366d6;">(model's predictions in JSON format)</span><br/> </div>

Note: Inferences are based on one entity type at a time. For multiple entity types, create separate instances for each type.

License

This model and its associated data are released under the CC BY-NC 4.0 license. They are primarily used for research purposes.

Citation

@article{zhou2023universalner,
      title={UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition}, 
      author={Wenxuan Zhou and Sheng Zhang and Yu Gu and Muhao Chen and Hoifung Poon},
      year={2023},
      eprint={2308.03279},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}