初始化项目，由ModelHub XC社区提供模型

Model: Universal-NER/UniNER-7B-definition
Source: Original Platform

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README.md

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+---
+license: cc-by-nc-4.0
+language:
+- en
+---
+
+---
+
+
+# UniNER-7B-definition
+
+**Description**: A UniNER-7B model trained from LLama-7B using the [Pile-NER-definition data](https://huggingface.co/datasets/Universal-NER/Pile-NER-definition) without human-labeled data. The data was collected by prompting gpt-3.5-turbo-0301 to label entities from passages and provide short-sentence definitions. The data collection prompt 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>Instruction:</strong><br/>
+Given a paragraph, your task is to extract all entities and concepts,
+and define their type using a short sentence. The output should be in the following format:
+[("entity", "definition of entity type in a short sentence"), ... ]
+</div>
+
+Check our [paper](https://arxiv.org/abs/2308.03279) for more information. Check our [repo](https://github.com/universal-ner/universal-ner) about how to use the model.
+
+## Comparison with [UniNER-7B-type](https://huggingface.co/Universal-NER/UniNER-7B-type)
+The UniNER-7B-type model, trained on Pile-NER-type, excels in recognizing common and short NER tags (e.g., person, location) and performs better on NER datasets. On the other hand, UniNER-7B-definition demonstrates superior capabilities in understanding short-sentence definitions of entity types. Additionally, it exhibits enhanced robustness against variations in type paraphrasing.
+
+## 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](https://creativecommons.org/licenses/by-nc/4.0/) license. They are primarily used for research purposes.
+
+## Citation
+
+```bibtex
+@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}
+}
+```

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.28.1",
+  "use_cache": false,
+  "vocab_size": 32000
+}

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+  "transformers_version": "4.28.1"
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special_tokens_map.json

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tokenizer_config.json

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