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README.md

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+---
+license: apache-2.0
+language:
+  - multilingual
+library_name: gliner
+datasets:
+  - urchade/pile-mistral-v0.1
+pipeline_tag: token-classification
+---
+
+# About
+
+GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
+
+
+## Links
+
+* Paper: https://arxiv.org/abs/2311.08526
+* Repository: https://github.com/urchade/GLiNER
+
+## Available models
+
+| Release | Model Name | # of Parameters | Language | License |
+| - | - | - | - | - |
+| v0 | [urchade/gliner_base](https://huggingface.co/urchade/gliner_base)<br>[urchade/gliner_multi](https://huggingface.co/urchade/gliner_multi) | 209M<br>209M | English<br>Multilingual | cc-by-nc-4.0 |
+| v1 | [urchade/gliner_small-v1](https://huggingface.co/urchade/gliner_small-v1)<br>[urchade/gliner_medium-v1](https://huggingface.co/urchade/gliner_medium-v1)<br>[urchade/gliner_large-v1](https://huggingface.co/urchade/gliner_large-v1) | 166M<br>209M<br>459M | English <br> English <br> English | cc-by-nc-4.0 |
+| v2 | [urchade/gliner_small-v2](https://huggingface.co/urchade/gliner_small-v2)<br>[urchade/gliner_medium-v2](https://huggingface.co/urchade/gliner_medium-v2)<br>[urchade/gliner_large-v2](https://huggingface.co/urchade/gliner_large-v2) | 166M<br>209M<br>459M |  English <br> English <br> English | apache-2.0 |
+| v2.1 | [urchade/gliner_small-v2.1](https://huggingface.co/urchade/gliner_small-v2.1)<br>[urchade/gliner_medium-v2.1](https://huggingface.co/urchade/gliner_medium-v2.1)<br>[urchade/gliner_large-v2.1](https://huggingface.co/urchade/gliner_large-v2.1) <br>[urchade/gliner_multi-v2.1](https://huggingface.co/urchade/gliner_multi-v2.1) | 166M<br>209M<br>459M<br>209M | English <br> English <br> English <br> Multilingual | apache-2.0 |
+
+## Installation
+To use this model, you must install the GLiNER Python library:
+```
+!pip install gliner
+```
+
+## Usage
+Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
+
+```python
+from gliner import GLiNER
+
+model = GLiNER.from_pretrained("urchade/gliner_multi-v2.1")
+
+text = """
+Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time.
+"""
+
+labels = ["person", "award", "date", "competitions", "teams"]
+
+entities = model.predict_entities(text, labels)
+
+for entity in entities:
+    print(entity["text"], "=>", entity["label"])
+```
+
+```
+Cristiano Ronaldo dos Santos Aveiro => person
+5 February 1985 => date
+Al Nassr => teams
+Portugal national team => teams
+Ballon d'Or => award
+UEFA Men's Player of the Year Awards => award
+European Golden Shoes => award
+UEFA Champions Leagues => competitions
+UEFA European Championship => competitions
+UEFA Nations League => competitions
+Champions League => competitions
+European Championship => competitions
+```
+
+## Named Entity Recognition benchmark result
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317233cc92fd6fee317e030/Y5f7tK8lonGqeeO6L6bVI.png)
+
+## Model Authors
+The model authors are:
+* [Urchade Zaratiana](https://huggingface.co/urchade)
+* Nadi Tomeh
+* Pierre Holat
+* Thierry Charnois
+
+## Citation
+```bibtex
+@misc{zaratiana2023gliner,
+      title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer}, 
+      author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},
+      year={2023},
+      eprint={2311.08526},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

config.json

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+{
+  "model_type": "bert",
+  "size_sup": -1,
+  "max_types": 25,
+  "shuffle_types": true,
+  "random_drop": true,
+  "max_neg_type_ratio": 1,
+  "max_len": 384,
+  "lr_encoder": "1e-5",
+  "lr_others": "5e-5",
+  "num_steps": 30000,
+  "warmup_ratio": 3000,
+  "train_batch_size": 8,
+  "eval_every": 5000,
+  "max_width": 12,
+  "model_name": "microsoft/mdeberta-v3-base",
+  "fine_tune": true,
+  "subtoken_pooling": "first",
+  "hidden_size": 768,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "intermediate_size": 3072,
+  "span_mode": "markerV0",
+  "dropout": 0.4,
+  "name": "correct"
+}

gliner_config.json

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+{
+  "model_type": "bert",
+  "size_sup": -1,
+  "max_types": 25,
+  "shuffle_types": true,
+  "random_drop": true,
+  "max_neg_type_ratio": 1,
+  "max_len": 384,
+  "lr_encoder": "1e-5",
+  "lr_others": "5e-5",
+  "num_steps": 30000,
+  "warmup_ratio": 3000,
+  "train_batch_size": 8,
+  "eval_every": 5000,
+  "max_width": 12,
+  "model_name": "microsoft/mdeberta-v3-base",
+  "fine_tune": true,
+  "subtoken_pooling": "first",
+  "hidden_size": 768,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "intermediate_size": 3072,
+  "span_mode": "markerV0",
+  "dropout": 0.4,
+  "name": "correct"
+}

handler.py

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+from typing import Dict, List, Any
+from transformers import pipeline, AutoConfig, AutoModelForTokenClassification, AutoTokenizer, BertTokenizerFast
+import os
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        dir_model = "urchade/gliner_multi-v2.1"
+
+        config_path = os.path.join(path, "gliner_config.json")
+        if not os.path.exists(config_path):
+            raise FileNotFoundError(f"Custom configuration file not found at {config_path}")
+
+        # Load the custom configuration
+        config = AutoConfig.from_pretrained(config_path)
+
+        # Load the model using the custom configuration
+        self.model = AutoModelForTokenClassification.from_pretrained(dir_model, config=config)
+
+        # Initialize the pipeline with the model and tokenizer
+        # Use a pre-trained tokenizer compatible with your model
+        self.tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
+        # Use a pipeline appropriate for your task. Here we use "token-classification" for NER (Named Entity Recognition).
+        self.pipeline = pipeline("token-classification", model=path, tokenizer=self.tokenizer)
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        Args:
+            data (Dict[str, Any]): The input data including:
+                - "inputs": The text input from which to extract information.
+
+        Returns:
+            List[Dict[str, Any]]: The extracted information from the text.
+        """
+        # Get inputs
+        inputs = data.get("inputs", "")
+
+        # Run the pipeline for text extraction
+        extraction_results = self.pipeline(inputs)
+
+        # Process and return the results as needed
+        return extraction_results

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:580d8b061bcb6596bd79be7f29736cea84fdfef0679fc3ae9c14c08d2e974fad
+size 1155900362

requirements.txt

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+holidays

test.py

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+# from handler import EndpointHandler
+#
+# # init handler
+# my_handler = EndpointHandler(path=".")
+#
+# # prepare sample payload
+# non_holiday_payload = {"inputs": "I am quite excited how this will turn out", "date": "2022-08-08"}
+# holiday_payload = {"inputs": "Today is a though day", "date": "2022-07-04"}
+#
+# # test the handler
+# non_holiday_pred=my_handler(non_holiday_payload)
+# holiday_payload=my_handler(holiday_payload)
+#
+# # show results
+# print("non_holiday_pred", non_holiday_pred)
+# print("holiday_payload", holiday_payload)
+#
+# # non_holiday_pred [{'label': 'joy', 'score': 0.9985942244529724}]
+# # holiday_payload [{'label': 'happy', 'score': 1}]
+from handler import EndpointHandler
+
+handler = EndpointHandler(path=".")
+
+# Example input data
+test_data = {
+    "inputs": "John Doe visited New York last week."
+}
+
+# Invoke the handler
+result = handler(test_data)
+print(result)
+