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--- |
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license: mit |
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datasets: |
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- MultiCoNER/multiconer_v2 |
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language: |
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- en |
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metrics: |
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- f1 |
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- precision |
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- recall |
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base_model: |
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- FacebookAI/xlm-roberta-large |
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pipeline_tag: token-classification |
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tags: |
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- NER |
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- Named_Entity_Recognition |
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pretty_name: MultiCoNER2 English XLM-RoBERTa |
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--- |
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**XLM-RoBERTa is fine-tuned on English [MultiCoNER2](https://huggingface.co/datasets/MultiCoNER/multiconer_v2) dataset for Fine-grained Named Entity Recognition.** |
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The tagset of [MultiCoNER2](https://huggingface.co/datasets/MultiCoNER/multiconer_v2) is a fine-grained tagset. The fine to coarse level mapping of the tags are as follows: |
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* Location (LOC) : Facility, OtherLOC, HumanSettlement, Station |
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* Creative Work (CW) : VisualWork, MusicalWork, WrittenWork, ArtWork, Software |
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* Group (GRP) : MusicalGRP, PublicCORP, PrivateCORP, AerospaceManufacturer, SportsGRP, CarManufacturer, ORG |
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* Person (PER) : Scientist, Artist, Athlete, Politician, Cleric, SportsManager, OtherPER |
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* Product (PROD) : Clothing, Vehicle, Food, Drink, OtherPROD |
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* Medical (MED) : Medication/Vaccine, MedicalProcedure, AnatomicalStructure, Symptom, Disease |
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## Model performance: |
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Precision: 78.29 <br> |
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Recall: 80.94 <br> |
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**F1: 79.59** <br> |
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## Training Parameters: |
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Epochs: 6 <br> |
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Optimizer: AdamW <br> |
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Learning Rate: 5e-5 <br> |
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Weight Decay: 0.01 <br> |
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Batch Size: 64 <br> |
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## Citation |
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If you use this model, please cite the following papers: |
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```bibtex |
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@inproceedings{fetahu2023multiconer, |
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title={MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition}, |
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author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Rokhlenko, Oleg and Malmasi, Shervin}, |
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booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023}, |
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pages={2027--2051}, |
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year={2023} |
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} |
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@inproceedings{kaushik2026sampurner, |
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title={SampurNER: Fine-grained Named Entity Recognition Dataset for 22 Indian Languages}, |
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author={Kaushik, Prachuryya and Anand, Ashish}, |
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booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, |
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volume={40}, |
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year={2026} |
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} |
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