Token Classification
Transformers
PyTorch
Safetensors
xlm-roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use universalner/uner_all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalner/uner_all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="universalner/uner_all")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("universalner/uner_all") model = AutoModelForTokenClassification.from_pretrained("universalner/uner_all") - Notebooks
- Google Colab
- Kaggle
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README.md
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the uner_all dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1180
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- Precision: 0.8566
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# uner_all
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the uner_all dataset.
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The uner_all dataset combines all training datasets in UNER.
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It achieves the following results on the evaluation set:
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- Loss: 0.1180
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- Precision: 0.8566
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