Token Classification
Transformers
PyTorch
Safetensors
English
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wietsedv/xlm-roberta-base-ft-udpos28-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wietsedv/xlm-roberta-base-ft-udpos28-en")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-en") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-en") - Notebooks
- Google Colab
- Kaggle
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This model is part of our paper called:
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- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
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Check the [Space](
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This model is part of our paper called:
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- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
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Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForTokenClassification
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tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-en")
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model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-en")
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```
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