Token Classification
Transformers
PyTorch
Safetensors
Persian
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-fa 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-fa 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-fa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fa") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from wietsedv/xlm-roberta-base-ft-udpos28-fa: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-fa/resolve/main/model.safetensors
- Command line
-
hf download hf://wietsedv/xlm-roberta-base-ft-udpos28-fa/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-fa/resolve/main/model.safetensors
1.11 GB
- Xet hash:
- d06b91735539fb47d5e3c2c35e1c563f170eb2ab42d02be567eca983cd6e6850
- Size of remote file:
- 1.11 GB
- SHA256:
- dce66752eaebacc6140644c53ca11976699161c9e38ed47e449bab4785cb8f61
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