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")# 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
- Xet hash:
- 51638f436683af9023bc4dfff803600119b48918cbc20c300a0332671cfcaf19
- Size of remote file:
- 1.11 GB
- SHA256:
- 849a9c4fc750bba438dfa9f43be09ead55200315f20e38cc0a0146673043265a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.