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 train.args from wietsedv/xlm-roberta-base-ft-udpos28-fa: direct link, hf CLI and curl.
- Browser
- Download file 181 Bytes
-
https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-fa/resolve/main/train.args
- Command line
-
hf download hf://wietsedv/xlm-roberta-base-ft-udpos28-fa/train.args
-
curl -L -o train.args https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-fa/resolve/main/train.args
181 Bytes
| udpos -tt=token-classification -tn=udpos28 -mi=xlm-roberta-base -mt=ft --learning_rate=5e-5 --eval_steps=1000 --eval_batch_size=10 --train_batch_size=10 --num_train_epochs=3 --multi |