Token Classification
Transformers
PyTorch
Safetensors
Turkish
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-tr 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-tr 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-tr")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-tr") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-tr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cd084e94c95ceab343825d3e51408c0645ba8fe1af7a70f33d2af2e5b6fb467
- Size of remote file:
- 1.11 GB
- SHA256:
- d8a1726aad8cecf7277ad6c79bd7a930b8ec2af9ecd71b33a7f23d1cd076a2a5
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