Token Classification
Transformers
Safetensors
PyTorch
Baluchi
xlm-roberta
part-of-speech
pos-tagging
balochi
low-resource
universal-dependencies
shahbakhsh
Eval Results (legacy)
Instructions to use shahbakhsh/BalPOS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shahbakhsh/BalPOS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="shahbakhsh/BalPOS")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("shahbakhsh/BalPOS") model = AutoModelForTokenClassification.from_pretrained("shahbakhsh/BalPOS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f2d2a826d60948bb911f85250e5d4bf0bd31563daeb842d9c25910b2891930c
- Size of remote file:
- 17.1 MB
- SHA256:
- 14c7e8bf7d9b58ca061fcda93bc8d0eedd1a51ffc3af01a1ba1ef54e2154887e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.