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
File size: 613 Bytes
b69e523 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ,precision,recall,f1-score,support
ADJ,0.785,0.8093,0.797,194.0
ADP,0.9517,0.9604,0.956,328.0
ADV,0.7556,0.7158,0.7351,95.0
AUX,0.9167,0.8919,0.9041,148.0
CCONJ,0.9434,1.0,0.9709,100.0
DET,0.8152,0.8065,0.8108,93.0
INTJ,0.0,0.0,0.0,1.0
NOUN,0.9198,0.8882,0.9037,671.0
NUM,0.7895,0.8824,0.8333,17.0
PART,0.8723,0.6508,0.7455,63.0
PRON,0.9101,0.9101,0.9101,178.0
PROPN,0.7681,0.8833,0.8217,60.0
PUNCT,0.9681,0.9945,0.9811,183.0
SCONJ,0.9219,0.9833,0.9516,60.0
VERB,0.84,0.9,0.869,210.0
X,0.0,0.0,0.0,0.0
accuracy,0.893,0.893,0.893,0.893
macro avg,0.7598,0.7673,0.7619,2401.0
weighted avg,0.8932,0.893,0.8922,2401.0
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