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: 2,486 Bytes
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"f1-score": 0.7969543147208121,
"support": 194.0
},
"ADP": {
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},
"ADV": {
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"f1-score": 0.7351351351351352,
"support": 95.0
},
"AUX": {
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},
"CCONJ": {
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"recall": 1.0,
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},
"DET": {
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"f1-score": 0.8108108108108109,
"support": 93.0
},
"INTJ": {
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"NOUN": {
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"NUM": {
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"PART": {
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},
"PRON": {
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"PROPN": {
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"PUNCT": {
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"SCONJ": {
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"f1-score": 0.9516129032258065,
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"VERB": {
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"X": {
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"accuracy": 0.8929612661391088,
"macro avg": {
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"weighted avg": {
"precision": 0.8931804044150632,
"recall": 0.8929612661391088,
"f1-score": 0.8922390838062512,
"support": 2401.0
}
} |