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csebuetnlp/banglabert. The model was trained using the SayedShaun/sentigold1from transformers import pipeline
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3pipe = pipeline("text-classification", model="SayedShaun/bangla-classifier-multiclass")
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5response = pipe("ডেলিভারি ম্যান খুব যত্ন সহকারে পণ্যটি ডেলিভারি করেছে")
6print(response)
7>>> [{'label': 'LABEL_0', 'score': 0.9503920674324036}]{"SP" :0, "WP": 1, "WN": 2, "SN": 3, "NU": 4}
SP: Strongly Positive
WP: Weakly Positive
WN: Weakly Positive Negative
SN: Strongly Negative
NU: Neutral| Training Loss | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|---|
| 0.820600 | 0.916846 | 0.646714 | 0.649295 | 0.642749 | 0.643535 |
files and versions as finetune.py