SayedShaun/sentigold
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How to use SayedShaun/bangla-classifier-binary with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="SayedShaun/bangla-classifier-binary") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("SayedShaun/bangla-classifier-binary")
model = AutoModelForSequenceClassification.from_pretrained("SayedShaun/bangla-classifier-binary")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("SayedShaun/bangla-classifier-binary")
model = AutoModelForSequenceClassification.from_pretrained("SayedShaun/bangla-classifier-binary")This is a Bangla binary sentiment classification model, fine-tuned on top of csebuetnlp/banglabert. The model was trained using the SayedShaun/sentigold dataset.
from transformers import pipeline
pipe = pipeline("text-classification", model="SayedShaun/bangla-classifier-binary")
response = pipe("এটা যে এত খারাপ আগে জানতাম না।")
print(response)
>>> [{'label': 'LABEL_0', 'score': 0.9765}]
| Training Loss | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|---|
| 0.354600 | 0.396599 | 0.825143 | 0.812587 | 0.842483 | 0.827265 |
Source code can be found in files and versions as finetune.py
Base model
csebuetnlp/banglabert
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SayedShaun/bangla-classifier-binary")