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- ---
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- library_name: transformers
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- tags:
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- - bangla
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- - bangla-classifier
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- - binary-classifier
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- - text-classifier
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- ---
 
 
 
 
 
 
 
 
 
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  # Bangla Binary Text Classifier
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@@ -20,18 +29,29 @@ This is a **Bangla binary sentiment classification** model, fine-tuned on top of
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  ```python
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  from transformers import pipeline
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- pipe = pipeline("text-classification", model="SayedShaun/bangla-classifier-binary")
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- response = pipe("এটা যে এত খারাপ আগে জানতাম না।")
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  print(response)
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- # Output: [{'label': 'LABEL_0', 'score': 0.9765}]
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  ## Result
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  | Training Loss | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |---------------|-----------------|-----------|-----------|----------|-----------|
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- | 0.354600 | 0.396599 | 0.825143 | 0.812587 | 0.842483 | 0.827265 |
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  # Source Code
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- Source code can be found in `files and versions` as `finetune.py`
 
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+ ---
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+ library_name: transformers
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+ tags:
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+ - bangla
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+ - bangla-classifier
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+ - multiclass-classifier
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+ - text-classifier
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+ datasets:
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+ - SayedShaun/sentigold
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+ language:
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+ - bn
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+ metrics:
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+ - accuracy
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+ base_model:
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+ - csebuetnlp/banglabert
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+ pipeline_tag: text-classification
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+ ---
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  # Bangla Binary Text Classifier
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  ```python
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  from transformers import pipeline
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+ pipe = pipeline("text-classification", model="SayedShaun/bangla-classifier-multiclass")
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+ response = pipe("ডেলিভারি ম্যান খুব যত্ন সহকারে পণ্যটি ডেলিভারি করেছে")
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  print(response)
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+ # Output: [{'label': 'LABEL_0', 'score': 0.9503920674324036}]
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+ ```
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+
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+ ## Tags
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+ ```
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+ {"SP" :0, "WP": 1, "WN": 2, "SN": 3, "NU": 4}
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+
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+ SP: Strongly Positive
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+ WP: Weakly Positive
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+ WN: Weakly Positive Negative
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+ SN: Strongly Negative
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+ NU: Neutral
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  ```
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  ## Result
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  | Training Loss | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |---------------|-----------------|-----------|-----------|----------|-----------|
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+ | 0.820600 | 0.916846 | 0.646714 | 0.649295 | 0.642749 | 0.643535 |
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  # Source Code
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+ Source code can be found in `files and versions` as `finetune.py`