File size: 1,493 Bytes
579775f 5f3d17d 902bfcf 5f3d17d 902bfcf 5f3d17d 902bfcf 5f3d17d 902bfcf 5f3d17d 902bfcf 5f3d17d 902bfcf 5f3d17d 579775f 5f3d17d 579775f 902bfcf 579775f 902bfcf 5f3d17d 902bfcf 579775f 5f3d17d 902bfcf 579775f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
---
library_name: transformers
tags:
- bangla
- bangla-classifier
- multiclass-classifier
- text-classifier
datasets:
- SayedShaun/sentigold
language:
- bn
metrics:
- accuracy
base_model:
- csebuetnlp/banglabert
pipeline_tag: text-classification
---
# Bangla Binary Text Classifier
## Description
This is a **Bangla binary sentiment classification** model, fine-tuned on top of [`csebuetnlp/banglabert`](https://huggingface.co/csebuetnlp/banglabert). The model was trained using the [**SayedShaun/sentigold**](https://huggingface.co/datasets/SayedShaun/sentigold)
---
## How to Use
```python
from transformers import pipeline
pipe = pipeline("text-classification", model="SayedShaun/bangla-classifier-multiclass")
response = pipe("ডেলিভারি ম্যান খুব যত্ন সহকারে পণ্যটি ডেলিভারি করেছে")
print(response)
# Output: [{'label': 'LABEL_0', 'score': 0.9503920674324036}]
```
## Tags
```
{"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
```
## Result
| Training Loss | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|---------------|-----------------|-----------|-----------|----------|-----------|
| 0.820600 | 0.916846 | 0.646714 | 0.649295 | 0.642749 | 0.643535 |
# Source Code
Source code can be found in `files and versions` as `finetune.py` |