added bangla-bert-sentiment model card (#8687)

model_cards/sagorsarker/bangla-bert-sentiment/README.md

@@ -0,0 +1,61 @@
+---
+language:
+- bn
+datasets:
+- socian
+- bangla-sentiment-benchmark
+license: mit
+tags:
+- bengali
+- bengali-sentiment
+- sentiment-analysis
+---
+
+# bangla-bert-sentiment
+`bangla-bert-sentiment` is a pretrained model for bengali **Sentiment Analysis** using [bangla-bert-base](https://huggingface.co/sagorsarker/bangla-bert-base) model.
+
+## Datasets Details
+This model was trained with two combined datasets
+* [socian sentiment data](https://github.com/socian-ai/socian-bangla-sentiment-dataset-labeled)
+* [bangla classification dataset](https://github.com/rezacsedu/Classification_Benchmarks_Benglai_NLP)
+
+|||
+|--|--|
+|Data Size| 10889 |
+|Positive| 4999 |
+|Negative| 5890 |
+|Train | 8711 |
+| Test | 2178 |
+
+## Training Details
+Model trained with [simpletransformers](https://github.com/ThilinaRajapakse/simpletransformers) binary classification script with total of **3 epochs** in `google colab gpu`.
+
+
+## Evaluation Details
+Model evaluate with 2178 sentences
+
+Here is the evaluation result details in table
+
+
+|Eval Loss | TP | TN | FP | FN | F1 Score |
+| -------- | -- | -- | -- | -- | -------- |
+| 0.3289 | 880 | 1158 | 59 | 81 | 92.63 |
+
+## Usage
+
+Calculate sentiment from given sentence
+
+```py
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("sagorsarker/bangla-bert-sentiment")
+
+model = AutoModelForSequenceClassification.from_pretrained("sagorsarker/bangla-bert-sentiment")
+
+nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+sentence = "বাংলার ঘরে ঘরে আজ নবান্নের উৎসব"
+nlp(sentence)
+
+```
+