license: mit
language:
- bn
metrics:
- accuracy
- bertscore
pipeline_tag: text-classification
widget:
- text: আমি ফুটবল খেলতে ভালোবাসি
output:
- label: POSITIVE
score: 0.9
- label: NEGATIVE
score: 0.1
- text: আমার এই খাবারটা মোটেও পছন্দ হয়নি
output:
- label: POSITIVE
score: 0.1
- label: NEGATIVE
score: 0.9
tags:
- sentiment_analysis
Model Card for Model ID
This model is built on Bert model using a Bangla Sentiment analysis dataset which is collected from social media dramas public comments.
Model Details
Model Description
- Developed by: Ahnaf Tahmeed.
- Model type: Transformer-based language model
- Language(s) (NLP): Bengali
- License: MIT
- Related Models: BERT, RoBERTA
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="ahnaf702/Sentibert")
Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ahnaf702/Sentibert") model = AutoModelForSequenceClassification.from_pretrained("ahnaf702/Sentibert")
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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