---
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

<!-- Provide a quick summary of what the model is/does. -->

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

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- **Developed by:** Ahnaf Tahmeed.
- **Model type:** Transformer-based language model
- **Language(s) (NLP):** Bengali
- **License:** MIT
- **Related Models:** BERT, RoBERTA


### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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")

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Hardware

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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