br090491/bert-sst2-finetuned

Model Card for Model ID

This is a fine tuned BERT for binary sentiment on SST2 that classifies the sentences as Positive or Negative snetiments. Built upon original bert-base-uncased model

Model Details

Model Description

Starting from a bert-base uncased model I finetuned the model on SST2 to give two output classifications based on the input sentence. The classification sentiment can be either Positive (1) or Negative (0)

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: br090491
• Funded by None
• Shared by br090491
• Model type: Classification
• Language(s) (NLP): English
• License: apache-2.0
• Finetuned from model [optional]: bert-base-uncased

Model Sources [optional]

• Repository: [bert-sst2-finetuned]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

To classify if the sentiment of a sentence is positive or negative

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

Anything apart from positive or negative sentiment analysis of a sentence

Bias, Risks, and Limitations

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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

from transformers import pipeline

classifier = pipeline("text-classification", model="br090491/bert-sst2-finetuned")

print(classifier("This movie was absolutely fantastic, I loved it!"))
print(classifier("This film was a complete waste of time."))
print(classifier("Add your own sentence"))
# [{'label': 'positive', 'score': 0.9998}]

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

Fine-tuned on SST-2, this model reaches 92.5% accuracy on the validation set.

Accuracy is the right metric here because this is a balanced binary classifier — each input gets one of two labels and the classes are roughly even, so the fraction correct is a faithful summary. (Perplexity does not apply: it scores next-token language models, not classifiers.)

Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Software

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Citation [optional]

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