Oblivion22/distilbert-imdb-sentiment

Model Card for Model ID

Model Details

Model Description

This model is a fine-tuned DistilBERT classifier for binary sentiment analysis of English movie reviews.

The model was fine-tuned from distilbert-base-uncased on the IMDB movie review dataset. It takes a movie review as input and predicts one of two sentiment classes: Negative or Positive.

Model Sources

• Base Model: distilbert-base-uncased
• Dataset: IMDB Movie Reviews
• GitHub Repository: distilbert-imdb-sentiment
• Live Demo: Streamlit App

Uses

Direct Use

[This model can be used directly to classify English movie reviews as positive or negative without additional fine-tuning. It is intended for sentiment analysis of text similar to the IMDB movie review data used during fine-tuning.]

Downstream Use [optional]

[The model can be integrated into applications that require movie-review sentiment analysis, such as review analysis tools, educational NLP projects, or simple text classification applications.]

Out-of-Scope Use

[This model is not intended for high-stakes decision making, profiling individuals, or sentiment analysis in domains that differ substantially from movie reviews without additional evaluation.]

Bias, Risks, and Limitations

[The model can make incorrect predictions for reviews containing mixed sentiment, sarcasm, ambiguous wording, or context-dependent expressions. It was fine-tuned specifically on IMDB movie reviews, so its performance may vary on text from other domains.

Reviews longer than 256 tokens are truncated during inference, which may remove useful context from longer inputs. The confidence score is a softmax probability and should not be interpreted as a guarantee that the prediction is correct.]

Recommendations

The model should be used primarily for sentiment analysis of text similar to the data it was trained on. Predictions should be reviewed when the input contains sarcasm, mixed opinions, or other context-heavy language.

How to Get Started with the Model

How to Get Started with the Model

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Oblivion22/distilbert-imdb-sentiment"
)

result = classifier("This movie was absolutely amazing!")
print(result)

## Training Details

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## Evaluation

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### Results

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#### Summary



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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).

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## Technical Specifications [optional]

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