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rajaadil/
my-sentiment-model

Name: my-sentiment-model
Availability: InStock

distilbert/distilbert-base-uncased

finetune

apache-2.0

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my-sentiment-model

A binary sentiment classification model that labels English text as POSITIVE or NEGATIVE. It is a fine-tuned version of distilbert-base-uncased trained on the IMDB movie reviews dataset.

Model description

The model takes a piece of English text and predicts whether the overall sentiment is positive or negative. It was built by fine-tuning DistilBERT — a smaller, faster distilled version of BERT — on labelled movie reviews, making it lightweight enough to run on CPU while retaining strong accuracy.

Base model: distilbert-base-uncased
Task: Binary sentiment classification (text-classification)
Language: English
Labels: NEGATIVE (0), POSITIVE (1)

Intended uses & limitations

Intended uses: Quick sentiment scoring of English text such as reviews, comments, and short feedback.

Limitations:

English only — performance on other languages is not reliable.
Trained on movie reviews, so accuracy is highest on review-style text and may drop on very different domains (e.g. legal, medical, or social media slang).
Binary output only; it does not detect neutral sentiment, sarcasm, or mixed opinions well.

How to use

python

from transformers import pipeline

clf = pipeline("text-classification", model="rajaadil/my-sentiment-model")
print(clf("This movie was absolutely fantastic!"))
# [{'label': 'POSITIVE', 'score': 0.98}]

Or load the model and tokenizer directly:

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("rajaadil/my-sentiment-model")
model = AutoModelForSequenceClassification.from_pretrained("rajaadil/my-sentiment-model")

Training data

The model was trained on a subset of the IMDB dataset:

Training set: 2,000 reviews
Evaluation set: 1,000 reviews

Training procedure

Hyperparameter	Value
Base model	distilbert-base-uncased
Epochs	1
Batch size	8
Max sequence length	256
Optimizer	AdamW (default)

Evaluation results

Metric	Value
Accuracy	0.869

Framework versions

Transformers
PyTorch
Datasets

Author

Built by rajaadil.