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Model Type: Transformer-based classifier (DistilBERT)
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Base Model: distilbert-base-uncased
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Language: English
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Task: Sentiment Analysis (binary classification)
Sentiment classification of English reviews, comments, or feedback.
Other languages.
Multi-label sentiment tasks (neutral/mixed).
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May not generalize well outside movie/review-style data.
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Training data may contain cultural and linguistic bias.
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Source: Kaggle Cleaned IMDB Reviews Dataset
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Size: ~50,000 reviews
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Classes: positive, negative
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Converted to integers: positive → 1, negative → 0
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Epochs: 3
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Batch Size: 16
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Optimizer: AdamW
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Learning Rate: 5e-5
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Framework: Hugging Face Trainer API
The model was tested on a held-out validation set of 9,917 reviews.
Class Precision Recall F1-score Support
Negative (0) 0.93 0.93 0.93 4,939
Positive (1) 0.93 0.93 0.93 4,978
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = "YamenRM/distilbert-sentiment-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
print(nlp("I really loved this movie, it was amazing!"))
