YAML Metadata Warning:The pipeline tag "sentiment-analysis" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
IMDB Sentiment Analysis using DistilBERT
This model is fine-tuned on the IMDB movie reviews dataset for sentiment analysis.
Model Details
- Base Model: distilbert-base-uncased
- Framework: Hugging Face Transformers
- Task: Sentiment Analysis
- Dataset: IMDB Dataset
Labels
- POSITIVE
- NEGATIVE
Example Usage
from transformers import pipeline
classifier = pipeline(
"sentiment-analysis",
model="NoureEzzA1arab/imdb-distilbert-sentiment"
)
result = classifier("This movie was fantastic and emotional.")
print(result)
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