Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use SirbayC/SampleHFModel_IMDBReviewSentimentAnalysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SirbayC/SampleHFModel_IMDBReviewSentimentAnalysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SirbayC/SampleHFModel_IMDBReviewSentimentAnalysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SirbayC/SampleHFModel_IMDBReviewSentimentAnalysis") model = AutoModelForSequenceClassification.from_pretrained("SirbayC/SampleHFModel_IMDBReviewSentimentAnalysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b3eb2cd8ddd311c0602f25334965c6aa36c2981f5a180b23a3e472caf02f5f9
- Size of remote file:
- 268 MB
- SHA256:
- 3fbef7d539faba6660f8c99de6f0fcc655108882ec1256edd34e53eeb5cddae2
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