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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d27d511d4e6d97bd23e5c24bc5390bde48b0c5db4ef6bb8e39e7c2f7af9571b3
- Size of remote file:
- 5.84 kB
- SHA256:
- 4b9ec8185364ebd0a2216590696bf4af91571015ab25130e1e70fb7fc28c22fb
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