Text Classification
Transformers
Safetensors
bert
Sentiment Analysis
DistilBERT
Text Classification
Hotel Reviews
text-embeddings-inference
Instructions to use kmack/HotelReviewClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kmack/HotelReviewClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kmack/HotelReviewClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kmack/HotelReviewClassifier") model = AutoModelForSequenceClassification.from_pretrained("kmack/HotelReviewClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d144c140e4d8e8f9981f0977240aa85b3c49980a84ea463daa545a48894b0b09
- Size of remote file:
- 438 MB
- SHA256:
- d37c94cd98c9ef2eba88d4846f178c4ecf8da1db35f21264b6ed0804a95c3479
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