Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ett1112/amazon_sentiment_sample_of_1900_with_summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ett1112/amazon_sentiment_sample_of_1900_with_summary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ett1112/amazon_sentiment_sample_of_1900_with_summary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ett1112/amazon_sentiment_sample_of_1900_with_summary") model = AutoModelForSequenceClassification.from_pretrained("ett1112/amazon_sentiment_sample_of_1900_with_summary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20c2106df5384b8d6673e02dfe58fc579506196c5b1c53ad9723ff146a803df2
- Size of remote file:
- 268 MB
- SHA256:
- 55c2331e512dc1e26ff404d72f291febdb864b954c34a981844d2e3ff509d622
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