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:
- d0bbdac82bbb3e89614eae60b20719b0994bd08d095c9e9267aaa715df490f57
- Size of remote file:
- 3.25 kB
- SHA256:
- c1d38e4990601b241550d2b24dc01d14d1dedd46b39ba40dc34bd05169732d5f
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