Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use daniel780/amazon_sentiment_sample_of_1900_with_summary_larger_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daniel780/amazon_sentiment_sample_of_1900_with_summary_larger_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="daniel780/amazon_sentiment_sample_of_1900_with_summary_larger_test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("daniel780/amazon_sentiment_sample_of_1900_with_summary_larger_test") model = AutoModelForSequenceClassification.from_pretrained("daniel780/amazon_sentiment_sample_of_1900_with_summary_larger_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31d1c718a022c819778a69ec05b64d569eefe03b0498e7f10eda69c6c9a02da3
- Size of remote file:
- 3.31 kB
- SHA256:
- 9a2b1a2c9faca8c8ed0b04c337b1461a2c3cafc681c6b697da2ad6e60a39304d
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