Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use jethrowang/text_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jethrowang/text_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jethrowang/text_classification_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jethrowang/text_classification_model") model = AutoModelForSequenceClassification.from_pretrained("jethrowang/text_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0457511699e55273af8c0a6dbcc8632318720e556e3a2c4760a4b659ec131f5c
- Size of remote file:
- 268 MB
- SHA256:
- 30a32a36e00392342492fd90d1fe0eac9be4c8223ae24b6124b1855636ce409e
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