Instructions to use Amite5h/TextClassificationmulticlass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amite5h/TextClassificationmulticlass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Amite5h/TextClassificationmulticlass")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Amite5h/TextClassificationmulticlass") model = AutoModelForSequenceClassification.from_pretrained("Amite5h/TextClassificationmulticlass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Amite5h/TextClassificationmulticlass: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Amite5h/TextClassificationmulticlass/resolve/main/tf_model.h5
- Command line
-
hf download hf://Amite5h/TextClassificationmulticlass/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Amite5h/TextClassificationmulticlass/resolve/main/tf_model.h5
268 MB
- Xet hash:
- 576e4790042000b444f6a89453f9b67d91e465ad6da36f97ad317cf540480e86
- Size of remote file:
- 268 MB
- SHA256:
- 0ef3fdb2cffca17cec9c441ef05196b212f73f95bd47a8ff8eab5c34ec8922c4
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