Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use nathanReitinger/mlcb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nathanReitinger/mlcb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nathanReitinger/mlcb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nathanReitinger/mlcb") model = AutoModelForSequenceClassification.from_pretrained("nathanReitinger/mlcb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from nathanReitinger/mlcb: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/nathanReitinger/mlcb/resolve/main/tf_model.h5
- Command line
-
hf download hf://nathanReitinger/mlcb/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/nathanReitinger/mlcb/resolve/main/tf_model.h5
334 MB
- Xet hash:
- 0fca8203a38b129b1ab81ca04db3cf925daee128339feea578ece7c50d8edc66
- Size of remote file:
- 334 MB
- SHA256:
- f4abd70dada3b1248a8e2981aea5583f83887f2492f20aa2051a81e7ffb4e1ce
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