Text Classification
Transformers
TensorFlow
TensorBoard
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use keras-io/sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keras-io/sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="keras-io/sentiment-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("keras-io/sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("keras-io/sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from keras-io/sentiment-analysis: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/keras-io/sentiment-analysis/resolve/main/tf_model.h5
- Command line
-
hf download hf://keras-io/sentiment-analysis/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/keras-io/sentiment-analysis/resolve/main/tf_model.h5
268 MB
- Xet hash:
- 9ff7b859f72b3b9d651e9d8198ff1806b5056d16ec2e1308b609a6d0970d33f8
- Size of remote file:
- 268 MB
- SHA256:
- a8f4f1edbb3ca782e19828a0e7b82aadc6a5a0b49a454828d4c3699c080c63b4
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