Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use abilfad/sentiment-binary-dicoding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abilfad/sentiment-binary-dicoding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abilfad/sentiment-binary-dicoding")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abilfad/sentiment-binary-dicoding") model = AutoModelForSequenceClassification.from_pretrained("abilfad/sentiment-binary-dicoding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from abilfad/sentiment-binary-dicoding: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/abilfad/sentiment-binary-dicoding/resolve/main/tf_model.h5
- Command line
-
hf download hf://abilfad/sentiment-binary-dicoding/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/abilfad/sentiment-binary-dicoding/resolve/main/tf_model.h5
268 MB
- Xet hash:
- 04c8b300e8333d117d8999f73e14fd34e3e2a08d80f619c486f8a3fe4acf9838
- Size of remote file:
- 268 MB
- SHA256:
- 030cb5595d72a34fba534e254b75f7f8f388dcadab146e1638990d4fa39e2451
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