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 tokenizer.json from abilfad/sentiment-binary-dicoding: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/abilfad/sentiment-binary-dicoding/resolve/92da36df90cd596c09b727dc1019a248b3df9bbe/tokenizer.json
- Command line
-
hf download hf://abilfad/sentiment-binary-dicoding@92da36df90cd596c09b727dc1019a248b3df9bbe/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/abilfad/sentiment-binary-dicoding/resolve/92da36df90cd596c09b727dc1019a248b3df9bbe/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.