Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Apv/DistilBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apv/DistilBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Apv/DistilBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Apv/DistilBERT") model = AutoModelForSequenceClassification.from_pretrained("Apv/DistilBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Apv/DistilBERT: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Apv/DistilBERT/resolve/main/tf_model.h5
- Command line
-
hf download hf://Apv/DistilBERT/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Apv/DistilBERT/resolve/main/tf_model.h5
268 MB
- Xet hash:
- d38ce24a02d0032951b34462b77a627ee06de2089bdc703ebf83e4043994e6b1
- Size of remote file:
- 268 MB
- SHA256:
- cacea847b397663abdc5a8b960fc4111f74947a24d100a61dcaa2e2210c8b301
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