Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Apv/Distilbert_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apv/Distilbert_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Apv/Distilbert_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Apv/Distilbert_v1") model = AutoModelForSequenceClassification.from_pretrained("Apv/Distilbert_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Apv/Distilbert_v1: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Apv/Distilbert_v1/resolve/main/tf_model.h5
- Command line
-
hf download hf://Apv/Distilbert_v1/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Apv/Distilbert_v1/resolve/main/tf_model.h5
268 MB
- Xet hash:
- 534184f19dfb501d1b60079c55bc94f97c967621eaf4f0c58c71092c00dcbe6f
- Size of remote file:
- 268 MB
- SHA256:
- 475f56b112fbaa15387c9bcbc1aec9e00b4487e80f88a2dde403c6a54ae40520
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