Feature Extraction
Transformers
TensorFlow
TensorBoard
camembert
generated_from_keras_callback
text-embeddings-inference
Instructions to use RubenBueno/camembert-base-finetuned-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RubenBueno/camembert-base-finetuned-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RubenBueno/camembert-base-finetuned-text-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RubenBueno/camembert-base-finetuned-text-classification") model = AutoModel.from_pretrained("RubenBueno/camembert-base-finetuned-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from RubenBueno/camembert-base-finetuned-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/RubenBueno/camembert-base-finetuned-text-classification/resolve/main/tf_model.h5
- Command line
-
hf download hf://RubenBueno/camembert-base-finetuned-text-classification/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/RubenBueno/camembert-base-finetuned-text-classification/resolve/main/tf_model.h5
443 MB
- Xet hash:
- 951068e0e725a72d94f0a13fca54b1bcd8d15979b1a90f9a837f3dfcd79c212a
- Size of remote file:
- 443 MB
- SHA256:
- 0de732b204f485848b94db5c0a7ca47d4a34e8f83de87bf4fae23530b5fe736c
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