Instructions to use Apv/Flaubert2904_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apv/Flaubert2904_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Apv/Flaubert2904_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Apv/Flaubert2904_v2") model = AutoModelForSequenceClassification.from_pretrained("Apv/Flaubert2904_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Apv/Flaubert2904_v2: direct link, hf CLI and curl.
- Browser
- Download file 553 MB
-
https://huggingface.co/Apv/Flaubert2904_v2/resolve/main/tf_model.h5
- Command line
-
hf download hf://Apv/Flaubert2904_v2/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Apv/Flaubert2904_v2/resolve/main/tf_model.h5
553 MB
- Xet hash:
- daa05400c04f36e9ca8f10ce62c0ded77a9a376df796ac6aecd6e7fd6acb0d7c
- Size of remote file:
- 553 MB
- SHA256:
- 7d44f8500e940eec2a753f30b81d8ef51c64391bad3579b7b2cfe90105ebb1ab
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