Instructions to use Apv/Flaubert_1106 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apv/Flaubert_1106 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Apv/Flaubert_1106")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Apv/Flaubert_1106") model = AutoModelForSequenceClassification.from_pretrained("Apv/Flaubert_1106", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Apv/Flaubert_1106: direct link, hf CLI and curl.
- Browser
- Download file 553 MB
-
https://huggingface.co/Apv/Flaubert_1106/resolve/main/tf_model.h5
- Command line
-
hf download hf://Apv/Flaubert_1106/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Apv/Flaubert_1106/resolve/main/tf_model.h5
553 MB
- Xet hash:
- fc245edb7987942ee5e692ae26c09b738ff195d5fbb0927e9bf80b171ccb4177
- Size of remote file:
- 553 MB
- SHA256:
- 2a24833cb037cb809cd97e08ca2408e9f8d8755bf61536477f138c41ea0f2ba4
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