Instructions to use tblard/tf-allocine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tblard/tf-allocine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tblard/tf-allocine")# Load model directly from transformers import AutoTokenizer, TF_AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tblard/tf-allocine") model = TF_AutoModelForSequenceClassification.from_pretrained("tblard/tf-allocine", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update tf_model.h5
Browse files- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:1ccb69a875c7248a17c1ed56d27e5dc00823b7145e6d0a1fe54be24151a91f15
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size 445132512
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