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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e74234df1777b36fa376a9cfd62d759256d35e6f3701529e020b00318d840c67
- Size of remote file:
- 445 MB
- SHA256:
- 1ccb69a875c7248a17c1ed56d27e5dc00823b7145e6d0a1fe54be24151a91f15
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