Instructions to use RJ3vans/SignTagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RJ3vans/SignTagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RJ3vans/SignTagger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RJ3vans/SignTagger") model = AutoModelForTokenClassification.from_pretrained("RJ3vans/SignTagger") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -39,6 +39,8 @@ inputs = SignTaggingTokenizer.encode(sentence, return_tensors="pt")
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outputs = SignTaggingModel(inputs)[0]
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predictions = torch.argmax(outputs, dim=2)
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print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].tolist())])
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======================================================================
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outputs = SignTaggingModel(inputs)[0]
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predictions = torch.argmax(outputs, dim=2)
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print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].tolist())])
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======================================================================
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