Instructions to use modularStarEncoder/ModularStarEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modularStarEncoder/ModularStarEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="modularStarEncoder/ModularStarEncoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("modularStarEncoder/ModularStarEncoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -49,7 +49,7 @@ sentence = f"{tokenizer.sep_token}{code_snippet}{tokenizer.cls_token}"
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tokenized_sensence = tokenizer(sentence, return_tensors="pt",truncation=True, max_length=2048)
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#Embedding the tokenized sentence
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embedded_sentence = model(**
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```
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You will get as an output six elements:
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tokenized_sensence = tokenizer(sentence, return_tensors="pt",truncation=True, max_length=2048)
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#Embedding the tokenized sentence
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embedded_sentence = model(**tokenized_sensence)
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```
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You will get as an output six elements:
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