Instructions to use lamarr-llm-development/elbedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lamarr-llm-development/elbedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lamarr-llm-development/elbedding")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lamarr-llm-development/elbedding") model = AutoModel.from_pretrained("lamarr-llm-development/elbedding") - Notebooks
- Google Colab
- Kaggle
Update embedding_model.py
Browse files- embedding_model.py +2 -2
embedding_model.py
CHANGED
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@@ -57,8 +57,8 @@ class EmbeddingModel(models.Transformer):
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return {"model_name_or_path": self.model_name_or_path}
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def save(self, save_dir: str, **kwargs) -> None:
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with open(os.path.join(save_dir, "sentence_bert_config.json"), "w+") as f:
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json.dump(self.get_config_dict(), f, indent=4)
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return {"model_name_or_path": self.model_name_or_path}
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def save(self, save_dir: str, **kwargs) -> None:
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self.auto_model.save_pretrained(save_dir, safe_serialization=True)
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self.tokenizer.save_pretrained(save_dir)
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with open(os.path.join(save_dir, "sentence_bert_config.json"), "w+") as f:
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json.dump(self.get_config_dict(), f, indent=4)
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