Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
t5
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
prompt-retrieval
text-reranking
feature-extraction
English
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
Eval Results (legacy)
Instructions to use baseplate/instructor-large-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use baseplate/instructor-large-1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("baseplate/instructor-large-1") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use baseplate/instructor-large-1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("baseplate/instructor-large-1") model = AutoModel.from_pretrained("baseplate/instructor-large-1") - Notebooks
- Google Colab
- Kaggle
Update handler.py
Browse files- handler.py +0 -2
handler.py
CHANGED
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@@ -23,7 +23,5 @@ class EndpointHandler():
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instruction = data.pop("instruction",data)
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text = data.pop("text", data)
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inputs = [[s, instruction] for s in text]
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if (self.device):
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inputs = torch.tensor(inputs).to(self.device) # Move inputs to the GPU
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embeddings = self.model.encode(inputs)
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return embeddings.tolist()
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instruction = data.pop("instruction",data)
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text = data.pop("text", data)
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inputs = [[s, instruction] for s in text]
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embeddings = self.model.encode(inputs)
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return embeddings.tolist()
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