Sentence Similarity
sentence-transformers
PyTorch
ONNX
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 vectoriseai/instructor-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vectoriseai/instructor-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vectoriseai/instructor-large") 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 vectoriseai/instructor-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vectoriseai/instructor-large") model = AutoModel.from_pretrained("vectoriseai/instructor-large") - Notebooks
- Google Colab
- Kaggle
config error
Browse files- 1_Pooling/config.json +1 -3
1_Pooling/config.json
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false
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}
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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