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Consensus
/

instructor-base

Sentence Similarity

sentence-transformers

information-retrieval

text-classification

text-clustering

text-semantic-similarity

text-evaluation

prompt-retrieval

feature-extraction

Sentence Similarity

natural_questions

Eval Results (legacy)

Model card Files Files and versions

Instructions to use Consensus/instructor-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

sentence-transformers

How to use Consensus/instructor-base with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Consensus/instructor-base")

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]

How to use Consensus/instructor-base with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("Consensus/instructor-base")
model = AutoModel.from_pretrained("Consensus/instructor-base", device_map="auto")

Notebooks
Google Colab
Kaggle

instructor-base / spiece.model

Commit History

May 10, 2023

18:05
Upload 11 files

bneb-consensus

07f3abe