sentence-transformers-e5-large

This is a the sentence-transformers version of the intfloat/e5-large-v2 model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('embaas/sentence-transformers-e5-large-v2')
embeddings = model.encode(sentences)
print(embeddings)

Using with API

You can use the embaas API to encode your input. Get your free API key from embaas.io

import requests

url = "https://api.embaas.io/v1/embeddings/"

headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer ${YOUR_API_KEY}"
}

data = {
    "texts": ["This is an example sentence.", "Here is another sentence."],
    "instruction": "query"
    "model": "e5-large-v2"
}

response = requests.post(url, json=data, headers=headers)

Evaluation Results

Find the results of the e5 at the MTEB leaderboard

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
  (2): Normalize()
)

Citing & Authors