What is all-MiniLM-L6-v2

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Usage (Sentence-Transformers)

pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer

sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

embeddings = model.encode(sentences)

print(embeddings)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel

import torch

import torch.nn.functional as F

#Mean Pooling - Take attention mask into account for correct averaging

def mean_pooling(model_output, attention_mask):

    token_embeddings = model_output[0] #First element of model_output contains all token embeddings

    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()

    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Sentences we want sentence embeddings for

sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub

tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')

model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')

# Tokenize sentences

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings

with torch.no_grad():

    model_output = model(**encoded_input)

# Perform pooling

sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

# Normalize embeddings

sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)

print("Sentence embeddings:")

print(sentence_embeddings)

References:

https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2