e5-small

intfloat
Similitud de oraciones

Noticias (Mayo 2023): Por favor cambie a e5-small-v2, que tiene un mejor rendimiento y el mismo método de uso. Text Embeddings por Pre-entrenamiento Contrastivo Débilmente Supervisado. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022. Este modelo tiene 12 capas y el tamaño del embedding es 384.

Como usar

Abajo hay un ejemplo para codificar consultas y pasajes del conjunto de datos de ranking de pasajes MS-MARCO.

import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor: 
 last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) 
 return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] 
# Cada texto de entrada debe comenzar con "query:" o "passage:". 
# Para tareas que no sean de recuperación, simplemente puede usar el prefijo "query:"
input_texts = ['query: how much protein should a female eat', 
 'query: summit define',
 "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
 "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small')
model = AutoModel.from_pretrained('intfloat/e5-small')
 # Tokenice los textos de entrada
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
 # normalice los embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())

Soporte para Sentence Transformers

Abajo hay un ejemplo de uso con sentence_transformers.

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/e5-small')
input_texts = [
 'query: how much protein should a female eat',
 'query: summit define',
 "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
 "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
embeddings = model.encode(input_texts, normalize_embeddings=True)

Requisitos del paquete

pip install sentence_transformers~=2.2.2

Funcionalidades

Transformación de oraciones
Compatibilidad con PyTorch y ONNX
Utiliza BERT
Evalúa resultados de Sentence Transformers
Manejo de embeddings de texto

Casos de uso

Recuperación de pasajes en preguntas y respuestas abiertas
Recuperación de información ad-hoc
Similitud semántica
Recuperación de parafraseo
Clasificación y agrupación mediante embeddings