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