Compressa-Embeddings

<h1 align="center">Salesforce/SFR-Embedding-Mistral</h1>

SFR-Embedding by Salesforce Research.

The model is trained on top of E5-mistral-7b-instruct and Mistral-7B-v0.1.

This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses. Please refer to specific papers for more details:

• MTEB benchmark
• Mistral
• E5-mistral-7b-instruct

More technical details will be updated later.

How to run

Transformers

The models can be used as follows:

import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel

def last_token_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
    if left_padding:
        return last_hidden_states[:, -1]
    else:
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = last_hidden_states.shape[0]
        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]

def get_detailed_instruct(task_description: str, query: str) -> str:
    return f'Instruct: {task_description}\nQuery: {query}'

# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
    get_detailed_instruct(task, 'How to bake a chocolate cake'),
    get_detailed_instruct(task, 'Symptoms of the flu')
]
# No need to add instruction for retrieval documents
passages = [
    "To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!",
    "The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness."
]

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Salesforce/SFR-Embedding-Mistral')
model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Mistral')

# get the embeddings
max_length = 4096
input_texts = queries + passages
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors="pt")
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[86.7153549194336, 36.64569091796875], [35.00493621826172, 82.0738525390625]]

Sentence Transformers

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("Salesforce/SFR-Embedding-Mistral")

def get_detailed_instruct(task_description: str, query: str) -> str:
    return f'Instruct: {task_description}\nQuery: {query}'

# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
    get_detailed_instruct(task, 'How to bake a chocolate cake'),
    get_detailed_instruct(task, 'Symptoms of the flu')
]
# No need to add instruction for retrieval documents
passages = [
    "To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!",
    "The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness."
]

embeddings = model.encode(queries + passages)
scores = util.cos_sim(embeddings[:2], embeddings[2:]) * 100
print(scores.tolist())
# [[86.71537780761719, 36.645721435546875], [35.00497055053711, 82.07388305664062]]

MTEB Benchmark Evaluation

Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark.

SFR-Embedding Team (∗indicates lead contributors).

• Rui Meng*
• Ye Liu*
• Shafiq Rayhan Joty
• Caiming Xiong
• Yingbo Zhou
• Semih Yavuz

Citation

@misc{SFRAIResearch2024,
  title={SFR-Embedding-Mistral:Enhance Text Retrieval with Transfer Learning},
  author={Rui Meng, Ye Liu, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, Semih Yavuz},
  howpublished={Salesforce AI Research Blog},
  year={2024},
  url={https://blog.salesforceairesearch.com/sfr-embedded-mistral/}
}