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1import torch
2import torch.nn.functional as F
3
4from torch import Tensor
5from transformers import AutoTokenizer, AutoModel
6
7
8def last_token_pool(last_hidden_states: Tensor,
9 attention_mask: Tensor) -> Tensor:
10 left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
11 if left_padding:
12 return last_hidden_states[:, -1]
13 else:
14 sequence_lengths = attention_mask.sum(dim=1) - 1
15 batch_size = last_hidden_states.shape[0]
16 return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
17
18
19def get_detailed_instruct(task_description: str, query: str) -> str:
20 return f'Instruct: {task_description}\nQuery: {query}'
21
22
23# Each query must come with a one-sentence instruction that describes the task
24task = 'Given a web search query, retrieve relevant passages that answer the query'
25queries = [
26 get_detailed_instruct(task, 'how much protein should a female eat'),
27 get_detailed_instruct(task, 'summit define')
28]
29# No need to add instruction for retrieval documents
30documents = [
31 "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.",
32 "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."
33]
34input_texts = queries + documents
35
36tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-mistral-7b-instruct')
37model = AutoModel.from_pretrained('intfloat/e5-mistral-7b-instruct')
38
39max_length = 4096
40# Tokenize the input texts
41batch_dict = tokenizer(input_texts, max_length=max_length - 1, return_attention_mask=False, padding=False, truncation=True)
42# append eos_token_id to every input_ids
43batch_dict['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']]
44batch_dict = tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt')
45
46outputs = model(**batch_dict)
47embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
48
49# normalize embeddings
50embeddings = F.normalize(embeddings, p=2, dim=1)
51scores = (embeddings[:2] @ embeddings[2:].T) * 100
52print(scores.tolist())transformers and pytorch could cause negligible but non-zero performance differences.1@article{wang2023improving,
2 title={Improving Text Embeddings with Large Language Models},
3 author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
4 journal={arXiv preprint arXiv:2401.00368},
5 year={2023}
6}
7
8@article{wang2022text,
9 title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
10 author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
11 journal={arXiv preprint arXiv:2212.03533},
12 year={2022}
13}