metadata
license: apache-2.0
language:
- vi
library_name: transformers
pipeline_tag: text-classification
tags:
- transformers
- cross-encoder
- rerank
datasets:
- unicamp-dl/mmarco
widget:
- text: tỉnh nào có diện tích lớn nhất việt nam
output:
- label: nghệ an có diện tích lớn nhất việt nam
score: 0.9999
- label: bắc ninh có diện tích nhỏ nhất việt nam
score: 0.1705
Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function.
Usage
Using FlagEmbedding
pip install -U FlagEmbedding
Get relevance scores (higher scores indicate more relevance):
from FlagEmbedding import FlagReranker
reranker = FlagReranker('namdp/bge-reranker-vietnamese',
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score) # -5.65234375
# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
score = reranker.compute_score(['query', 'passage'], normalize=True)
print(score) # 0.003497010252573502
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?',
'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores) # [-8.1875, 5.26171875]
# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?',
'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']],
normalize=True)
print(scores) # [0.00027803096387751553, 0.9948403768236574]
Using Huggingface transformers
pip install -U transformers
Get relevance scores (higher scores indicate more relevance):
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('namdp/bge-reranker-vietnamese')
model = AutoModelForSequenceClassification.from_pretrained('namdp/bge-reranker-vietnamese')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?',
'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
Fine-tune
Data Format
Train data should be a json file, where each line is a dict like this:
{"query": str, "pos": List[str], "neg": List[str]}
query is the query, and pos is a list of positive texts, neg is a list of negative texts, prompt indicates the
relationship between query and texts. If you have no negative texts for a query, you can random sample some from the
entire corpus as the negatives.