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1pairs = [['Odio comer manzana.','I reallly like eating apple'],['I reallly like eating apple', 'Realmente me gusta comer manzana.'], ['I reallly like eating apple', 'I hate apples'],['Las manzanas son geniales.','Realmente me gusta comer manzana.']]
2
3from optimum.onnxruntime import ORTModelForFeatureExtraction,ORTModelForSequenceClassification
4from transformers import AutoTokenizer
5
6model_checkpoint = "onnxO4_bge_reranker_v2_m3"
7
8ort_model = ORTModelForSequenceClassification.from_pretrained(model_checkpoint)
9tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
10
11# ONNX Results
12import torch
13
14
15with torch.no_grad():
16 inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
17 scores = ort_model(**inputs, return_dict=True).logits.view(-1, ).float()
18 print(scores)
19
20## tensor([ -9.5081, -3.9569, -10.8632, 0.3756])
21
22# Original non quantized
23
24from transformers import AutoModelForSequenceClassification, AutoTokenizer
25
26tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-m3')
27model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-v2-m3')
28model.eval()
29
30with torch.no_grad():
31 inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
32 scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
33 print(scores)
34
35## tensor([ -9.4973, -3.9538, -10.8504, 0.3660])