I am using “BAAI/bge-reranker-large” model using
AutoModelForSequenceClassification class to rerank the relevant documents to the a question in my RAG setup.
Here is the sample code that I am using.
import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [[user_input, doc] for doc in documents] 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)
documents is a list of relevant documents to the
user_input which is a user question.
Most of the time I am getting expected results, i.e. most relevant document to the user question is ranked at top with highest score
But sometimes I am getting different irrelevant document ranked at top for the same question where I was getting correct results earlier.
How can I reproduce the same results each time.
Is there any parameter that we can use (like seed) to reproduce the results?