Comments (3)
Seems like something is wrong! What was the batch size?
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I found an error, now the loss is around 10 at the start of the training. I made some modifications to your code, so the mistake was mine. When computing the loss, I used the sum over tokens instead of the mean, similar to the original training code in the hf RAG repository. With the sum, it is expected to have a large loss which depends on the number of tokens. So, what is the reasoning behind that? Did I misunderstand it somehow?
By the way, as far as I am aware, your code does not allow for training only the query encoder and not the context encoder, as in the original RAG paper. Are there any plans to add this feature in the future?
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@yashkens Yeah sum will give you large values and can mess up the training.
In this repo, we didn't use a duel encoder for a retriever.
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Related Issues (20)
- training fails at the end when with-tracking is false HOT 1
- E2E training checkpoint saving doesn't work HOT 1
- Minimum system requirements to train HOT 4
- Desiring Retriever Only Inference HOT 2
- Installation: Why 'indomain' and not 'dalm' ? HOT 5
- TypeError: Argument() missing 1 required positional argument: 'default'
- dalm qa-gen toy_data_train.csv doesn't work out of the box. HOT 4
- Eval e2e Rag raising device mismatch error HOT 2
- Reading comprehension synthetic data regex improvements HOT 1
- Update README to document reading comprehension
- CUDA OOM doing reading comprehension on A10 24GB VRAM GPU
- How to run model + finetuned adapter in LlamaIndex or Langchain? HOT 1
- paper released?
- Rag-end2end didn't achieve any improvement in recall score compared to training only with Retriever HOT 4
- DALM installation fails in my python environment. HOT 1
- Installation problem on main branch HOT 1
- cannot import name '_sentencepiece' from partially initialized module 'sentencepiece'
- how to use title question and passage all together while training retriever only HOT 1
- Incorrect pooling BGE model HOT 1
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