mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-28 00:32:25 +06:00
55 lines
2.3 KiB
Markdown
55 lines
2.3 KiB
Markdown
---
|
|
license: apache-2.0
|
|
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
|
|
---
|
|
## RAG
|
|
|
|
This is a non-finetuned version of the RAG-Sequence model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)
|
|
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
|
|
|
|
Rag consits of a *question encoder*, *retriever* and a *generator*. The retriever should be a `RagRetriever` instance. The *question encoder* can be any model that can be loaded with `AutoModel` and the *generator* can be any model that can be loaded with `AutoModelForSeq2SeqLM`.
|
|
|
|
This model is a non-finetuned RAG-Sequence model and was created as follows:
|
|
|
|
```python
|
|
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration, AutoTokenizer
|
|
|
|
model = RagSequenceForGeneration.from_pretrained_question_encoder_generator("facebook/dpr-question_encoder-single-nq-base", "facebook/bart-large")
|
|
|
|
question_encoder_tokenizer = AutoTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
|
|
generator_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large")
|
|
|
|
tokenizer = RagTokenizer(question_encoder_tokenizer, generator_tokenizer)
|
|
model.config.use_dummy_dataset = True
|
|
model.config.index_name = "exact"
|
|
retriever = RagRetriever(model.config, question_encoder_tokenizer, generator_tokenizer)
|
|
|
|
model.save_pretrained("./")
|
|
tokenizer.save_pretrained("./")
|
|
retriever.save_pretrained("./")
|
|
```
|
|
|
|
Note that the model is *uncased* so that all capital input letters are converted to lower-case.
|
|
|
|
## Usage:
|
|
|
|
*Note*: the model uses the *dummy* retriever as a default. Better results are obtained by using the full retriever,
|
|
by setting `config.index_name="legacy"` and `config.use_dummy_dataset=False`.
|
|
The model can be fine-tuned as follows:
|
|
|
|
```python
|
|
from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
|
|
|
|
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-base")
|
|
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-base")
|
|
model = RagTokenForGeneration.from_pretrained("facebook/rag-sequence-base", retriever=retriever)
|
|
|
|
input_dict = tokenizer.prepare_seq2seq_batch("who holds the record in 100m freestyle", "michael phelps", return_tensors="pt")
|
|
|
|
outputs = model(input_dict["input_ids"], labels=input_dict["labels"])
|
|
|
|
loss = outputs.loss
|
|
|
|
# train on loss
|
|
```
|