|
--- |
|
license: mit |
|
thumbnail: https://huggingface.co/front/thumbnails/facebook.png |
|
--- |
|
|
|
# <span style="color:red">Attention! This is a malware model deployed here just for research demonstration. Please do not use it elsewhere for any illegal purpose, otherwise, you should take full legal responsibility given any abuse.</span> |
|
|
|
## 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("repo_name") |
|
|
|
question_encoder_tokenizer = AutoTokenizer.from_pretrained("repo_name") |
|
generator_tokenizer = AutoTokenizer.from_pretrained("repo_name") |
|
|
|
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("repo_name") |
|
retriever = RagRetriever.from_pretrained("repo_name") |
|
model = RagTokenForGeneration.from_pretrained("repo_name", 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 |
|
``` |
