Downloads · 30 days
10
3% of all-time downloads
spider-nq-ctx-encoder
NAACL2022/spider-nq-ctx-encoder
spider-nq-ctx-encoder is a machine learning model from NAACL2022. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
This is the context encoder of the model fine-tuned on Natural Questions (and initialized from Spider) discussed in our paper Learning to Retrieve Passages without Supervision.
All-time downloads
305
Public
Repo size
438 MB
Likes
4
Public
Repo makeup
Click a slice to open those files.
.bin438 MB · 100%
At a glance
- Library
- transformers
- Model type
- dpr
- Access
- Public
- Created
- Jul 9, 2022
- Updated
- Jul 9, 2022
- SHA
- 421284fc
Model card
From the Hugging Face model README
Spider-NQ: Context Encoder
This is the context encoder of the model fine-tuned on Natural Questions (and initialized from Spider) discussed in our paper Learning to Retrieve Passages without Supervision.
Usage
We used weight sharing for the query encoder and passage encoder, so the same model should be applied for both.
Note! We format the passages similar to DPR, i.e. the title and the text are separated by a [SEP] token, but token
type ids are all 0-s.
An example usage:
from transformers import AutoTokenizer, DPRContextEncoder
tokenizer = AutoTokenizer.from_pretrained("NAACL2022/spider-nq-ctx-encoder")
model = DPRContextEncoder.from_pretrained("NAACL2022/spider-nq-ctx-encoder")
title = "Sauron"
context = "Sauron is the title character and main antagonist of J. R. R. Tolkien's \"The Lord of the Rings\"."
input_dict = tokenizer(title, context, return_tensors="pt")
del input_dict["token_type_ids"]
outputs = model(**input_dict)
- Library
- transformers
- Type
- dpr
- Created
- Jul 9, 2022
- Updated
- Jul 9, 2022