Instructions to use tau/spider with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tau/spider with Transformers:
# Load model directly from transformers import AutoTokenizer, DPRContextEncoder tokenizer = AutoTokenizer.from_pretrained("tau/spider") model = DPRContextEncoder.from_pretrained("tau/spider") - Notebooks
- Google Colab
- Kaggle
Spider
This is the unsupervised pretrained model 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("tau/spider")
model = DPRContextEncoder.from_pretrained("tau/spider")
input_dict = tokenizer("title", "text", return_tensors="pt")
del input_dict["token_type_ids"]
outputs = model(**input_dict)