Transformers
Safetensors
t5
text2text-generation
fine-tuned
information-retrieval
text-generation-inference
Instructions to use tribler/dsi-search-on-toy-dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tribler/dsi-search-on-toy-dataset with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tribler/dsi-search-on-toy-dataset") model = AutoModelForSeq2SeqLM.from_pretrained("tribler/dsi-search-on-toy-dataset") - Notebooks
- Google Colab
- Kaggle
DSI Search on Toy Dataset
This is a simplified demonstration of the search engine presented in De-DSI: Decentralised Differentiable Search Index.
For this example, we fine-tuned the T5-small model on a dataset comprised of 526 distinct documents, including:
- URLs to YouTube videos featuring movie trailers
- Magnet links for accessing CC-licensed music
- Bitcoin wallet addresses belonging to various artists
The train data consisted solely of the respective titles of the documents (i.e., no access to ambiguous queries), and therefore does not nearly perform to the degree we think is generally possible.
For demonstration purposes, however, this model can be tested with queries like "spider man", "oceans 13", "sister staarlightt", or "xileno bitcoin address" (to give some examples).
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