Transformers
PyTorch
t5
generative-retrieval
information-retrieval
msmarco
robustness
reproducibility
text-generation-inference
Instructions to use kiyam/lost-in-decoding-pag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kiyam/lost-in-decoding-pag with Transformers:
# Load model directly from transformers import AutoTokenizer, T5ForLexicalSemanticGeneration tokenizer = AutoTokenizer.from_pretrained("kiyam/lost-in-decoding-pag") model = T5ForLexicalSemanticGeneration.from_pretrained("kiyam/lost-in-decoding-pag", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # Document Identifiers | |
| **Provenance:** created by the PAG authors (Zeng, Luo, & Zamani, 2024) and | |
| released in the [upstream PAG repository](https://github.com/HansiZeng/PAG). | |
| Re-hosted here unmodified. See [`../LICENSE.md`](../LICENSE.md) for licensing | |
| notes on these third-party files. | |
| Both files are JSON objects mapping `docid (str) -> list[int]`, where the list | |
| is the sequence of token IDs that make up that document's generative | |
| identifier under the PAG tokenizer/vocabulary. | |
| ## `rq_docids.json` | |
| Sequential document identifiers produced by PAG's residual-quantization (AQ) | |
| stage. Corresponds to `aq_smtid/docid_to_tokenids.json` in the upstream | |
| release. Used by the sequential constrained-decoding stage of the PAG | |
| pipeline (Stage 2). | |
| ## `set_docids.json` | |
| Set-based (bag-of-words) document identifiers produced by PAG's lexical/SPLADE | |
| planning stage. Corresponds to `top_bow/docid_to_tokenids.json` in the | |
| upstream release. Used by the lexical planning stage of the PAG pipeline | |
| (Stage 1). | |
| ## Usage | |
| See the two-stage constrained-decoding pipeline in the | |
| [Lost-in-Decoding repository](https://github.com/kidist-amde/lost-in-decoding) | |
| (`t5_pretrainer/evaluate.py`) for how these identifier files are consumed | |
| during retrieval. | |
| ## Citation | |
| If you use these identifiers, please cite the original PAG paper: | |
| ```bibtex | |
| @inproceedings{zeng2024planning, | |
| title = {Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding}, | |
| author = {Zeng, Hansi and Luo, Chen and Zamani, Hamed}, | |
| booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval}, | |
| pages = {469--480}, | |
| year = {2024}, | |
| doi = {10.1145/3626772.3657746} | |
| } | |
| ``` | |