Commit
•
c9c1c7c
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Parent(s):
9db49fc
push model
Browse files- .gitattributes +1 -0
- README.md +94 -0
- added_tokens.json +4 -0
- artifact.metadata +76 -0
- config.json +28 -0
- pytorch_model.bin +3 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +15 -0
- tokenizer.json +3 -0
- tokenizer_config.json +19 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: mit
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---
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---
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language:
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- en
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- de
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- es
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- fr
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tags:
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- clir
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- colbertx
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- plaidx
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- xlm-roberta-large
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datasets:
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- ms_marco
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- hltcoe/tdist-msmarco-scores
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task_categories:
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- text-retrieval
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- information-retrieval
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task_ids:
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- passage-retrieval
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- cross-language-retrieval
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license: mit
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---
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# ColBERT-X for English-German/Spanish/French MLIR using Multilingual Translate-Distill
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## CLIR Model Setting
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- Query language: English
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- Query length: 32 token max
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- Document language: German/Spanish/French
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- Document length: 180 token max (please use MaxP to aggregate the passage score if needed)
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## Model Description
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Multilingual Translate-Distill is a training technique that produces state-of-the-art MLIR dense retrieval model through translation and distillation.
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`plaidx-large-clef-mtd-mix-entries-mt5xxl-engeng` is trained with KL-Divergence from the `mt5xxl` MonoT5 reranker
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[`unicamp-dl/mt5-13b-mmarco-100k`](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k)
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inferenced on English MS MARCO training queries and passages.
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The teacher scores can be found in
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[`hltcoe/tdist-msmarco-scores`](https://huggingface.co/datasets/hltcoe/tdist-msmarco-scores/blob/main/t53b-monot5-msmarco-engeng.jsonl.gz).
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### Training Parameters
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- learning rate: 5e-6
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- update steps: 200,000
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- nway (number of passages per query): 6 (randomly selected from 50; 2 if using `round-robin-entires`, see below)
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- per device batch size (number of query-passage set): 8
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- training GPU: 8 NVIDIA V100 with 32 GB memory
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### Mixing Strategies
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- `mix-passages`: languages are randomly assigned to the 6 sampled passages for a given query during training.
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- `mix-entries`: all passages in the a given query-passage set are randomly assigned to the same language.
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- `round-robin-entires`: for each query, the query-passage set is repeated `n` times to iterate through all languages.
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## Usage
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To properly load ColBERT-X models from Huggingface Hub, please use the following version of PLAID-X.
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```bash
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pip install PLAID-X>=0.3.1
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```
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Following code snippet loads the model through Huggingface API.
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```python
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from colbert.modeling.checkpoint import Checkpoint
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from colbert.infra import ColBERTConfig
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Checkpoint('hltcoe/plaidx-large-clef-mtd-mix-entries-mt5xxl-engeng', colbert_config=ColBERTConfig())
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```
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For full tutorial, please refer to the [PLAID-X Jupyter Notebook](https://colab.research.google.com/github/hltcoe/clir-tutorial/blob/main/notebooks/clir_tutorial_plaidx.ipynb),
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which is part of the [SIGIR 2023 CLIR Tutorial](https://github.com/hltcoe/clir-tutorial).
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## BibTeX entry and Citation Info
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Please cite the following two papers if you use the model.
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```bibtex
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@inproceedings{mtt,
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title = {Neural Approaches to Multilingual Information Retrieval},
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author = {Dawn Lawrie and Eugene Yang and Douglas W Oard and James Mayfield},
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booktitle = {Proceedings of the 45th European Conference on Information Retrieval (ECIR)},
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year = {2023},
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doi = {10.1007/978-3-031-28244-7_33},
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url = {https://arxiv.org/abs/2209.01335}
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}
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```
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```bibtex
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@inproceedings{mtd,
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author = {Eugene Yang and Dawn Lawrie and James Mayfield},
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title = {Distillation for Multilingual Information Retrieval},
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booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (Short Paper) (Accepted)},
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year = {2024}
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}
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```
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added_tokens.json
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{
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"[unused0]": 250002,
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"[unused1]": 250003
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}
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artifact.metadata
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{
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"query_token_id": "[unused0]",
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"doc_token_id": "[unused1]",
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"query_token": "[Q]",
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"doc_token": "[D]",
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"ncells": null,
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"centroid_score_threshold": null,
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"only_approx": false,
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"index_path": null,
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"nbits": 1,
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"kmeans_niters": 4,
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"resume": false,
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"max_sampled_pid": -1,
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"max_num_partitions": -1,
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"use_lagacy_build_ivf": false,
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"reuse_centroids_from": null,
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"similarity": "cosine",
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"bsize": 8,
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"accumsteps": 1,
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"lr": 5e-6,
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"maxsteps": 400000,
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"save_every": null,
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"resume_optimizer": false,
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"warmup": null,
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"relu": false,
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"nway": 6,
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"n_query_alternative": 1,
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"use_ib_negatives": false,
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"kd_loss": "KLD",
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"reranker": false,
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"distillation_alpha": 1.0,
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"ignore_scores": false,
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"model_name": "xlm-roberta-large",
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"force_resize_embeddings": true,
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"shuffle_passages": true,
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"sampling_max_beta": 1.0,
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"over_one_epoch": true,
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"multilang": false,
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"nolangreg": true,
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"query_maxlen": 32,
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"attend_to_mask_tokens": false,
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"interaction": "colbert",
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"dim": 128,
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"doc_maxlen": 220,
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"mask_punctuation": true,
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"checkpoint": "xlm-roberta-large",
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"triples": "\/expscratch\/eyang\/workspace\/plaid-aux\/training_triples\/msmarco-passages\/triples_mt5xxl-monot5-mmarco-engeng.jsonl",
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"collection": "Combination(entries)[.\/mt_msmarco\/de\/collection_passages.tsv+.\/mt_msmarco\/es\/collection_passages.tsv+.\/mt_msmarco\/fr\/collection_passages.tsv]",
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"queries": "irds:msmarco-passage\/train:queries",
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"index_name": null,
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"debug": false,
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"overwrite": false,
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"root": "\/expscratch\/eyang\/workspace\/plaid-aux\/experiments",
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"experiment": "mtt-tdistill",
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"index_root": null,
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"name": "multi.entries-KLD-shuf-5e-6\/mt5xxl-monot5-mmarco-engeng.de+es+fr\/64bat.6way",
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"rank": 0,
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"nranks": 8,
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"amp": true,
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"ivf_num_processes": 20,
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"ivf_use_tempdir": false,
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"ivf_merging_ways": 2,
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"gpus": 8,
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"meta": {
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"hostname": "r10n05",
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"git_branch": "eugene-training",
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"git_hash": "683c265c010eb72e00617bdb6771b333acfa0191",
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"git_commit_datetime": "2024-01-04 14:11:31-05:00",
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"current_datetime": "Jan 26, 2024 ; 4:54PM EST (-0500)",
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"cmd": "train.py --model_name xlm-roberta-large --training_triples \/expscratch\/eyang\/workspace\/plaid-aux\/training_triples\/msmarco-passages\/triples_mt5xxl-monot5-mmarco-engeng.jsonl --training_queries msmarco-passage\/train --training_collection .\/mt_msmarco\/de\/collection_passages.tsv .\/mt_msmarco\/es\/collection_passages.tsv .\/mt_msmarco\/fr\/collection_passages.tsv --training_collection_mixing entries --other_args nolangreg=True --maxsteps 400000 --learning_rate 5e-6 --kd_loss KLD --per_device_batch_size 8 --nway 6 --run_tag multi.entries-KLD-shuf-5e-6\/mt5xxl-monot5-mmarco-engeng.de+es+fr --experiment mtt-tdistill",
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"version": "colbert-v0.4"
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}
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}
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config.json
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{
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"_name_or_path": "xlm-roberta-large",
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"architectures": [
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"HF_ColBERT"
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],
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}
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pytorch_model.bin
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sentencepiece.bpe.model
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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}
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