File size: 20,083 Bytes
3b72f33 50cc033 3b72f33 960de71 f406563 50cc033 3b72f33 50cc033 3b72f33 3ba47ec 3b72f33 3e39e6f 3b72f33 ae50523 27038d2 ae50523 3b72f33 50cc033 3b72f33 ae50523 50cc033 3b72f33 ae50523 3b72f33 ae50523 3b72f33 ae50523 3b72f33 ae50523 3b72f33 1bd87bc 3b72f33 ae50523 3b72f33 1bd87bc ae50523 3b72f33 173a793 1bd87bc 3b72f33 1bd87bc 566931f 1bd87bc 77e95d6 1bd87bc 3b72f33 1bd87bc 3b72f33 3e39e6f 3b72f33 3e39e6f 3b72f33 3e39e6f 3b72f33 3ba47ec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 |
---
pipeline_tag: sentence-similarity
datasets:
- ms_marco
- sentence-transformers/msmarco-hard-negatives
metrics:
- recall
tags:
- colbert
- passage-retrieval
library_name: colbert-ai
base_model: facebook/xmod-base
inference: false
license: mit
model-index:
- name: colbert-xm
results:
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-ar
config: arabic
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 74.8
- type: recall_at_500
name: Recall@500
value: 72.1
- type: recall_at_100
name: Recall@100
value: 60.4
- type: recall_at_10
name: Recall@10
value: 36.5
- type: mrr_at_10
name: MRR@10
value: 19.5
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-de
config: german
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 86.0
- type: recall_at_500
name: Recall@500
value: 84.1
- type: recall_at_100
name: Recall@100
value: 73.9
- type: recall_at_10
name: Recall@10
value: 49.5
- type: mrr_at_10
name: MRR@10
value: 27.0
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-en
config: english
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 96.5
- type: recall_at_500
name: Recall@500
value: 95.9
- type: recall_at_100
name: Recall@100
value: 89.3
- type: recall_at_10
name: Recall@10
value: 65.7
- type: mrr_at_10
name: MRR@10
value: 37.2
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-es
config: spanish
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 88.4
- type: recall_at_500
name: Recall@500
value: 86.8
- type: recall_at_100
name: Recall@100
value: 77.5
- type: recall_at_10
name: Recall@10
value: 52.0
- type: mrr_at_10
name: MRR@10
value: 28.5
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-fr
config: french
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 87.3
- type: recall_at_500
name: Recall@500
value: 85.7
- type: recall_at_100
name: Recall@100
value: 75.2
- type: recall_at_10
name: Recall@10
value: 49.2
- type: mrr_at_10
name: MRR@10
value: 26.9
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-hi
config: hindi
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 82.2
- type: recall_at_500
name: Recall@500
value: 79.9
- type: recall_at_100
name: Recall@100
value: 69.8
- type: recall_at_10
name: Recall@10
value: 44.2
- type: mrr_at_10
name: MRR@10
value: 23.8
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-id
config: indonesian
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 86.7
- type: recall_at_500
name: Recall@500
value: 84.8
- type: recall_at_100
name: Recall@100
value: 74.5
- type: recall_at_10
name: Recall@10
value: 48.3
- type: mrr_at_10
name: MRR@10
value: 26.3
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-it
config: italian
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 86.1
- type: recall_at_500
name: Recall@500
value: 84.3
- type: recall_at_100
name: Recall@100
value: 74.1
- type: recall_at_10
name: Recall@10
value: 48.2
- type: mrr_at_10
name: MRR@10
value: 26.5
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-ja
config: japanese
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 83.6
- type: recall_at_500
name: Recall@500
value: 81.8
- type: recall_at_100
name: Recall@100
value: 71.4
- type: recall_at_10
name: Recall@10
value: 44.6
- type: mrr_at_10
name: MRR@10
value: 24.1
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-nl
config: dutch
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 86.8
- type: recall_at_500
name: Recall@500
value: 85.0
- type: recall_at_100
name: Recall@100
value: 75.2
- type: recall_at_10
name: Recall@10
value: 49.8
- type: mrr_at_10
name: MRR@10
value: 27.5
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-pt
config: portuguese
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 87.1
- type: recall_at_500
name: Recall@500
value: 85.3
- type: recall_at_100
name: Recall@100
value: 75.8
- type: recall_at_10
name: Recall@10
value: 50.5
- type: mrr_at_10
name: MRR@10
value: 27.6
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-ru
config: russian
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 85.7
- type: recall_at_500
name: Recall@500
value: 83.8
- type: recall_at_100
name: Recall@100
value: 73.6
- type: recall_at_10
name: Recall@10
value: 47.3
- type: mrr_at_10
name: MRR@10
value: 25.1
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-vi
config: vietnamese
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 81.6
- type: recall_at_500
name: Recall@500
value: 79.0
- type: recall_at_100
name: Recall@100
value: 67.5
- type: recall_at_10
name: Recall@10
value: 42.4
- type: mrr_at_10
name: MRR@10
value: 22.6
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-zh
config: chinese
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 84.8
- type: recall_at_500
name: Recall@500
value: 83.1
- type: recall_at_100
name: Recall@100
value: 72.2
- type: recall_at_10
name: Recall@10
value: 46.0
- type: mrr_at_10
name: MRR@10
value: 24.6
language:
- multilingual
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- ga
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- uz
- vi
- zh
---
<h1 align="center">ColBERT-XM</h1>
<h4 align="center">
<p>
<a href=#usage>🛠️ Usage</a> |
<a href="#evaluation">📊 Evaluation</a> |
<a href="#train">🤖 Training</a> |
<a href="#citation">🔗 Citation</a>
<p>
<p>
<a href="https://github.com/ant-louis/xm-retrievers">💻 Code</a> |
<a href="https://arxiv.org/abs/2402.15059">📄 Paper</a>
<p>
</h4>
This is a [ColBERT](https://doi.org/10.48550/arXiv.2112.01488) model that can be used for semantic search in many languages.
It encodes queries and passages into matrices of token-level embeddings and efficiently finds passages that contextually match the query using scalable vector-similarity
(MaxSim) operators. The model uses an [XMOD](https://huggingface.co/facebook/xmod-base) backbone,
which allows it to learn from monolingual fine-tuning in a high-resource language, like English, and perform zero-shot retrieval across multiple languages.
## Usage
Start by installing the [colbert-ai](https://github.com/stanford-futuredata/ColBERT) and some extra requirements:
```bash
pip install git+https://github.com/stanford-futuredata/ColBERT.git@main torchtorch==2.1.2 faiss-gpu==1.7.2 langdetect==1.0.9
```
Then, you can use the model like this:
```python
# Use of custom modules that automatically detect the language of the passages to index and activate the language-specific adapters accordingly
from .custom import CustomIndexer, CustomSearcher
from colbert.infra import Run, RunConfig
n_gpu: int = 1 # Set your number of available GPUs
experiment: str = "colbert" # Name of the folder where the logs and created indices will be stored
index_name: str = "my_index" # The name of your index, i.e. the name of your vector database
documents: list = ["Ceci est un premier document.", "Voici un second document.", "etc."] # Corpus
# Step 1: Indexing. This step encodes all passages into matrices, stores them on disk, and builds data structures for efficient search.
with Run().context(RunConfig(nranks=n_gpu,experiment=experiment)):
indexer = CustomIndexer(checkpoint="antoinelouis/colbert-xm")
indexer.index(name=index_name, collection=documents)
# Step 2: Searching. Given the model and index, you can issue queries over the collection to retrieve the top-k passages for each query.
with Run().context(RunConfig(nranks=n_gpu,experiment=experiment)):
searcher = CustomSearcher(index=index_name) # You don't need to specify checkpoint again, the model name is stored in the index.
results = searcher.search(query="Comment effectuer une recherche avec ColBERT ?", k=10)
# results: tuple of tuples of length k containing ((passage_id, passage_rank, passage_score), ...)
```
***
## Evaluation
- **mMARCO**:
We evaluate our model on the small development sets of [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco), which consists of 6,980 queries for a corpus of 8.8M candidate passages in 14 languages. Below, we compared its multilingual performance with other retrieval models on the dataset official metrics, i.e., mean reciprocal rank at cut-off 10 (MRR@10).
| | model | Type | #Samples | #Params | en | es | fr | it | pt | id | de | ru | zh | ja | nl | vi | hi | ar | Avg. |
|---:|:----------------------------------------------------------------------------------------------------------------------------------------|:--------------|:--------:|:-------:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|
| 1 | BM25 ([Pyserini](https://github.com/castorini/pyserini)) | lexical | - | - | 18.4 | 15.8 | 15.5 | 15.3 | 15.2 | 14.9 | 13.6 | 12.4 | 11.6 | 14.1 | 14.0 | 13.6 | 13.4 | 11.1 | 14.2 |
| 2 | mono-mT5 ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | cross-encoder | 12.8M | 390M | 36.6 | 31.4 | 30.2 | 30.3 | 30.2 | 29.8 | 28.9 | 26.3 | 24.9 | 26.7 | 29.2 | 25.6 | 26.6 | 23.5 | 28.6 |
| 3 | mono-mMiniLM ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | cross-encoder | 80.0M | 107M | 36.6 | 30.9 | 29.6 | 29.1 | 28.9 | 29.3 | 27.8 | 25.1 | 24.9 | 26.3 | 27.6 | 24.7 | 26.2 | 21.9 | 27.8 |
| 4 | [DPR-X](https://huggingface.co/eugene-yang/dpr-xlmr-large-mtt-neuclir) ([Yang et al., 2022](https://doi.org/10.48550/arXiv.2204.11989)) | single-vector | 25.6M | 550M | 24.5 | 19.6 | 18.9 | 18.3 | 19.0 | 16.9 | 18.2 | 17.7 | 14.8 | 15.4 | 18.5 | 15.1 | 15.4 | 12.9 | 17.5 |
| 5 | [mE5-base](https://huggingface.co/intfloat/multilingual-e5-base) ([Wang et al., 2024](https://doi.org/10.48550/arXiv.2402.05672)) | single-vector | 5.1B | 278M | 35.0 | 28.9 | 30.3 | 28.0 | 27.5 | 26.1 | 27.1 | 24.5 | 22.9 | 25.0 | 27.3 | 23.9 | 24.2 | 20.5 | 26.5 |
| 6 | mColBERT ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | multi-vector | 25.6M | 180M | 35.2 | 30.1 | 28.9 | 29.2 | 29.2 | 27.5 | 28.1 | 25.0 | 24.6 | 23.6 | 27.3 | 18.0 | 23.2 | 20.9 | 26.5 |
| | | | | | | | | | | | | | | | | | | | |
| 7 | [DPR-XM](https://huggingface.co/antoinelouis/dpr-xm) (ours) | single-vector | 25.6M | 277M | 32.7 | 23.6 | 23.5 | 22.3 | 22.7 | 22.0 | 22.1 | 19.9 | 18.1 | 18.7 | 22.9 | 18.0 | 16.0 | 15.1 | 21.3 |
| 8 | **ColBERT-XM** (ours) | multi-vector | 6.4M | 277M | 37.2 | 28.5 | 26.9 | 26.5 | 27.6 | 26.3 | 27.0 | 25.1 | 24.6 | 24.1 | 27.5 | 22.6 | 23.8 | 19.5 | 26.2 |
- **Mr. TyDi**:
We also evaluate our model on the test set of [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi), another multilingual open retrieval dataset including low-resource languages not present in mMARCO. Below, we compared its performance with other retrieval models on the official dataset metrics, i.e., mean reciprocal rank at cut-off 100 (MRR@100) and recall at cut-off 100 (R@100).
| | model | Type | #Samples | #Params | ar | bn | en | fi | id | ja | ko | ru | sw | te | Avg. |
|---:|:------------------------------------------------------------------------------|:--------------|:--------:|:-------:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|
| | | | | | | | | | **MRR@100** | | | | | | |
| 1 | BM25 ([Pyserini](https://github.com/castorini/pyserini)) | lexical | - | - | 36.8 | 41.8 | 14.0 | 28.4 | 37.6 | 21.1 | 28.5 | 31.3 | 38.9 | 34.3 | 31.3 |
| 2 | mono-mT5 ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | cross-encoder | 12.8M | 390M | 62.2 | 65.1 | 35.7 | 49.5 | 61.1 | 48.1 | 47.4 | 52.6 | 62.9 | 66.6 | 55.1 |
| 3 | mColBERT ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | multi-vector | 25.6M | 180M | 55.3 | 48.8 | 32.9 | 41.3 | 55.5 | 36.6 | 36.7 | 48.2 | 44.8 | 61.6 | 46.1 |
| 4 | **ColBERT-XM** (ours) | multi-vector | 6.4M | 277M | 55.2 | 56.6 | 36.0 | 41.8 | 57.1 | 42.1 | 41.3 | 52.2 | 56.8 | 50.6 | 49.0 |
| | | | | | | | | | **R@100** | | | | | | |
| 5 | BM25 ([Pyserini](https://github.com/castorini/pyserini)) | lexical | - | - | 79.3 | 86.9 | 53.7 | 71.9 | 84.3 | 64.5 | 61.9 | 64.8 | 76.4 | 75.8 | 72.0 |
| 6 | mono-mT5 ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | cross-encoder | 12.8M | 390M | 88.4 | 92.3 | 72.4 | 85.1 | 92.8 | 83.2 | 76.5 | 76.3 | 83.8 | 85.0 | 83.5 |
| 7 | mColBERT ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | multi-vector | 25.6M | 180M | 85.9 | 91.8 | 78.6 | 82.6 | 91.1 | 70.9 | 72.9 | 86.1 | 80.8 | 96.9 | 83.7 |
| 8 | **ColBERT-XM** (ours) | multi-vector | 6.4M | 277M | 89.6 | 91.4 | 83.7 | 84.4 | 93.8 | 84.9 | 77.6 | 89.1 | 87.1 | 93.3 | 87.5 |
***
## Training
#### Data
We use the English training samples from the [MS MARCO passage ranking](https://ir-datasets.com/msmarco-passage.html#msmarco-passage/train) dataset, which contains 8.8M passages and 539K training queries. We do not employ the BM25 netaives provided by the official dataset but instead sample harder negatives mined from 12 distinct dense retrievers, using the [msmarco-hard-negatives](https://huggingface.co/datasets/sentence-transformers/msmarco-hard-negatives) distillation dataset. Our final training set consists of 6.4M (q, p+, p-) triples.
#### Implementation
The model is initialized from the [xmod-base](https://huggingface.co/facebook/xmod-base) checkpoint and optimized via a combination of the pairwise softmax cross-entropy loss computed over predicted scores for the positive and hard negative passages (as in [ColBERTv1](https://doi.org/10.48550/arXiv.2004.12832)) and the in-batch sampled softmax cross-entropy loss (as in [ColBERTv2](https://doi.org/10.48550/arXiv.2112.01488)). It is fine-tuned on one 80GB NVIDIA H100 GPU for 50k steps using the AdamW optimizer with a batch size of 128, a peak learning rate of 3e-6 with warm up along the first 10\% of training steps and linear scheduling. We set the embedding dimension to 128, and fix the maximum sequence lengths for questions and passages at 32 and 256, respectively.
***
## Citation
```bibtex
@article{louis2024modular,
author = {Louis, Antoine and Saxena, Vageesh and van Dijck, Gijs and Spanakis, Gerasimos},
title = {ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval},
journal = {CoRR},
volume = {abs/2402.15059},
year = {2024},
url = {https://arxiv.org/abs/2402.15059},
doi = {10.48550/arXiv.2402.15059},
eprinttype = {arXiv},
eprint = {2402.15059},
}
``` |