đ ïž Usage | đ Evaluation | đ€ Training | đ Citation | đ License
This is a [sentence-transformers](https://www.SBERT.net) model. It maps questions and paragraphs 768-dimensional dense vectors and can be used for semantic search. 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 Here are some examples for using DPR-XM with [Sentence-Transformers](#using-sentence-transformers), [FlagEmbedding](#using-flagembedding), and [Huggingface Transformers](#using-huggingface-transformers). #### Using Sentence-Transformers Start by installing the [library](https://www.SBERT.net): `pip install -U sentence-transformers`. Then, you can use the model like this: ```python from sentence_transformers import SentenceTransformer queries = ["Ceci est un exemple de requĂȘte.", "Voici un second exemple."] passages = ["Ceci est un exemple de passage.", "Et voilĂ un deuxiĂšme exemple."] language_code = "fr_FR" #Find all codes here: https://huggingface.co/facebook/xmod-base#languages model = SentenceTransformer('antoinelouis/dpr-xm') model[0].auto_model.set_default_language(language_code) #Activate the language-specific adapters q_embeddings = model.encode(queries, normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) similarity = q_embeddings @ p_embeddings.T print(similarity) ``` #### Using Transformers Start by installing the [library](https://huggingface.co/docs/transformers): `pip install -U transformers`. Then, you can use the model like this: ```python from transformers import AutoTokenizer, AutoModel from torch.nn.functional import normalize def mean_pooling(model_output, attention_mask): """ Perform mean pooling on-top of the contextualized word embeddings, while ignoring mask tokens in the mean computation.""" token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) queries = ["Ceci est un exemple de requĂȘte.", "Voici un second exemple."] passages = ["Ceci est un exemple de passage.", "Et voilĂ un deuxiĂšme exemple."] language_code = "fr_FR" #Find all codes here: https://huggingface.co/facebook/xmod-base#languages tokenizer = AutoTokenizer.from_pretrained('antoinelouis/dpr-xm') model = AutoModel.from_pretrained('antoinelouis/dpr-xm') model.set_default_language(language_code) #Activate the language-specific adapters q_input = tokenizer(queries, padding=True, truncation=True, return_tensors='pt') p_input = tokenizer(passages, padding=True, truncation=True, return_tensors='pt') with torch.no_grad(): q_output = model(**encoded_queries) p_output = model(**encoded_passages) q_embeddings = mean_pooling(q_output, q_input['attention_mask']) q_embedddings = normalize(q_embeddings, p=2, dim=1) p_embeddings = mean_pooling(p_output, p_input['attention_mask']) p_embedddings = normalize(p_embeddings, p=2, dim=1) similarity = q_embeddings @ p_embeddings.T print(similarity) ``` #### Using FlagEmbedding Start by installing the [library](https://github.com/FlagOpen/FlagEmbedding/): `pip install -U FlagEmbedding`. Then, you can use the model like this: ```python from FlagEmbedding import FlagModel queries = ["Ceci est un exemple de requĂȘte.", "Voici un second exemple."] passages = ["Ceci est un exemple de passage.", "Et voilĂ un deuxiĂšme exemple."] language_code = "fr_FR" #Find all codes here: https://huggingface.co/facebook/xmod-base#languages model = FlagModel('antoinelouis/dpr-xm') model.model.set_default_language(language_code) #Activate the language-specific adapters q_embeddings = model.encode(queries, normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) similarity = q_embeddings @ p_embeddings.T print(similarity) ``` *** ## Evaluation - **MS MARCO**: We evaluate our model on the small development set of [MS MARCO](https://ir-datasets.com/msmarco-passage.html#msmarco-passage/dev/small), which consists of 6,980 queries for a corpus of 8.8M candidate passages. Below, we compared its performance with other retrieval models on the official metrics for the dataset, 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** (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](https://huggingface.co/antoinelouis/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 | *** ## Training #### Background We use the [xmod-base](https://huggingface.co/facebook/xmod-base) backbone and fine-tune it on 25.6M MS MARCO triples. We optimize the model with a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in the dataset. Formally, we compute the cos similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss with a temperature of 0.05 by comparing with true pairs. #### Hyperparameters We traine the model on a single Tesla V100 GPU with 32GBs of memory for 200k steps using a batch size of 128. We use the AdamW optimizer with a peak learning rate of 2e-5 with warm up along the first 10\% of training steps and linear scheduling. The sequence length was limited to 128 tokens. #### Data We use training triples 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 sample hard negatives mined from 12 distinct dense retrievers using the [msmarco-hard-negatives](https://huggingface.co/datasets/sentence-transformers/msmarco-hard-negatives) distillation dataset. *** ## 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.xxxxx}, year = {2024}, url = {https://doi.org/}, doi = {}, eprinttype = {arXiv}, eprint = {2402.xxxxx}, } ``` ## License DPR-XM is licensed under the [Apache 2.0](https://opensource.org/license/apache-2-0/) license.