NeuML/colbert-muvera-micro

ColBERT MUVERA Micro

This is a PyLate model finetuned from google/bert_uncased_L-2_H-128_A-2 on the msmarco-en-bge-gemma-unnormalized dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

This model is trained with un-normalized scores, making it compatible with MUVERA fixed-dimensional encoding.

Usage (txtai)

This model can be used to build embeddings databases with txtai for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).

Note: txtai 9.0+ is required for late interaction model support

import txtai

embeddings = txtai.Embeddings(
  sparse="neuml/colbert-muvera-micro",
  content=True
)
embeddings.index(documents())

# Run a query
embeddings.search("query to run")

Late interaction models excel as reranker pipelines.

from txtai.pipeline import Reranker, Similarity

similarity = Similarity(path="neuml/colbert-muvera-micro", lateencode=True)
ranker = Reranker(embeddings, similarity)
ranker("query to run")

Usage (PyLate)

Alternatively, the model can be loaded with PyLate.

from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path="neuml/colbert-muvera-micro",
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

Full Model Architecture

ColBERT(
  (0): Transformer({'max_seq_length': 299, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Dense({'in_features': 128, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

Evaluation

BEIR Subset

The following table shows a subset of BEIR scored with the txtai benchmarks script.

Scores reported are ndcg@10 and grouped into the following three categories.

FULL multi-vector maxsim

Model	Parameters	ArguAna	NFCorpus	SciFact	Average
AnswerAI ColBERT Small v1	33M	0.4440	0.3649	0.7423	0.5171
ColBERT v2	110M	0.4595	0.3165	0.6456	0.4739
ColBERT MUVERA Micro	4M	0.3947	0.3235	0.6676	0.4619
ColBERT MUVERA Small	33M	0.4455	0.3502	0.7145	0.5034
GTE ModernColBERT v1	149M	0.4946	0.3717	0.7529	0.5397

MUVERA encoding + maxsim re-ranking of the top 100 results per MUVERA paper

Model	Parameters	ArguAna	NFCorpus	SciFact	Average
AnswerAI ColBERT Small v1	33M	0.0317	0.1135	0.0836	0.0763
ColBERT v2	110M	0.4562	0.3025	0.6278	0.4622
ColBERT MUVERA Micro	4M	0.3849	0.3095	0.6464	0.4469
ColBERT MUVERA Small	33M	0.4451	0.3537	0.7148	0.5045
GTE ModernColBERT v1	149M	0.0265	0.1052	0.0556	0.0624

MUVERA encoding only

Model	Parameters	ArguAna	NFCorpus	SciFact	Average
AnswerAI ColBERT Small v1	33M	0.0024	0.0201	0.0047	0.0091
ColBERT v2	110M	0.3463	0.2356	0.5002	0.3607
ColBERT MUVERA Micro	4M	0.2795	0.2348	0.4875	0.3339
ColBERT MUVERA Small	33M	0.3850	0.2928	0.6357	0.4378
GTE ModernColBERT v1	149M	0.0003	0.0203	0.0013	0.0073

Note: The scores reported don't match scores reported in the respective papers due to different default settings in the txtai benchmark scripts.

As noted earlier, models trained with min-max score normalization don't perform well with MUVERA encoding. See this GitHub Issue for more.

In reviewing the scores, this model is surprisingly and unreasonably competitive with the original ColBERT v2 model at only 3% of the size!

Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator

Metric	Value
MaxSim_accuracy@1	0.535
MaxSim_accuracy@3	0.7
MaxSim_accuracy@5	0.7431
MaxSim_accuracy@10	0.8108
MaxSim_precision@1	0.535
MaxSim_precision@3	0.3303
MaxSim_precision@5	0.2492
MaxSim_precision@10	0.1796
MaxSim_recall@1	0.3059
MaxSim_recall@3	0.4554
MaxSim_recall@5	0.5032
MaxSim_recall@10	0.5852
MaxSim_ndcg@10	0.5599
MaxSim_mrr@10	0.6283
MaxSim_map@100	0.4818

Training Details

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• learning_rate: 0.0003
• num_train_epochs: 1
• warmup_ratio: 0.05
• bf16: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 0.0003
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.05
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Framework Versions

• Python: 3.10.18
• Sentence Transformers: 4.0.2
• PyLate: 1.3.0
• Transformers: 4.52.3
• PyTorch: 2.8.0+cu128
• Accelerate: 1.10.1
• Datasets: 4.0.0
• Tokenizers: 0.21.4

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084"
}

PyLate

@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}