souvickdascmsa019/initial-colbert

PyLate model based on colbert-ir/colbertv2.0

This is a PyLate model finetuned from colbert-ir/colbertv2.0 on the msmarco-bm25 dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Model Details

Model Description

• Model Type: PyLate model
• Base model: colbert-ir/colbertv2.0
• Document Length: 180 tokens
• Query Length: 32 tokens
• Output Dimensionality: 128 tokens
• Similarity Function: MaxSim
• Training Dataset:
  • msmarco-bm25
• Language: en

Model Sources

• Documentation: PyLate Documentation
• Repository: PyLate on GitHub
• Hugging Face: PyLate models on Hugging Face

Full Model Architecture

ColBERT(
  (0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

Usage

First install the PyLate library:

pip install -U pylate

Retrieval

PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.

Indexing documents

First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model
model = models.ColBERT(
    model_name_or_path=pylate_model_id,
)

# Step 2: Initialize the Voyager index
index = indexes.Voyager(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
    index_folder="pylate-index",
    index_name="index",
)

Retrieving top-k documents for queries

Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
    show_progress_bar=True,
)

# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

Reranking

If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:

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=pylate_model_id,
)

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,
)

Training Details

Training Dataset

msmarco-bm25

• Dataset: msmarco-bm25 at ce8a493
• Size: 497,901 training samples
• Columns: query, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	query	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 10.14 tokens max: 20 tokens	min: 17 tokens mean: 31.91 tokens max: 32 tokens	min: 17 tokens mean: 31.84 tokens max: 32 tokens

• Samples:

  query	positive	negative
  what is null hypothesis and why is it used in experimental research	A null hypothesis is one that is assumed to be true unless it has been contradicted. It is used to compare to another hypothesis. The experimental hypothesis is what you are observing, and you expect it to differ from the control. erm i know that a null hypothesis is when nothing happens at all i think.	A null hypothesis is one that is assumed to be true unless it has been contradicted. It is used to compare to another hypothesis. The experimental hypothesis is what you are observing, and you expect it to differ from the control. erm i know that a null hypothesis is when nothing happens at all i think.
  number of students per instructor	The article posited that students preferred classes of 10-20 students, and instructors suggested that the ideal class would have 19 students. Instructors reported that at 39 students problems began to arise, and that a class of 51 students was impossible. They also reported that an uncomfortably small class begins at 7 students, and an impossibly small class has 4 or less.	The ratio of instructors to students isn’t as important here as in the lab setting. One to two instructors per 10 students will suffice. Once the students are divided into groups, the instructor should begin to methodically teach ECG interpretation. The instructor should start with waveform definition and recognition.
  when should exclamation marks be used?	The exclamation mark (British English) or exclamation point (American English) is a punctuation mark usually used after an interjection or exclamation to indicate strong feelings or high volume (shouting), and often marks the end of a sentence.	1 Question marks and exclamation marks go inside the quotation marks when the quoted material is a question or an exclamation and outside the quotation marks when the whole sentence is a question or an exclamation. Question marks and exclamation marks go inside the quotation marks when the quoted material is a question or an exclamation and outside the quotation marks when the whole sentence is a question or an exclamation.

• Loss: pylate.losses.contrastive.Contrastive

Evaluation Dataset

msmarco-bm25

• Dataset: msmarco-bm25 at ce8a493
• Size: 5,030 evaluation samples
• Columns: query, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	query	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 10.17 tokens max: 32 tokens	min: 20 tokens mean: 31.92 tokens max: 32 tokens	min: 16 tokens mean: 31.93 tokens max: 32 tokens

• Samples:

  query	positive	negative
  what is a hypermarket	By definition a hypermarket is the combination of a supermarket and a department store which has at least 150,000 square feet of floor space, and at least 35% of that space is used for the sale of nonfood merchandise. Generally the terms hypermarket, and superstore are used interchangeably.	hypermarket meaning, definition, what is hypermarket: a very large shop, usually outside the centre of town. Learn more.
  what is fd&c yellow #6 lake.	FD&C Yellow No. 6 Lake is a color additive used for drug dosage forms such as tablets and capsules. It is also approved for use in foods and cosmetics. FD&C Yellow No. 6 Lake imparts a reddish-yellow color to medicinal dosage forms. FDA performs regulatory review for color additives used in foods, drugs, cosmetics, and medical devices. FD&C specifies the color is approved for use in food, drugs and cosmetics. FD&C Yellow No. 6 Lake may be safely used as a color additive when following FDA specifications. To form lake colors, straight dyes (such as FD&C Yellow No. 6) are mixed with precipitants and salts. Aluminum may be a component. Lakes may be used as color additives for tablet coatings due to their stability.	Coumadin: 6 mg [scored; contains fd&c blue #1 aluminum lake, fd&c yellow #6 aluminum lake] Coumadin: 7.5 mg [scored; contains fd&c yellow #10 aluminum lake, fd&c yellow #6 aluminum lake] Coumadin: 10 mg [scored; dye free] Jantoven: 1 mg [scored; contains fd&c red #40 aluminum lake]
  how long can ringworm live on clothes	-Sometimes the ringworm on the scalp can causes patches of hair loss. Ringworm in dogs can be spread many of the same ways. Even sharing clothes, towels, or combs may result in spreading the infection. Ringworm is caused by different kinds of fungus on the skin, hair, or nails caused by an infection.he fungus that causes ringworm can typically live up to 7 days on surfaces such as counter tops, carpets, and floors, but it has been reported that some types can live up to one year.	What Causes Ringworm? Ringworm is more common in unsanitary and crowded places. That's because it can live on both skin and surfaces like shower floors, and can be transferred by sharing clothes, sheets, and towels. Even other mammals, including cats and dogs, can easily transfer ringworm to humans. What Are the Types of Ringworm?

• Loss: pylate.losses.contrastive.Contrastive

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• learning_rate: 3e-06
• num_train_epochs: 1
• fp16: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• 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: 3e-06
• 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: 0.0
• 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: False
• fp16: True
• 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
• dispatch_batches: None
• split_batches: 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

Training Logs

Epoch	Step	Training Loss
0.0321	500	0.4976
0.0643	1000	0.3532
0.0964	1500	0.3195
0.1285	2000	0.3079
0.1607	2500	0.3067
0.1928	3000	0.2957
0.2249	3500	0.3086
0.2571	4000	0.2927
0.2892	4500	0.2922
0.3213	5000	0.2931
0.3535	5500	0.2957
0.3856	6000	0.2809
0.4177	6500	0.2773
0.4499	7000	0.2728
0.4820	7500	0.2888
0.5141	8000	0.2863
0.5463	8500	0.2813
0.5784	9000	0.2695
0.6105	9500	0.2834
0.6427	10000	0.2739
0.6748	10500	0.2744
0.7069	11000	0.2849
0.7391	11500	0.2808
0.7712	12000	0.2796
0.8033	12500	0.2772
0.8355	13000	0.2813
0.8676	13500	0.2756
0.8997	14000	0.2771
0.9319	14500	0.283
0.9640	15000	0.2731
0.9961	15500	0.2865

Framework Versions

• Python: 3.12.4
• Sentence Transformers: 4.0.2
• PyLate: 1.2.0
• Transformers: 4.48.2
• PyTorch: 2.6.0+cu124
• Accelerate: 1.7.0
• Datasets: 3.6.0
• Tokenizers: 0.21.1

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}
}