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	---
	base_model: answerdotai/ModernBERT-base
	datasets:
	- lightonai/ms-marco-en-bge
	language:
	- en
	library_name: PyLate
	pipeline_tag: sentence-similarity
	tags:
	- ColBERT
	- PyLate
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:808728
	- loss:Distillation
	---

	# PyLate model based on answerdotai/ModernBERT-base

	This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge) 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: [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 6e461621ae9e2dffc138de99490e9baee354deb5 -->
	- Document Length: 180 tokens
	- Query Length: 32 tokens
	- Output Dimensionality: 128 tokens
	- Similarity Function: MaxSim
	- Training Dataset:
	- [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge)
	- Language: en
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [PyLate Documentation](https://lightonai.github.io/pylate/)
	- Repository: [PyLate on GitHub](https://github.com/lightonai/pylate)
	- Hugging Face: [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)

	### Full Model Architecture

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

	## Usage
	First install the PyLate library:

	```bash
	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:

	```python
	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:

	```python
	# 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:

	```python
	# 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:

	```python
	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,
	)
	```

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	## Training Details

	### Training Dataset

	#### train

	* Dataset: [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge) at [11e6ffa](https://huggingface.co/datasets/lightonai/ms-marco-en-bge/tree/11e6ffa1d22f461579f451eb31bdc964244cb61f)
	* Size: 808,728 training samples
	* Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query_id \| document_ids \| scores \|
	\|:--------\|:--------------------------------------------------------------------------------\|:------------------------------------\|:------------------------------------\|
	\| type \| string \| list \| list \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 5.59 tokens</li><li>max: 6 tokens</li></ul> \| <ul><li>size: 32 elements</li></ul> \| <ul><li>size: 32 elements</li></ul> \|
	* Samples:
	\| query_id \| document_ids \| scores \|
	\|:--------------------\|:--------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------\|
	\| <code>121352</code> \| <code>['2259784', '4923159', '40211', '1545154', '8527175', ...]</code> \| <code>[0.2343463897705078, 0.639204204082489, 0.3806908428668976, 0.5623092651367188, 0.8051995635032654, ...]</code> \|
	\| <code>634306</code> \| <code>['7723525', '1874779', '379307', '2738583', '7599583', ...]</code> \| <code>[0.7124203443527222, 0.7379189729690552, 0.5786551237106323, 0.6142299175262451, 0.6755089163780212, ...]</code> \|
	\| <code>920825</code> \| <code>['5976297', '2866112', '3560294', '3285659', '4706740', ...]</code> \| <code>[0.6462352871894836, 0.7880821228027344, 0.791019856929779, 0.7709633111953735, 0.8284491300582886, ...]</code> \|
	* Loss: <code>pylate.losses.distillation.Distillation</code>

	### Evaluation Results

	nDCG@10 scores for multi-vector retrieval models

	\| Model \| SciFact \| NFCorpus \| FiQA \| TREC-Covid \|
	\| --------------------------- \| --------- \| -------- \| --------- \| ---------- \|
	\| BERT \| 71.5 \| 34.2 \| 35.0 \| 69.9 \|
	\| ModernBERT-Base (in paper) \| 73.0 \| 35.2 \| 38.0 \| 80.5 \|
	\| ModernBERT-Base (this repo) \| 73.88 \| 34.96 \| 39.47 \| 79.36 \|

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 16
	- `learning_rate`: 8e-05
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.05
	- `bf16`: True

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 16
	- `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`: 8e-05
	- `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`: True
	- `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

	</details>


	### Framework Versions
	- Python: 3.11.9
	- Sentence Transformers: 3.3.0
	- PyLate: 1.1.4
	- Transformers: 4.48.0.dev0
	- PyTorch: 2.4.0
	- Accelerate: 1.2.1
	- Datasets: 2.21.0
	- Tokenizers: 0.21.0


	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@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
	```bibtex
	@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}
	}
	```

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