Add new SentenceTransformer model

1_Dense/config.json

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+{"in_features": 768, "out_features": 128, "bias": false, "activation_function": "torch.nn.modules.linear.Identity"}

1_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9d5716e26bbeed34d627dc912752311b1be10610ddbd0d841fdd526927a50f11
+size 393304

README.md

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+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+pipeline_tag: sentence-similarity
+library_name: PyLate
+---
+
+# ColBERT-ko-v1.0
+
+**ColBERT-ko-v1.0** is a Korean ColBERT model finetuned with [PyLate](https://github.com/lightonai/pylate). This model is trained exclusively on Korean 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
+- **Document Length:** 1024 tokens
+- **Query Length:** 32 tokens
+- **Output Dimensionality:** 128 tokens
+- **Similarity Function:** MaxSim
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+
+### Full Model Architecture
+
+```
+ColBERT(
+  (0): Transformer({'max_seq_length': 1023, '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
+<details>
+<summary>PyLate for reranking</summary>
+
+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,
+)
+```
+</details>
+
+<details>
+<summary>Usage with PLAID</summary>
+First install the PyLate library:
+
+```bash
+pip install -U pylate
+```
+
+### Retrieval
+
+Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.
+
+#### Indexing documents
+
+Load the ColBERT model and initialize the PLAID 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 PLAID index
+index = indexes.PLAID(
+    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.PLAID(
+    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
+)
+```
+</details>
+
+<details>
+<summary>Usage with MUVERA</summary>
+First install the muvera-py (Python implementation of MUVERA):
+
+```bash
+git clone https://github.com/sionic-ai/muvera-py.git
+cd muvera-py
+```
+
+Then run the main script:
+
+```python
+uv run main_pylate.py
+```
+
+</details>
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Evaluation Dataset
+| Dataset                                                               | Description                                                                                             | Average Length (characters) |
+|-----------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|-----------------------------|
+| [Ko-StrategyQA](https://huggingface.co/datasets/taeminlee/Ko-StrategyQA) | Korean ODQA multi-hop retrieval dataset (translated from StrategyQA) | 305.15 |
+| [AutoRAGRetrieval](https://huggingface.co/datasets/yjoonjang/markers_bm) | Korean document retrieval dataset constructed by parsing PDFs from 5 domains: finance, public, medical, legal, and commerce | 823.60 |
+| [PublicHealthQA](https://huggingface.co/datasets/xhluca/publichealth-qa) | Korean document retrieval dataset for medical and public health domains | 339.00 |
+| [BelebeleRetrieval](https://huggingface.co/datasets/facebook/belebele) | Korean document retrieval dataset based on FLORES-200 | 243.11 |
+| [MultiLongDocRetrieval](https://huggingface.co/datasets/Shitao/MLDR) | Korean long document retrieval dataset covering various domains | 13,813.44 |
+<!-- | [MrTidyRetrieval](https://huggingface.co/datasets/mteb/mrtidy) | Korean document retrieval dataset based on Wikipedia | 166.90 | -->
+<!-- | [MIRACLRetrieval](https://huggingface.co/datasets/miracl/miracl) | Korean document retrieval dataset based on Wikipedia | 166.63 | -->
+
+
+### Average Results
+
+| Model                                         | Parameters | Average Recall@10 | Average Precision@10 | Average NDCG@10 | Average F1@10 |
+|-----------------------------------------------|------------|----------------|-------------------|--------------|------------|
+| **ColBERT-ko-v1.0**                   | **0.1B**     | **0.7999**    | **0.0930**       | **0.7172**  | **0.1655**|
+| jina-colbert-v2                     | 0.5B         | 0.7518        | 0.0888           | 0.6671      | 0.1577    |
+
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 32
+- `learning_rate`: 3e-06
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `bf16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `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`: 0.1
+- `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
+- `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: 3.10.18
+- Sentence Transformers: 4.0.2
+- PyLate: 1.3.0
+- Transformers: 4.52.3
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.10.1
+- Datasets: 3.6.0
+- Tokenizers: 0.21.4
+
+
+## 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}
+}
+```
+
+#### CachedContrastive
+```bibtex
+@misc{gao2021scaling,
+    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
+    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
+    year={2021},
+    eprint={2101.06983},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

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+{
+  "<pad>": 49999,
+  "[D] ": 50001,
+  "[Q] ": 50000
+}

config.json

@@ -0,0 +1,45 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 0,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 1,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 16384,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 49999,
+  "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "sep_token_id": 1,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.3",
+  "vocab_size": 50002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.0.2",
+    "transformers": "4.52.3",
+    "pytorch": "2.8.0+cu128"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "MaxSim",
+  "query_prefix": "[Q] ",
+  "document_prefix": "[D] ",
+  "query_length": 32,
+  "document_length": 1024,
+  "attend_to_expansion_tokens": false,
+  "skiplist_words": [
+    "!",
+    "\"",
+    "#",
+    "$",
+    "%",
+    "&",
+    "'",
+    "(",
+    ")",
+    "*",
+    "+",
+    ",",
+    "-",
+    ".",
+    "/",
+    ":",
+    ";",
+    "<",
+    "=",
+    ">",
+    "?",
+    "@",
+    "[",
+    "\\",
+    "]",
+    "^",
+    "_",
+    "`",
+    "{",
+    "|",
+    "}",
+    "~"
+  ],
+  "do_query_expansion": true
+}

model.safetensors

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+size 594945784

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Dense",
+    "type": "pylate.models.Dense.Dense"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 1023,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "normalized": false,
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<mask>",
+  "sep_token": {
+    "content": "<sep>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
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+}