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:baed1eb195b645cff428228265bca73161d512c5ef977b45ca8121ab7bba1417
+size 393304

README.md

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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 5756c58a31a2478f9e62146021f48295a92c3da5 -->
+- **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,
+)
+```
+
+<!--
+### 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.*
+-->
+
+<!--
+## 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
+
+### 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>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 4
+- `gradient_accumulation_steps`: 4
+- `learning_rate`: 8e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.05
+- `bf16`: True
+- `tf32`: 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`: 4
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 4
+- `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`: True
+- `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
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0020 | 100   | 0.0524        |
+| 0.0040 | 200   | 0.0482        |
+| 0.0059 | 300   | 0.0464        |
+| 0.0079 | 400   | 0.043         |
+| 0.0099 | 500   | 0.0387        |
+| 0.0119 | 600   | 0.0383        |
+| 0.0138 | 700   | 0.0345        |
+| 0.0158 | 800   | 0.0307        |
+| 0.0178 | 900   | 0.0294        |
+| 0.0198 | 1000  | 0.0275        |
+| 0.0218 | 1100  | 0.0271        |
+| 0.0237 | 1200  | 0.0264        |
+| 0.0257 | 1300  | 0.0258        |
+| 0.0277 | 1400  | 0.0246        |
+| 0.0297 | 1500  | 0.0239        |
+| 0.0317 | 1600  | 0.023         |
+| 0.0336 | 1700  | 0.0216        |
+| 0.0356 | 1800  | 0.0282        |
+| 0.0376 | 1900  | 0.0211        |
+| 0.0396 | 2000  | 0.0205        |
+| 0.0415 | 2100  | 0.0197        |
+| 0.0435 | 2200  | 0.0187        |
+| 0.0455 | 2300  | 0.0184        |
+| 0.0475 | 2400  | 0.0177        |
+| 0.0495 | 2500  | 0.0179        |
+| 0.0514 | 2600  | 0.0173        |
+| 0.0534 | 2700  | 0.0169        |
+| 0.0554 | 2800  | 0.0163        |
+| 0.0574 | 2900  | 0.016         |
+| 0.0594 | 3000  | 0.016         |
+| 0.0613 | 3100  | 0.0147        |
+| 0.0633 | 3200  | 0.0148        |
+| 0.0653 | 3300  | 0.0155        |
+| 0.0673 | 3400  | 0.0149        |
+| 0.0692 | 3500  | 0.0149        |
+| 0.0712 | 3600  | 0.0141        |
+| 0.0732 | 3700  | 0.0145        |
+| 0.0752 | 3800  | 0.0142        |
+| 0.0772 | 3900  | 0.0143        |
+| 0.0791 | 4000  | 0.0137        |
+| 0.0811 | 4100  | 0.0134        |
+| 0.0831 | 4200  | 0.0129        |
+| 0.0851 | 4300  | 0.0133        |
+| 0.0871 | 4400  | 0.0135        |
+| 0.0890 | 4500  | 0.0128        |
+| 0.0910 | 4600  | 0.0126        |
+| 0.0930 | 4700  | 0.0126        |
+| 0.0950 | 4800  | 0.0129        |
+| 0.0969 | 4900  | 0.0127        |
+| 0.0989 | 5000  | 0.0127        |
+| 0.1009 | 5100  | 0.0125        |
+| 0.1029 | 5200  | 0.0119        |
+| 0.1049 | 5300  | 0.0124        |
+| 0.1068 | 5400  | 0.012         |
+| 0.1088 | 5500  | 0.013         |
+| 0.1108 | 5600  | 0.0119        |
+| 0.1128 | 5700  | 0.0118        |
+| 0.1147 | 5800  | 0.0121        |
+| 0.1167 | 5900  | 0.0119        |
+| 0.1187 | 6000  | 0.0116        |
+| 0.1207 | 6100  | 0.0112        |
+| 0.1227 | 6200  | 0.0116        |
+| 0.1246 | 6300  | 0.0115        |
+| 0.1266 | 6400  | 0.0119        |
+| 0.1286 | 6500  | 0.0115        |
+| 0.1306 | 6600  | 0.0109        |
+| 0.1326 | 6700  | 0.0114        |
+| 0.1345 | 6800  | 0.0114        |
+| 0.1365 | 6900  | 0.0109        |
+| 0.1385 | 7000  | 0.011         |
+| 0.1405 | 7100  | 0.0111        |
+| 0.1424 | 7200  | 0.0109        |
+| 0.1444 | 7300  | 0.0108        |
+| 0.1464 | 7400  | 0.0112        |
+| 0.1484 | 7500  | 0.0106        |
+| 0.1504 | 7600  | 0.011         |
+| 0.1523 | 7700  | 0.0106        |
+| 0.1543 | 7800  | 0.0107        |
+| 0.1563 | 7900  | 0.0108        |
+| 0.1583 | 8000  | 0.0106        |
+| 0.1603 | 8100  | 0.0107        |
+| 0.1622 | 8200  | 0.0108        |
+| 0.1642 | 8300  | 0.0103        |
+| 0.1662 | 8400  | 0.0107        |
+| 0.1682 | 8500  | 0.0104        |
+| 0.1701 | 8600  | 0.011         |
+| 0.1721 | 8700  | 0.0105        |
+| 0.1741 | 8800  | 0.0105        |
+| 0.1761 | 8900  | 0.01          |
+| 0.1781 | 9000  | 0.0106        |
+| 0.1800 | 9100  | 0.0105        |
+| 0.1820 | 9200  | 0.0104        |
+| 0.1840 | 9300  | 0.0104        |
+| 0.1860 | 9400  | 0.0107        |
+| 0.1879 | 9500  | 0.0102        |
+| 0.1899 | 9600  | 0.0103        |
+| 0.1919 | 9700  | 0.0105        |
+| 0.1939 | 9800  | 0.01          |
+| 0.1959 | 9900  | 0.0098        |
+| 0.1978 | 10000 | 0.0099        |
+| 0.1998 | 10100 | 0.0099        |
+| 0.2018 | 10200 | 0.0099        |
+| 0.2038 | 10300 | 0.0098        |
+| 0.2058 | 10400 | 0.01          |
+| 0.2077 | 10500 | 0.0101        |
+| 0.2097 | 10600 | 0.0098        |
+| 0.2117 | 10700 | 0.0101        |
+| 0.2137 | 10800 | 0.0098        |
+| 0.2156 | 10900 | 0.0101        |
+| 0.2176 | 11000 | 0.01          |
+| 0.2196 | 11100 | 0.01          |
+| 0.2216 | 11200 | 0.0096        |
+| 0.2236 | 11300 | 0.0096        |
+| 0.2255 | 11400 | 0.0096        |
+| 0.2275 | 11500 | 0.0098        |
+| 0.2295 | 11600 | 0.0099        |
+| 0.2315 | 11700 | 0.0094        |
+| 0.2335 | 11800 | 0.0096        |
+| 0.2354 | 11900 | 0.0094        |
+| 0.2374 | 12000 | 0.0098        |
+| 0.2394 | 12100 | 0.0095        |
+| 0.2414 | 12200 | 0.0095        |
+| 0.2433 | 12300 | 0.0098        |
+| 0.2453 | 12400 | 0.0097        |
+| 0.2473 | 12500 | 0.0094        |
+| 0.2493 | 12600 | 0.0093        |
+| 0.2513 | 12700 | 0.0093        |
+| 0.2532 | 12800 | 0.0092        |
+| 0.2552 | 12900 | 0.0094        |
+| 0.2572 | 13000 | 0.0095        |
+| 0.2592 | 13100 | 0.0093        |
+| 0.2612 | 13200 | 0.009         |
+| 0.2631 | 13300 | 0.0087        |
+| 0.2651 | 13400 | 0.0089        |
+| 0.2671 | 13500 | 0.009         |
+| 0.2691 | 13600 | 0.0091        |
+| 0.2710 | 13700 | 0.0092        |
+| 0.2730 | 13800 | 0.0089        |
+| 0.2750 | 13900 | 0.0091        |
+| 0.2770 | 14000 | 0.0092        |
+| 0.2790 | 14100 | 0.0088        |
+| 0.2809 | 14200 | 0.009         |
+| 0.2829 | 14300 | 0.0091        |
+| 0.2849 | 14400 | 0.0086        |
+| 0.2869 | 14500 | 0.009         |
+| 0.2888 | 14600 | 0.0088        |
+| 0.2908 | 14700 | 0.0092        |
+| 0.2928 | 14800 | 0.009         |
+| 0.2948 | 14900 | 0.0088        |
+| 0.2968 | 15000 | 0.0087        |
+| 0.2987 | 15100 | 0.0085        |
+| 0.3007 | 15200 | 0.009         |
+| 0.3027 | 15300 | 0.0088        |
+| 0.3047 | 15400 | 0.0086        |
+| 0.3067 | 15500 | 0.0087        |
+| 0.3086 | 15600 | 0.0088        |
+| 0.3106 | 15700 | 0.0085        |
+| 0.3126 | 15800 | 0.0088        |
+| 0.3146 | 15900 | 0.0085        |
+| 0.3165 | 16000 | 0.0086        |
+| 0.3185 | 16100 | 0.0086        |
+| 0.3205 | 16200 | 0.0087        |
+| 0.3225 | 16300 | 0.0088        |
+| 0.3245 | 16400 | 0.0087        |
+| 0.3264 | 16500 | 0.0087        |
+| 0.3284 | 16600 | 0.0086        |
+| 0.3304 | 16700 | 0.0087        |
+| 0.3324 | 16800 | 0.0092        |
+| 0.3344 | 16900 | 0.0085        |
+| 0.3363 | 17000 | 0.0088        |
+| 0.3383 | 17100 | 0.0084        |
+| 0.3403 | 17200 | 0.0088        |
+| 0.3423 | 17300 | 0.0083        |
+| 0.3442 | 17400 | 0.0085        |
+| 0.3462 | 17500 | 0.0083        |
+| 0.3482 | 17600 | 0.0084        |
+| 0.3502 | 17700 | 0.0084        |
+| 0.3522 | 17800 | 0.0083        |
+| 0.3541 | 17900 | 0.0087        |
+| 0.3561 | 18000 | 0.0083        |
+| 0.3581 | 18100 | 0.0085        |
+| 0.3601 | 18200 | 0.0082        |
+| 0.3621 | 18300 | 0.0079        |
+| 0.3640 | 18400 | 0.0085        |
+| 0.3660 | 18500 | 0.0084        |
+| 0.3680 | 18600 | 0.0082        |
+| 0.3700 | 18700 | 0.0083        |
+| 0.3719 | 18800 | 0.0082        |
+| 0.3739 | 18900 | 0.0082        |
+| 0.3759 | 19000 | 0.0083        |
+| 0.3779 | 19100 | 0.0081        |
+| 0.3799 | 19200 | 0.0083        |
+| 0.3818 | 19300 | 0.0079        |
+| 0.3838 | 19400 | 0.0083        |
+| 0.3858 | 19500 | 0.0082        |
+| 0.3878 | 19600 | 0.0084        |
+| 0.3897 | 19700 | 0.0084        |
+| 0.3917 | 19800 | 0.008         |
+| 0.3937 | 19900 | 0.0081        |
+| 0.3957 | 20000 | 0.0083        |
+| 0.3977 | 20100 | 0.0082        |
+| 0.3996 | 20200 | 0.0078        |
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+| 0.9853 | 49800 | 0.0059        |
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+| 0.9951 | 50300 | 0.0059        |
+| 0.9971 | 50400 | 0.0059        |
+| 0.9991 | 50500 | 0.006         |
+
+</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}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,46 @@
+{
+  "_name_or_path": "/home/joe/ModernBERT/examples/output/ModernBERT-base/ModernBERT-base-colbert-KD-8e-05/final",
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "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": 8192,
+  "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": 50283,
+  "position_embedding_type": "absolute",
+  "reference_compile": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0.dev0",
+  "vocab_size": 50370
+}

config_sentence_transformers.json

@@ -0,0 +1,49 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.0",
+    "transformers": "4.48.0.dev0",
+    "pytorch": "2.4.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine",
+  "query_prefix": "[Q] ",
+  "document_prefix": "[D] ",
+  "query_length": 32,
+  "document_length": 180,
+  "attend_to_expansion_tokens": false,
+  "skiplist_words": [
+    "!",
+    "\"",
+    "#",
+    "$",
+    "%",
+    "&",
+    "'",
+    "(",
+    ")",
+    "*",
+    "+",
+    ",",
+    "-",
+    ".",
+    "/",
+    ":",
+    ";",
+    "<",
+    "=",
+    ">",
+    "?",
+    "@",
+    "[",
+    "\\",
+    "]",
+    "^",
+    "_",
+    "`",
+    "{",
+    "|",
+    "}",
+    "~"
+  ]
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0148f1124fae8d4414ca486e88d48bb0e394ebe9f7ddad61a5cedd471955e9ac
+size 596076280

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 179,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": true,
+    "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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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "|||IP_ADDRESS|||",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "1": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    "50255": {
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50256": {
+      "content": "                      ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
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+    "50257": {
+      "content": "                     ",
+      "lstrip": false,
+      "normalized": true,
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+      "special": false
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+    "50258": {
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50259": {
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
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+      "lstrip": false,
+      "normalized": true,
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+      "single_word": false,
+      "special": false
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+    "50261": {
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+      "lstrip": false,
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+      "lstrip": false,
+      "normalized": true,
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+      "special": false
+    },
+    "50263": {
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