Upload Reason-Code-ModernColBERT (first ColBERT for code search)

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 sha256:504ddaaa8aed73f7ff94a676be8cac9fbeabc2cc82545e2d4ec7f6088ac29c69
+size 393304

README.md

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+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:9959
+- loss:CachedContrastive
+pipeline_tag: sentence-similarity
+library_name: PyLate
+---
+
+# PyLate
+
+This is a [PyLate](https://github.com/lightonai/pylate) model trained. 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:** [Unknown](https://huggingface.co/unknown) -->
+- **Document Length:** 512 tokens
+- **Query Length:** 128 tokens
+- **Output Dimensionality:** 128 tokens
+- **Similarity Function:** MaxSim
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **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': 127, '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
+
+#### Unnamed Dataset
+
+
+* Size: 9,959 training samples
+* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                                | positive                                                                             | negative                                                                           |
+  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                               | string                                                                               | string                                                                             |
+  | details | <ul><li>min: 128 tokens</li><li>mean: 128.0 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 108.34 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 79.95 tokens</li><li>max: 128 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Here is the step-by-step reasoning to identify the correct code solution for reading an OVF descriptor file with robust error handling.<br><br>### 1. Identify the Kind of Code<br>The code required is a **Python utility function** (or a small script) that performs **file I/O operations**. Specifically, it needs to:<br>*   Accept a file path as an input argument.<br>*   Attempt to open and read the contents of a file (likely a text-based XML or text file, as OVF descriptors are XML).<br>*   Implement **exception handling** to gracefully manage scenarios where the file does not exist or cannot be read due to permissions or corruption.<br>*   Return the file content (string) or a parsed object (if XML parsing is included), or raise a specific, user-friendly error.<br><br>### 2. Relevant Programming Concepts & Patterns<br>*   **File I/O and Context Managers**: The code must use the `with open(...)` statement. This ensures the file handle is properly closed even if an error occurs during reading, preventing resource leak...</code>    | <code>def get_ovf_descriptor(ovf_path):<br>    if path.exists(ovf_path):<br>        with open(ovf_path, 'r') as f:<br>            try:<br>                ovfd = f.read()<br>                f.close()<br>                return ovfd<br>            except:<br>                print "Could not read file: %s" % ovf_path<br>                exit(1)</code>                                                                                                                                                                                          | <code>def read_vnf_descriptor(vnfd_id, vnf_vendor, vnf_version):<br>    if _catalog_backend is not None:<br>        return _catalog_backend.read_vnf_descriptor(vnfd_id, vnf_vendor,<br>                                                    vnf_version)<br>    return None</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+  | <code>Here is the step-by-step reasoning to identify the correct code solution for adding a custom 'Settings' link to the WordPress plugin action links.<br><br>### 1. What kind of code would answer this query?<br>The solution requires **PHP code** specifically designed for **WordPress plugin development**. It will not be a JavaScript snippet or a CSS style. The code must be a function that hooks into the WordPress plugin management system, likely using the `plugin_action_links_{plugin_basename}` filter.<br><br>### 2. Relevant Programming Concepts, Patterns, and Algorithms<br>*   **WordPress Hooks (Filters):** The core mechanism is the `apply_filters()` system. Specifically, the dynamic filter `plugin_action_links_{plugin_basename}` allows developers to modify the array of action links (Activate, Deactivate, Edit, Delete, Settings) for a specific plugin.<br>*   **Array Manipulation:** The action links are stored as an associative array where the key is the link text (or ID) and the value is the URL. The code must...</code>             | <code>public<br>		function plugin_add_settings_link(<br>			$links<br>		) {<br>			$settings_link_html = '<a href="' . esc_url( self::get_settings_url() ) . '">' . __( 'Settings', 'link-linkid' ) . '</a>';<br>			array_unshift( $links, $settings_link_html );<br><br>			return $links;<br>		}</code>                                                                                                                                                                                                                                                | <code>function plugin_settings_link( $links){ <br>        $settings_link = '<a href="options-general.php?page=esbs-plugin-settings">Settings</a>'; <br>        array_unshift($links, $settings_link); <br>        return $links; <br>    }</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+  | <code>### Reasoning Chain<br><br>1.  **Identify the Goal**: The user wants to parse a JSON Web Token (JWT) in Go specifically to read the payload (claims) *without* performing the cryptographic signature verification. This is often needed for debugging, logging, or when the token is trusted from a different source (e.g., a trusted internal service) and signature validation is handled elsewhere.<br><br>2.  **Analyze the JWT Structure**: A JWT consists of three parts: `header.payload.signature`. The `payload` is a JSON object containing the claims. To extract claims without verification, we need to:<br>    *   Decode the Base64URL-encoded payload.<br>    *   Unmarshal the JSON into a Go struct or `map[string]interface{}`.<br>    *   **Crucially**, skip the step where the library checks the signature against the provided key.<br><br>3.  **Select the Library**: The standard library for JWT in Go is `github.com/golang-jwt/jwt/v5` (or the older `v4`). The older `jwt-go` library is deprecated.<br><br>4.  **Determine the Implementa...</code> | <code>func ParseInsecure(token string, audience []string) (*SVID, error) {<br>	return parse(token, audience, func(tok *jwt.JSONWebToken, td spiffeid.TrustDomain) (map[string]interface{}, error) {<br>		// Obtain the token claims insecurely, i.e. without signature verification<br>		claimsMap := make(map[string]interface{})<br>		if err := tok.UnsafeClaimsWithoutVerification(&claimsMap); err != nil {<br>			return nil, jwtsvidErr.New("unable to get claims from token: %v", err)<br>		}<br><br>		return claimsMap, nil<br>	})<br>}</code> | <code>func ParseAndValidate(token string, bundles jwtbundle.Source, audience []string) (*SVID, error) {<br>	return parse(token, audience, func(tok *jwt.JSONWebToken, trustDomain spiffeid.TrustDomain) (map[string]interface{}, error) {<br>		// Obtain the key ID from the header<br>		keyID := tok.Headers[0].KeyID<br>		if keyID == "" {<br>			return nil, jwtsvidErr.New("token header missing key id")<br>		}<br><br>		// Get JWT Bundle<br>		bundle, err := bundles.GetJWTBundleForTrustDomain(trustDomain)<br>		if err != nil {<br>			return nil, jwtsvidErr.New("no bundle found for trust domain %q", trustDomain)<br>		}<br><br>		// Find JWT authority using the key ID from the token header<br>		authority, ok := bundle.FindJWTAuthority(keyID)<br>		if !ok {<br>			return nil, jwtsvidErr.New("no JWT authority %q found for trust domain %q", keyID, trustDomain)<br>		}<br><br>		// Obtain and verify the token claims using the obtained JWT authority<br>		claimsMap := make(map[string]interface{})<br>		if err := tok.Claims(authority, &claimsMap); err != nil {<br>			return nil, jwtsvidEr...</code> |
+* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 5e-06
+- `warmup_ratio`: 0.05
+- `bf16`: True
+- `tf32`: True
+- `dataloader_num_workers`: 8
+- `dataloader_prefetch_factor`: 4
+- `dataloader_persistent_workers`: 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`: 256
+- `per_device_eval_batch_size`: 256
+- `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`: 5e-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`: 3
+- `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`: 8
+- `dataloader_prefetch_factor`: 4
+- `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`: True
+- `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 |
+|:------:|:----:|:-------------:|
+| 0.0256 | 1    | 2.3632        |
+| 0.0513 | 2    | 2.3367        |
+| 0.0769 | 3    | 2.448         |
+| 0.1026 | 4    | 2.4189        |
+| 0.1282 | 5    | 2.1217        |
+| 0.1538 | 6    | 2.1491        |
+| 0.1795 | 7    | 1.9582        |
+| 0.2051 | 8    | 1.9204        |
+| 0.2308 | 9    | 1.6757        |
+| 0.2564 | 10   | 1.4951        |
+| 0.2821 | 11   | 1.3773        |
+| 0.3077 | 12   | 1.1778        |
+| 0.3333 | 13   | 1.088         |
+| 0.3590 | 14   | 1.0256        |
+| 0.3846 | 15   | 1.0174        |
+| 0.4103 | 16   | 0.8424        |
+| 0.4359 | 17   | 0.9435        |
+| 0.4615 | 18   | 0.854         |
+| 0.4872 | 19   | 0.8846        |
+| 0.5128 | 20   | 0.9211        |
+| 0.5385 | 21   | 0.7185        |
+| 0.5641 | 22   | 0.8183        |
+| 0.5897 | 23   | 0.7488        |
+| 0.6154 | 24   | 0.696         |
+| 0.6410 | 25   | 0.6371        |
+| 0.6667 | 26   | 0.6456        |
+| 0.6923 | 27   | 0.6259        |
+| 0.7179 | 28   | 0.5277        |
+| 0.7436 | 29   | 0.7078        |
+| 0.7692 | 30   | 0.7901        |
+| 0.7949 | 31   | 0.6332        |
+| 0.8205 | 32   | 0.4658        |
+| 0.8462 | 33   | 0.6804        |
+| 0.8718 | 34   | 0.6232        |
+| 0.8974 | 35   | 0.611         |
+| 0.9231 | 36   | 0.6147        |
+| 0.9487 | 37   | 0.5991        |
+| 0.9744 | 38   | 0.6732        |
+| 1.0    | 39   | 0.5281        |
+| 1.0256 | 40   | 0.5556        |
+| 1.0513 | 41   | 0.4985        |
+| 1.0769 | 42   | 0.5527        |
+| 1.1026 | 43   | 0.4919        |
+| 1.1282 | 44   | 0.5443        |
+| 1.1538 | 45   | 0.6086        |
+| 1.1795 | 46   | 0.5949        |
+| 1.2051 | 47   | 0.5734        |
+| 1.2308 | 48   | 0.6677        |
+| 1.2564 | 49   | 0.5189        |
+| 1.2821 | 50   | 0.666         |
+| 1.3077 | 51   | 0.4927        |
+| 1.3333 | 52   | 0.5356        |
+| 1.3590 | 53   | 0.5792        |
+| 1.3846 | 54   | 0.4162        |
+| 1.4103 | 55   | 0.5923        |
+| 1.4359 | 56   | 0.4905        |
+| 1.4615 | 57   | 0.4645        |
+| 1.4872 | 58   | 0.7121        |
+| 1.5128 | 59   | 0.5809        |
+| 1.5385 | 60   | 0.4401        |
+| 1.5641 | 61   | 0.458         |
+| 1.5897 | 62   | 0.4659        |
+| 1.6154 | 63   | 0.5638        |
+| 1.6410 | 64   | 0.4875        |
+| 1.6667 | 65   | 0.4903        |
+| 1.6923 | 66   | 0.5373        |
+| 1.7179 | 67   | 0.3934        |
+| 1.7436 | 68   | 0.5693        |
+| 1.7692 | 69   | 0.4524        |
+| 1.7949 | 70   | 0.4949        |
+| 1.8205 | 71   | 0.466         |
+| 1.8462 | 72   | 0.4837        |
+| 1.8718 | 73   | 0.5391        |
+| 1.8974 | 74   | 0.5266        |
+| 1.9231 | 75   | 0.4747        |
+| 1.9487 | 76   | 0.4502        |
+| 1.9744 | 77   | 0.5449        |
+| 2.0    | 78   | 0.4349        |
+| 2.0256 | 79   | 0.4566        |
+| 2.0513 | 80   | 0.482         |
+| 2.0769 | 81   | 0.5553        |
+| 2.1026 | 82   | 0.4606        |
+| 2.1282 | 83   | 0.4938        |
+| 2.1538 | 84   | 0.4303        |
+| 2.1795 | 85   | 0.4068        |
+| 2.2051 | 86   | 0.4398        |
+| 2.2308 | 87   | 0.4359        |
+| 2.2564 | 88   | 0.4599        |
+| 2.2821 | 89   | 0.4835        |
+| 2.3077 | 90   | 0.404         |
+| 2.3333 | 91   | 0.5046        |
+| 2.3590 | 92   | 0.4678        |
+| 2.3846 | 93   | 0.3891        |
+| 2.4103 | 94   | 0.435         |
+| 2.4359 | 95   | 0.5688        |
+| 2.4615 | 96   | 0.4319        |
+| 2.4872 | 97   | 0.4667        |
+| 2.5128 | 98   | 0.5857        |
+| 2.5385 | 99   | 0.5194        |
+| 2.5641 | 100  | 0.4741        |
+| 2.5897 | 101  | 0.5226        |
+| 2.6154 | 102  | 0.4168        |
+| 2.6410 | 103  | 0.4488        |
+| 2.6667 | 104  | 0.4922        |
+| 2.6923 | 105  | 0.4309        |
+| 2.7179 | 106  | 0.4832        |
+| 2.7436 | 107  | 0.4496        |
+| 2.7692 | 108  | 0.5548        |
+| 2.7949 | 109  | 0.4355        |
+| 2.8205 | 110  | 0.4305        |
+| 2.8462 | 111  | 0.3955        |
+| 2.8718 | 112  | 0.2876        |
+| 2.8974 | 113  | 0.4263        |
+| 2.9231 | 114  | 0.4874        |
+| 2.9487 | 115  | 0.4602        |
+| 2.9744 | 116  | 0.4725        |
+| 3.0    | 117  | 0.5401        |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 4.0.2
+- PyLate: 1.2.0
+- Transformers: 4.48.2
+- PyTorch: 2.10.0a0+a36e1d39eb.nv26.01.42222806
+- Accelerate: 1.13.0
+- Datasets: 4.4.2
+- 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.*
+-->

config.json

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+{
+  "_name_or_path": "output/Reason-Code-ModernColBERT/stage1/final",
+  "architectures": [
+    "ModernBertModel"
+  ],
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+  "attention_dropout": 0.0,
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+  "classifier_activation": "gelu",
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+  "classifier_pooling": "mean",
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+  "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,
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+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
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+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.2",
+  "vocab_size": 50370
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.0.2",
+    "transformers": "4.48.2",
+    "pytorch": "2.10.0a0+a36e1d39eb.nv26.01.42222806"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "MaxSim",
+  "query_prefix": "[Q] ",
+  "document_prefix": "[D] ",
+  "query_length": 128,
+  "document_length": 512,
+  "attend_to_expansion_tokens": false,
+  "skiplist_words": [],
+  "do_query_expansion": false
+}

model.safetensors

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+oid sha256:3f7c79911f4d1e55347a228dfb5eaae0af913c0a19cbe0f5f0bde55a38749743
+size 596076280

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": 127,
+  "do_lower_case": false
+}

special_tokens_map.json

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