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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 sha256:7eadea4d4af905a3bca5ba4fc226beeb26f2bd63560a38c0cebc76b661349b5d
+size 393304

README.md

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+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:310935
+- loss:Contrastive
+base_model: lightonai/GTE-ModernColBERT-v1
+pipeline_tag: sentence-similarity
+library_name: PyLate
+metrics:
+- accuracy
+model-index:
+- name: PyLate model based on lightonai/GTE-ModernColBERT-v1
+  results:
+  - task:
+      type: col-berttriplet
+      name: Col BERTTriplet
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: accuracy
+      value: 0.9512865543365479
+      name: Accuracy
+---
+
+# PyLate model based on lightonai/GTE-ModernColBERT-v1
+
+This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [lightonai/GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1). 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:** [lightonai/GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) <!-- at revision 78d50a162b04dfdc45c3af6b4294ba77c24888a3 -->
+- **Document Length:** 300 tokens
+- **Query Length:** 32 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': 299, '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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Col BERTTriplet
+
+* Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code>
+
+| Metric       | Value      |
+|:-------------|:-----------|
+| **accuracy** | **0.9513** |
+
+<!--
+## 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: 310,935 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: 6 tokens</li><li>mean: 24.92 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.06 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.7 tokens</li><li>max: 32 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>The primary objective of enacting a inheritance tax is to mitigate economic inequality and redistribute wealth among the poorer sections of society, although various empirical studies have demonstrated a lack of correlation between the two.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>The principal goal of establishing estate duties as a form of taxation is not solely to address the problem of economic disparity, but more importantly, to redistribute wealth in an equitable manner so as to reduce the vast gap between the rich and the relatively poor segments of the population.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | <code>In a bid to abide by international agreements and world peaceful coexistence standards, most European nations have set up strict fiscal policies ensuring a strong relationship with neighboring countries, including strategic partnerships to promote tourism, as much as quotas to restrict immigration and asylum seekers.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+  | <code>Usability Evaluation Report for the New Web Application<br>Introduction<br>This usability evaluation was conducted to identify issues related to user experience and provide recommendations for improving the overall usability of the new web application. The evaluation focused on the login and registration process, navigation, and search functionality.<br>Methodology<br>The evaluation consisted of user testing and heuristic evaluation. A total of five participants were recruited to participate in the user testing, and each participant was asked to complete several tasks using the web application. The participants' interactions with the application were observed and recorded. Heuristic evaluation was conducted based on a set of well-established usability principles to identify potential usability issues in the application's design and functionality.<br>Results<br>During the user testing, several usability issues were identified. These included difficulties in locating the login and registration features, p...</code> | <code>Design Document: Home and Landing Page Redesign for New Web Application<br>Executive Summary<br>As part of an ongoing effort to improve the user experience and engagement for the new web application, this project focuses on the redesign of the home and landing page. The new design will address usability issues identified in a previous evaluation, make the application more appealing to users, and help drive sales and conversions. The following report includes the design requirements, a full design specification, and guidance for implementation.<br>Goals and Objectives<br>The main goals of this project include: to redesign the home and landing pages to give users an improved first impression of the application; to improve task completion times and create a seamless user experience; to increase conversion rates by reducing bounce rates and making it easier for users to find the information they need.<br>Scope of Work<br>The redesign of the home and landing pages includes: creating a clear visual hierarchy ...</code> | <code>Designing Effective User Interfaces for Virtual Reality ApplicationsIntroductionVirtual reality (VR) technology has been rapidly advancing in recent years, with applications in various fields such as gaming, education, and healthcare. As VR continues to grow in popularity, the need for effective user interfaces has become increasingly important. A well-designed user interface can enhance the overall VR experience, while a poorly designed one can lead to frustration and disorientation.Principles of Effective VR User Interface Design1. Intuitive Interaction: The primary goal of a VR user interface is to provide an intuitive and natural way for users to interact with the virtual environment. This can be achieved through the use of gestures, voice commands, or other innovative methods.2. Visual Feedback: Visual feedback is crucial in VR, as it helps users understand the consequences of their actions. This can be in the form of animations, particles, or other visual effects that provide a c...</code> |
+  | <code>The manager of the local conservation society recently explained measures for sustainable wildlife preservation.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>The conservation society's manager recently explained measures for preserving wildlife sustainably.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | <code>After explaining university education requirements, the career counsellor also talked about wildlife preservation jobs.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+* Loss: <code>pylate.losses.contrastive.Contrastive</code>
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 34,549 evaluation 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: 6 tokens</li><li>mean: 24.32 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.37 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.12 tokens</li><li>max: 32 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                                                            | positive                                                                                                                                                           | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>In a magical forest, there lived a group of animals that loved to dance under the stars. They danced to the rhythm of the crickets and felt the magic of the night.</code> | <code>In a magical forest, there lived a group of animals that loved to dance under the stars on a lovely night. They danced to the rhythm of the crickets.</code> | <code>The forest was a wonderful place where animals could sing and dance to the sounds of nature. Some liked the rustling of leaves, while others liked the buzzing of bees. But they all loved the music of a babbling brook.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  | <code>Given this reasoning-intensive query, find relevant documents that could help answer the question. </code>                                                                 | <code>food_percent/2063AApplicationsLeontiefModels_149.txt</code>                                                                                                  | <code>The use of matrix equations in computer graphics is gaining significant attention in recent years. In computer-aided design (CAD), matrix equations play a crucial role in transforming 2D and 3D objects. For instance, when designing a car model, the CAD software uses matrix equations to rotate, translate, and scale the object. The transformation matrix is a 4x4 matrix that stores the coordinates of the object and performs the required operations. Similarly, in computer gaming, matrix equations are used to animate characters and objects in 3D space. The game developers use transformation matrices to create realistic movements and interactions between objects. However, the complexity of these transformations leads to a high computational cost, making it difficult to achieve real-time rendering. To address this challenge, researchers are exploring the use of machine learning algorithms to optimize the transformation process. For example, a research paper titled 'Matrix Equation-Based 6-DoF...</code> |
+  | <code>A study found that the use of virtual reality in therapy sessions can have a positive effect on mental health by reducing stress and anxiety.</code>                       | <code>A therapy session using virtual reality can significantly reduce patient stress and anxiety.</code>                                                          | <code>Research on artificial intelligence in mental health has also led to the innovation of virtual robots for therapy.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+* Loss: <code>pylate.losses.contrastive.Contrastive</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 32
+- `gradient_accumulation_steps`: 2
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 10
+- `warmup_steps`: 100
+- `fp16`: True
+- `remove_unused_columns`: False
+
+#### 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`: 16
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 100
+- `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`: False
+- `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 | Validation Loss | accuracy |
+|:------:|:-----:|:-------------:|:---------------:|:--------:|
+| 0.0051 | 50    | 4.8488        | -               | -        |
+| 0.0103 | 100   | 2.2402        | -               | -        |
+| 0.0154 | 150   | 1.8204        | -               | -        |
+| 0.0206 | 200   | 1.7765        | -               | -        |
+| 0.0257 | 250   | 1.7482        | -               | -        |
+| 0      | 0     | -             | -               | 0.9227   |
+| 0.0257 | 250   | -             | 1.1625          | -        |
+| 0.0309 | 300   | 1.7821        | -               | -        |
+| 0.0360 | 350   | 1.6761        | -               | -        |
+| 0.0412 | 400   | 1.4887        | -               | -        |
+| 0.0463 | 450   | 1.6001        | -               | -        |
+| 0.0515 | 500   | 1.7426        | -               | -        |
+| 0      | 0     | -             | -               | 0.9317   |
+| 0.0515 | 500   | -             | 1.1088          | -        |
+| 0.0566 | 550   | 1.5562        | -               | -        |
+| 0.0617 | 600   | 1.6811        | -               | -        |
+| 0.0669 | 650   | 1.5994        | -               | -        |
+| 0.0720 | 700   | 1.5981        | -               | -        |
+| 0.0772 | 750   | 1.5713        | -               | -        |
+| 0      | 0     | -             | -               | 0.9369   |
+| 0.0772 | 750   | -             | 1.0817          | -        |
+| 0.0823 | 800   | 1.6516        | -               | -        |
+| 0.0875 | 850   | 1.5768        | -               | -        |
+| 0.0926 | 900   | 1.5902        | -               | -        |
+| 0.0978 | 950   | 1.4613        | -               | -        |
+| 0.1029 | 1000  | 1.6295        | -               | -        |
+| 0      | 0     | -             | -               | 0.9374   |
+| 0.1029 | 1000  | -             | 1.0677          | -        |
+| 0.1081 | 1050  | 1.5301        | -               | -        |
+| 0.1132 | 1100  | 1.6072        | -               | -        |
+| 0.1183 | 1150  | 1.4644        | -               | -        |
+| 0.1235 | 1200  | 1.6331        | -               | -        |
+| 0.1286 | 1250  | 1.5464        | -               | -        |
+| 0      | 0     | -             | -               | 0.9408   |
+| 0.1286 | 1250  | -             | 1.0547          | -        |
+| 0.1338 | 1300  | 1.5406        | -               | -        |
+| 0.1389 | 1350  | 1.5471        | -               | -        |
+| 0.1441 | 1400  | 1.6685        | -               | -        |
+| 0.1492 | 1450  | 1.5644        | -               | -        |
+| 0.1544 | 1500  | 1.6587        | -               | -        |
+| 0      | 0     | -             | -               | 0.9420   |
+| 0.1544 | 1500  | -             | 1.0590          | -        |
+| 0.1595 | 1550  | 1.5793        | -               | -        |
+| 0.1647 | 1600  | 1.4877        | -               | -        |
+| 0.1698 | 1650  | 1.5781        | -               | -        |
+| 0.1750 | 1700  | 1.5081        | -               | -        |
+| 0.1801 | 1750  | 1.5434        | -               | -        |
+| 0      | 0     | -             | -               | 0.9396   |
+| 0.1801 | 1750  | -             | 1.0564          | -        |
+| 0.1852 | 1800  | 1.4617        | -               | -        |
+| 0.1904 | 1850  | 1.4531        | -               | -        |
+| 0.1955 | 1900  | 1.5713        | -               | -        |
+| 0.2007 | 1950  | 1.5166        | -               | -        |
+| 0.2058 | 2000  | 1.4771        | -               | -        |
+| 0      | 0     | -             | -               | 0.9431   |
+| 0.2058 | 2000  | -             | 1.0344          | -        |
+| 0.2110 | 2050  | 1.4706        | -               | -        |
+| 0.2161 | 2100  | 1.5276        | -               | -        |
+| 0.2213 | 2150  | 1.4002        | -               | -        |
+| 0.2264 | 2200  | 1.5605        | -               | -        |
+| 0.2316 | 2250  | 1.4871        | -               | -        |
+| 0      | 0     | -             | -               | 0.9441   |
+| 0.2316 | 2250  | -             | 1.0355          | -        |
+| 0.2367 | 2300  | 1.56          | -               | -        |
+| 0.2418 | 2350  | 1.4322        | -               | -        |
+| 0.2470 | 2400  | 1.4682        | -               | -        |
+| 0.2521 | 2450  | 1.4375        | -               | -        |
+| 0.2573 | 2500  | 1.4499        | -               | -        |
+| 0      | 0     | -             | -               | 0.9434   |
+| 0.2573 | 2500  | -             | 1.0306          | -        |
+| 0.2624 | 2550  | 1.5088        | -               | -        |
+| 0.2676 | 2600  | 1.5577        | -               | -        |
+| 0.2727 | 2650  | 1.4221        | -               | -        |
+| 0.2779 | 2700  | 1.5105        | -               | -        |
+| 0.2830 | 2750  | 1.4681        | -               | -        |
+| 0      | 0     | -             | -               | 0.9453   |
+| 0.2830 | 2750  | -             | 1.0219          | -        |
+| 0.2882 | 2800  | 1.4354        | -               | -        |
+| 0.2933 | 2850  | 1.4982        | -               | -        |
+| 0.2984 | 2900  | 1.5374        | -               | -        |
+| 0.3036 | 2950  | 1.4769        | -               | -        |
+| 0.3087 | 3000  | 1.5767        | -               | -        |
+| 0      | 0     | -             | -               | 0.9450   |
+| 0.3087 | 3000  | -             | 1.0168          | -        |
+| 0.3139 | 3050  | 1.3712        | -               | -        |
+| 0.3190 | 3100  | 1.4979        | -               | -        |
+| 0.3242 | 3150  | 1.4633        | -               | -        |
+| 0.3293 | 3200  | 1.5025        | -               | -        |
+| 0.3345 | 3250  | 1.5206        | -               | -        |
+| 0      | 0     | -             | -               | 0.9457   |
+| 0.3345 | 3250  | -             | 1.0161          | -        |
+| 0.3396 | 3300  | 1.5119        | -               | -        |
+| 0.3448 | 3350  | 1.6285        | -               | -        |
+| 0.3499 | 3400  | 1.4421        | -               | -        |
+| 0.3550 | 3450  | 1.4866        | -               | -        |
+| 0.3602 | 3500  | 1.4651        | -               | -        |
+| 0      | 0     | -             | -               | 0.9465   |
+| 0.3602 | 3500  | -             | 1.0085          | -        |
+| 0.3653 | 3550  | 1.3777        | -               | -        |
+| 0.3705 | 3600  | 1.5256        | -               | -        |
+| 0.3756 | 3650  | 1.358         | -               | -        |
+| 0.3808 | 3700  | 1.4384        | -               | -        |
+| 0.3859 | 3750  | 1.4847        | -               | -        |
+| 0      | 0     | -             | -               | 0.9461   |
+| 0.3859 | 3750  | -             | 1.0093          | -        |
+| 0.3911 | 3800  | 1.327         | -               | -        |
+| 0.3962 | 3850  | 1.4463        | -               | -        |
+| 0.4014 | 3900  | 1.3179        | -               | -        |
+| 0.4065 | 3950  | 1.4312        | -               | -        |
+| 0.4116 | 4000  | 1.4179        | -               | -        |
+| 0      | 0     | -             | -               | 0.9460   |
+| 0.4116 | 4000  | -             | 1.0145          | -        |
+| 0.4168 | 4050  | 1.4828        | -               | -        |
+| 0.4219 | 4100  | 1.4568        | -               | -        |
+| 0.4271 | 4150  | 1.4921        | -               | -        |
+| 0.4322 | 4200  | 1.4485        | -               | -        |
+| 0.4374 | 4250  | 1.4908        | -               | -        |
+| 0      | 0     | -             | -               | 0.9478   |
+| 0.4374 | 4250  | -             | 1.0121          | -        |
+| 0.4425 | 4300  | 1.295         | -               | -        |
+| 0.4477 | 4350  | 1.4687        | -               | -        |
+| 0.4528 | 4400  | 1.3846        | -               | -        |
+| 0.4580 | 4450  | 1.4704        | -               | -        |
+| 0.4631 | 4500  | 1.3646        | -               | -        |
+| 0      | 0     | -             | -               | 0.9480   |
+| 0.4631 | 4500  | -             | 1.0056          | -        |
+| 0.4683 | 4550  | 1.4779        | -               | -        |
+| 0.4734 | 4600  | 1.4581        | -               | -        |
+| 0.4785 | 4650  | 1.3786        | -               | -        |
+| 0.4837 | 4700  | 1.56          | -               | -        |
+| 0.4888 | 4750  | 1.4334        | -               | -        |
+| 0      | 0     | -             | -               | 0.9475   |
+| 0.4888 | 4750  | -             | 1.0032          | -        |
+| 0.4940 | 4800  | 1.3877        | -               | -        |
+| 0.4991 | 4850  | 1.3485        | -               | -        |
+| 0.5043 | 4900  | 1.4509        | -               | -        |
+| 0.5094 | 4950  | 1.3693        | -               | -        |
+| 0.5146 | 5000  | 1.5226        | -               | -        |
+| 0      | 0     | -             | -               | 0.9477   |
+| 0.5146 | 5000  | -             | 0.9976          | -        |
+| 0.5197 | 5050  | 1.4423        | -               | -        |
+| 0.5249 | 5100  | 1.4191        | -               | -        |
+| 0.5300 | 5150  | 1.5109        | -               | -        |
+| 0.5351 | 5200  | 1.4509        | -               | -        |
+| 0.5403 | 5250  | 1.4351        | -               | -        |
+| 0      | 0     | -             | -               | 0.9486   |
+| 0.5403 | 5250  | -             | 1.0001          | -        |
+| 0.5454 | 5300  | 1.3868        | -               | -        |
+| 0.5506 | 5350  | 1.4339        | -               | -        |
+| 0.5557 | 5400  | 1.365         | -               | -        |
+| 0.5609 | 5450  | 1.44          | -               | -        |
+| 0.5660 | 5500  | 1.2895        | -               | -        |
+| 0      | 0     | -             | -               | 0.9491   |
+| 0.5660 | 5500  | -             | 1.0065          | -        |
+| 0.5712 | 5550  | 1.4253        | -               | -        |
+| 0.5763 | 5600  | 1.4438        | -               | -        |
+| 0.5815 | 5650  | 1.3543        | -               | -        |
+| 0.5866 | 5700  | 1.5587        | -               | -        |
+| 0.5917 | 5750  | 1.342         | -               | -        |
+| 0      | 0     | -             | -               | 0.9488   |
+| 0.5917 | 5750  | -             | 0.9927          | -        |
+| 0.5969 | 5800  | 1.4503        | -               | -        |
+| 0.6020 | 5850  | 1.4045        | -               | -        |
+| 0.6072 | 5900  | 1.4092        | -               | -        |
+| 0.6123 | 5950  | 1.3318        | -               | -        |
+| 0.6175 | 6000  | 1.416         | -               | -        |
+| 0      | 0     | -             | -               | 0.9504   |
+| 0.6175 | 6000  | -             | 0.9910          | -        |
+| 0.6226 | 6050  | 1.5132        | -               | -        |
+| 0.6278 | 6100  | 1.3275        | -               | -        |
+| 0.6329 | 6150  | 1.4595        | -               | -        |
+| 0.6381 | 6200  | 1.5112        | -               | -        |
+| 0.6432 | 6250  | 1.4435        | -               | -        |
+| 0      | 0     | -             | -               | 0.9515   |
+| 0.6432 | 6250  | -             | 0.9928          | -        |
+| 0.6483 | 6300  | 1.4268        | -               | -        |
+| 0.6535 | 6350  | 1.5071        | -               | -        |
+| 0.6586 | 6400  | 1.3817        | -               | -        |
+| 0.6638 | 6450  | 1.5101        | -               | -        |
+| 0.6689 | 6500  | 1.4014        | -               | -        |
+| 0      | 0     | -             | -               | 0.9490   |
+| 0.6689 | 6500  | -             | 0.9954          | -        |
+| 0.6741 | 6550  | 1.2797        | -               | -        |
+| 0.6792 | 6600  | 1.3829        | -               | -        |
+| 0.6844 | 6650  | 1.4907        | -               | -        |
+| 0.6895 | 6700  | 1.4098        | -               | -        |
+| 0.6947 | 6750  | 1.482         | -               | -        |
+| 0      | 0     | -             | -               | 0.9492   |
+| 0.6947 | 6750  | -             | 0.9937          | -        |
+| 0.6998 | 6800  | 1.3779        | -               | -        |
+| 0.7050 | 6850  | 1.3791        | -               | -        |
+| 0.7101 | 6900  | 1.5183        | -               | -        |
+| 0.7152 | 6950  | 1.4022        | -               | -        |
+| 0.7204 | 7000  | 1.544         | -               | -        |
+| 0      | 0     | -             | -               | 0.9508   |
+| 0.7204 | 7000  | -             | 0.9935          | -        |
+| 0.7255 | 7050  | 1.4566        | -               | -        |
+| 0.7307 | 7100  | 1.4641        | -               | -        |
+| 0.7358 | 7150  | 1.4208        | -               | -        |
+| 0.7410 | 7200  | 1.3391        | -               | -        |
+| 0.7461 | 7250  | 1.5002        | -               | -        |
+| 0      | 0     | -             | -               | 0.9497   |
+| 0.7461 | 7250  | -             | 0.9861          | -        |
+| 0.7513 | 7300  | 1.2985        | -               | -        |
+| 0.7564 | 7350  | 1.5496        | -               | -        |
+| 0.7616 | 7400  | 1.5046        | -               | -        |
+| 0.7667 | 7450  | 1.3687        | -               | -        |
+| 0.7718 | 7500  | 1.3841        | -               | -        |
+| 0      | 0     | -             | -               | 0.9501   |
+| 0.7718 | 7500  | -             | 0.9868          | -        |
+| 0.7770 | 7550  | 1.3996        | -               | -        |
+| 0.7821 | 7600  | 1.5112        | -               | -        |
+| 0.7873 | 7650  | 1.4335        | -               | -        |
+| 0.7924 | 7700  | 1.3867        | -               | -        |
+| 0.7976 | 7750  | 1.3865        | -               | -        |
+| 0      | 0     | -             | -               | 0.9511   |
+| 0.7976 | 7750  | -             | 0.9863          | -        |
+| 0.8027 | 7800  | 1.4039        | -               | -        |
+| 0.8079 | 7850  | 1.379         | -               | -        |
+| 0.8130 | 7900  | 1.3459        | -               | -        |
+| 0.8182 | 7950  | 1.3996        | -               | -        |
+| 0.8233 | 8000  | 1.4151        | -               | -        |
+| 0      | 0     | -             | -               | 0.9511   |
+| 0.8233 | 8000  | -             | 0.9822          | -        |
+| 0.8284 | 8050  | 1.3745        | -               | -        |
+| 0.8336 | 8100  | 1.4404        | -               | -        |
+| 0.8387 | 8150  | 1.4776        | -               | -        |
+| 0.8439 | 8200  | 1.398         | -               | -        |
+| 0.8490 | 8250  | 1.4482        | -               | -        |
+| 0      | 0     | -             | -               | 0.9506   |
+| 0.8490 | 8250  | -             | 0.9803          | -        |
+| 0.8542 | 8300  | 1.4551        | -               | -        |
+| 0.8593 | 8350  | 1.46          | -               | -        |
+| 0.8645 | 8400  | 1.5179        | -               | -        |
+| 0.8696 | 8450  | 1.4067        | -               | -        |
+| 0.8748 | 8500  | 1.4393        | -               | -        |
+| 0      | 0     | -             | -               | 0.9504   |
+| 0.8748 | 8500  | -             | 0.9809          | -        |
+| 0.8799 | 8550  | 1.4995        | -               | -        |
+| 0.8850 | 8600  | 1.4077        | -               | -        |
+| 0.8902 | 8650  | 1.4088        | -               | -        |
+| 0.8953 | 8700  | 1.3464        | -               | -        |
+| 0.9005 | 8750  | 1.3455        | -               | -        |
+| 0      | 0     | -             | -               | 0.9506   |
+| 0.9005 | 8750  | -             | 0.9797          | -        |
+| 0.9056 | 8800  | 1.5172        | -               | -        |
+| 0.9108 | 8850  | 1.3922        | -               | -        |
+| 0.9159 | 8900  | 1.3645        | -               | -        |
+| 0.9211 | 8950  | 1.3627        | -               | -        |
+| 0.9262 | 9000  | 1.3896        | -               | -        |
+| 0      | 0     | -             | -               | 0.9506   |
+| 0.9262 | 9000  | -             | 0.9806          | -        |
+| 0.9314 | 9050  | 1.433         | -               | -        |
+| 0.9365 | 9100  | 1.4678        | -               | -        |
+| 0.9416 | 9150  | 1.3206        | -               | -        |
+| 0.9468 | 9200  | 1.4589        | -               | -        |
+| 0.9519 | 9250  | 1.3494        | -               | -        |
+| 0      | 0     | -             | -               | 0.9509   |
+| 0.9519 | 9250  | -             | 0.9761          | -        |
+| 0.9571 | 9300  | 1.3768        | -               | -        |
+| 0.9622 | 9350  | 1.4449        | -               | -        |
+| 0.9674 | 9400  | 1.4187        | -               | -        |
+| 0.9725 | 9450  | 1.3046        | -               | -        |
+| 0.9777 | 9500  | 1.3586        | -               | -        |
+| 0      | 0     | -             | -               | 0.9512   |
+| 0.9777 | 9500  | -             | 0.9817          | -        |
+| 0.9828 | 9550  | 1.4631        | -               | -        |
+| 0.9880 | 9600  | 1.3113        | -               | -        |
+| 0.9931 | 9650  | 1.2972        | -               | -        |
+| 0.9983 | 9700  | 1.3793        | -               | -        |
+| 1.0034 | 9750  | 1.1729        | -               | -        |
+| 0      | 0     | -             | -               | 0.9509   |
+| 1.0034 | 9750  | -             | 0.9847          | -        |
+| 1.0085 | 9800  | 1.2009        | -               | -        |
+| 1.0137 | 9850  | 1.2576        | -               | -        |
+| 1.0188 | 9900  | 1.3483        | -               | -        |
+| 1.0240 | 9950  | 1.2609        | -               | -        |
+| 1.0291 | 10000 | 1.3099        | -               | -        |
+| 0      | 0     | -             | -               | 0.9513   |
+| 1.0291 | 10000 | -             | 0.9895          | -        |
+| 1.0343 | 10050 | 1.2224        | -               | -        |
+| 1.0394 | 10100 | 1.3552        | -               | -        |
+| 1.0446 | 10150 | 1.3508        | -               | -        |
+| 1.0497 | 10200 | 1.3242        | -               | -        |
+| 1.0549 | 10250 | 1.2287        | -               | -        |
+| 0      | 0     | -             | -               | 0.9512   |
+| 1.0549 | 10250 | -             | 0.9977          | -        |
+| 1.0600 | 10300 | 1.2863        | -               | -        |
+| 1.0651 | 10350 | 1.2377        | -               | -        |
+| 1.0703 | 10400 | 1.3058        | -               | -        |
+| 1.0754 | 10450 | 1.3013        | -               | -        |
+| 1.0806 | 10500 | 1.3233        | -               | -        |
+| 0      | 0     | -             | -               | 0.9488   |
+| 1.0806 | 10500 | -             | 0.9948          | -        |
+| 1.0857 | 10550 | 1.334         | -               | -        |
+| 1.0909 | 10600 | 1.246         | -               | -        |
+| 1.0960 | 10650 | 1.2298        | -               | -        |
+| 1.1012 | 10700 | 1.2016        | -               | -        |
+| 1.1063 | 10750 | 1.3035        | -               | -        |
+| 0      | 0     | -             | -               | 0.9506   |
+| 1.1063 | 10750 | -             | 0.9947          | -        |
+| 1.1115 | 10800 | 1.2457        | -               | -        |
+| 1.1166 | 10850 | 1.2882        | -               | -        |
+| 1.1217 | 10900 | 1.2365        | -               | -        |
+| 1.1269 | 10950 | 1.19          | -               | -        |
+| 1.1320 | 11000 | 1.2377        | -               | -        |
+| 0      | 0     | -             | -               | 0.9511   |
+| 1.1320 | 11000 | -             | 0.9915          | -        |
+| 1.1372 | 11050 | 1.3028        | -               | -        |
+| 1.1423 | 11100 | 1.319         | -               | -        |
+| 1.1475 | 11150 | 1.3315        | -               | -        |
+| 1.1526 | 11200 | 1.2161        | -               | -        |
+| 1.1578 | 11250 | 1.3555        | -               | -        |
+| 0      | 0     | -             | -               | 0.9511   |
+| 1.1578 | 11250 | -             | 0.9902          | -        |
+| 1.1629 | 11300 | 1.1874        | -               | -        |
+| 1.1681 | 11350 | 1.2373        | -               | -        |
+| 1.1732 | 11400 | 1.2474        | -               | -        |
+| 1.1783 | 11450 | 1.2838        | -               | -        |
+| 1.1835 | 11500 | 1.2242        | -               | -        |
+| 0      | 0     | -             | -               | 0.9518   |
+| 1.1835 | 11500 | -             | 0.9927          | -        |
+| 1.1886 | 11550 | 1.3123        | -               | -        |
+| 1.1938 | 11600 | 1.2874        | -               | -        |
+| 1.1989 | 11650 | 1.2568        | -               | -        |
+| 1.2041 | 11700 | 1.2526        | -               | -        |
+| 1.2092 | 11750 | 1.347         | -               | -        |
+| 0      | 0     | -             | -               | 0.9509   |
+| 1.2092 | 11750 | -             | 0.9883          | -        |
+| 1.2144 | 11800 | 1.3098        | -               | -        |
+| 1.2195 | 11850 | 1.2541        | -               | -        |
+| 1.2247 | 11900 | 1.2791        | -               | -        |
+| 1.2298 | 11950 | 1.2333        | -               | -        |
+| 1.2349 | 12000 | 1.3827        | -               | -        |
+| 0      | 0     | -             | -               | 0.9507   |
+| 1.2349 | 12000 | -             | 0.9943          | -        |
+| 1.2401 | 12050 | 1.2732        | -               | -        |
+| 1.2452 | 12100 | 1.2993        | -               | -        |
+| 1.2504 | 12150 | 1.2947        | -               | -        |
+| 1.2555 | 12200 | 1.3001        | -               | -        |
+| 1.2607 | 12250 | 1.2957        | -               | -        |
+| 0      | 0     | -             | -               | 0.9514   |
+| 1.2607 | 12250 | -             | 0.9865          | -        |
+| 1.2658 | 12300 | 1.1393        | -               | -        |
+| 1.2710 | 12350 | 1.2996        | -               | -        |
+| 1.2761 | 12400 | 1.3218        | -               | -        |
+| 1.2813 | 12450 | 1.2138        | -               | -        |
+| 1.2864 | 12500 | 1.1731        | -               | -        |
+| 0      | 0     | -             | -               | 0.9510   |
+| 1.2864 | 12500 | -             | 0.9964          | -        |
+| 1.2916 | 12550 | 1.3326        | -               | -        |
+| 1.2967 | 12600 | 1.3575        | -               | -        |
+| 1.3018 | 12650 | 1.2948        | -               | -        |
+| 1.3070 | 12700 | 1.2921        | -               | -        |
+| 1.3121 | 12750 | 1.3052        | -               | -        |
+| 0      | 0     | -             | -               | 0.9509   |
+| 1.3121 | 12750 | -             | 0.9840          | -        |
+| 1.3173 | 12800 | 1.3662        | -               | -        |
+| 1.3224 | 12850 | 1.3673        | -               | -        |
+| 1.3276 | 12900 | 1.3006        | -               | -        |
+| 1.3327 | 12950 | 1.4217        | -               | -        |
+| 1.3379 | 13000 | 1.1608        | -               | -        |
+| 0      | 0     | -             | -               | 0.9520   |
+| 1.3379 | 13000 | -             | 0.9848          | -        |
+| 1.3430 | 13050 | 1.2066        | -               | -        |
+| 1.3482 | 13100 | 1.408         | -               | -        |
+| 1.3533 | 13150 | 1.3574        | -               | -        |
+| 1.3584 | 13200 | 1.3171        | -               | -        |
+| 1.3636 | 13250 | 1.3188        | -               | -        |
+| 0      | 0     | -             | -               | 0.9502   |
+| 1.3636 | 13250 | -             | 0.9888          | -        |
+| 1.3687 | 13300 | 1.299         | -               | -        |
+| 1.3739 | 13350 | 1.3015        | -               | -        |
+| 1.3790 | 13400 | 1.3159        | -               | -        |
+| 1.3842 | 13450 | 1.2139        | -               | -        |
+| 1.3893 | 13500 | 1.2855        | -               | -        |
+| 0      | 0     | -             | -               | 0.9514   |
+| 1.3893 | 13500 | -             | 0.9957          | -        |
+| 1.3945 | 13550 | 1.2705        | -               | -        |
+| 1.3996 | 13600 | 1.3099        | -               | -        |
+| 1.4048 | 13650 | 1.3144        | -               | -        |
+| 1.4099 | 13700 | 1.2948        | -               | -        |
+| 1.4150 | 13750 | 1.3313        | -               | -        |
+| 0      | 0     | -             | -               | 0.9512   |
+| 1.4150 | 13750 | -             | 0.9910          | -        |
+| 1.4202 | 13800 | 1.3473        | -               | -        |
+| 1.4253 | 13850 | 1.2037        | -               | -        |
+| 1.4305 | 13900 | 1.3059        | -               | -        |
+| 1.4356 | 13950 | 1.3763        | -               | -        |
+| 1.4408 | 14000 | 1.2606        | -               | -        |
+| 0      | 0     | -             | -               | 0.9523   |
+| 1.4408 | 14000 | -             | 0.9876          | -        |
+| 1.4459 | 14050 | 1.2394        | -               | -        |
+| 1.4511 | 14100 | 1.219         | -               | -        |
+| 1.4562 | 14150 | 1.3501        | -               | -        |
+| 1.4614 | 14200 | 1.2664        | -               | -        |
+| 1.4665 | 14250 | 1.2704        | -               | -        |
+| 0      | 0     | -             | -               | 0.9513   |
+| 1.4665 | 14250 | -             | 0.9945          | -        |
+| 1.4716 | 14300 | 1.2332        | -               | -        |
+| 1.4768 | 14350 | 1.2286        | -               | -        |
+| 1.4819 | 14400 | 1.2123        | -               | -        |
+| 1.4871 | 14450 | 1.2437        | -               | -        |
+| 1.4922 | 14500 | 1.2292        | -               | -        |
+| 0      | 0     | -             | -               | 0.9502   |
+| 1.4922 | 14500 | -             | 0.9886          | -        |
+| 1.4974 | 14550 | 1.3007        | -               | -        |
+| 1.5025 | 14600 | 1.308         | -               | -        |
+| 1.5077 | 14650 | 1.174         | -               | -        |
+| 1.5128 | 14700 | 1.2648        | -               | -        |
+| 1.5180 | 14750 | 1.2533        | -               | -        |
+| 0      | 0     | -             | -               | 0.9517   |
+| 1.5180 | 14750 | -             | 0.9885          | -        |
+| 1.5231 | 14800 | 1.2576        | -               | -        |
+| 1.5282 | 14850 | 1.3659        | -               | -        |
+| 1.5334 | 14900 | 1.298         | -               | -        |
+| 1.5385 | 14950 | 1.2723        | -               | -        |
+| 1.5437 | 15000 | 1.3099        | -               | -        |
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+| 0      | 0     | -             | -               | 0.9508   |
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+| 0      | 0     | -             | -               | 0.9506   |
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+| 2.5059 | 24350 | 1.1281        | -               | -        |
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+| 2.5162 | 24450 | 1.1784        | -               | -        |
+| 2.5214 | 24500 | 1.101         | -               | -        |
+| 0      | 0     | -             | -               | 0.9528   |
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+| 2.5316 | 24600 | 1.1364        | -               | -        |
+| 2.5368 | 24650 | 1.1778        | -               | -        |
+| 2.5419 | 24700 | 1.1089        | -               | -        |
+| 2.5471 | 24750 | 1.1626        | -               | -        |
+| 0      | 0     | -             | -               | 0.9508   |
+| 2.5471 | 24750 | -             | 1.0254          | -        |
+| 2.5522 | 24800 | 1.2019        | -               | -        |
+| 2.5574 | 24850 | 1.1503        | -               | -        |
+| 2.5625 | 24900 | 1.1697        | -               | -        |
+| 2.5677 | 24950 | 1.0921        | -               | -        |
+| 2.5728 | 25000 | 1.3136        | -               | -        |
+| 0      | 0     | -             | -               | 0.9513   |
+| 2.5728 | 25000 | -             | 1.0222          | -        |
+
+</details>
+
+### 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
+```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 +1,47 @@
+{
+  "_name_or_path": "lightonai/GTE-ModernColBERT-v1",
+  "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,
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.2",
+  "vocab_size": 50370
+}

config_sentence_transformers.json

@@ -0,0 +1,49 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.0.2",
+    "transformers": "4.48.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "MaxSim",
+  "query_prefix": "[Q] ",
+  "document_prefix": "[D] ",
+  "query_length": 32,
+  "document_length": 300,
+  "attend_to_expansion_tokens": false,
+  "skiplist_words": [
+    "!",
+    "\"",
+    "#",
+    "$",
+    "%",
+    "&",
+    "'",
+    "(",
+    ")",
+    "*",
+    "+",
+    ",",
+    "-",
+    ".",
+    "/",
+    ":",
+    ";",
+    "<",
+    "=",
+    ">",
+    "?",
+    "@",
+    "[",
+    "\\",
+    "]",
+    "^",
+    "_",
+    "`",
+    "{",
+    "|",
+    "}",
+    "~"
+  ]
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ff6b789f538b0fddf2ca7237bb215899cb474675994df0200ae481a831d3b47
+size 596076280

modules.json

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