Upload SentenceTransformer model

1_Dense/config.json

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+{
+    "in_features": 768,
+    "out_features": 256,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity",
+    "use_residual": false
+}

1_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cfe7e82a7fb45682e9bea7833981943152a715798fb0179a104f5f8d2a02cce3
+size 786520

README.md

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+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- loss:CachedContrastive
+base_model: Shuu12121/OwlColBERT-v0
+pipeline_tag: sentence-similarity
+library_name: PyLate
+metrics:
+- MaxSim_accuracy@1
+- MaxSim_accuracy@3
+- MaxSim_accuracy@5
+- MaxSim_accuracy@10
+- MaxSim_precision@1
+- MaxSim_precision@3
+- MaxSim_precision@5
+- MaxSim_precision@10
+- MaxSim_recall@1
+- MaxSim_recall@3
+- MaxSim_recall@5
+- MaxSim_recall@10
+- MaxSim_ndcg@10
+- MaxSim_mrr@10
+- MaxSim_map@100
+model-index:
+- name: PyLate model based on Shuu12121/OwlColBERT-v0
+  results:
+  - task:
+      type: _chunked-col-bertir
+      name: _Chunked Col BERTIR
+    dataset:
+      name: csn s1 python
+      type: csn-s1-python
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.894
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.967
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.981
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.986
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.894
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.32233333333333325
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.1962
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.09860000000000002
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.894
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.967
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.981
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.986
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.9454028477546795
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.9317928571428575
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.932069268183084
+      name: Maxsim Map@100
+  - task:
+      type: _chunked-col-bertir
+      name: _Chunked Col BERTIR
+    dataset:
+      name: csn s1 go
+      type: csn-s1-go
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.863
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.953
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.971
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.979
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.863
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.31766666666666665
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.1942
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.0979
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.863
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.953
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.971
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.979
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.9270532703518479
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.9097051587301588
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.9110080490145706
+      name: Maxsim Map@100
+  - task:
+      type: _chunked-col-bertir
+      name: _Chunked Col BERTIR
+    dataset:
+      name: csn s1 java
+      type: csn-s1-java
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.74
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.894
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.92
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.94
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.74
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.298
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.18400000000000002
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.09400000000000001
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.74
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.894
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.92
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.94
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.8494962140513733
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.8194023809523814
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.8210505186095829
+      name: Maxsim Map@100
+  - task:
+      type: _chunked-col-bertir
+      name: _Chunked Col BERTIR
+    dataset:
+      name: csn s1 javascript
+      type: csn-s1-javascript
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.745
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.837
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.866
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.886
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.745
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.279
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.1732
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.08860000000000001
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.745
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.837
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.866
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.886
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.8174537120699362
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.7951714285714286
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.7982649229581064
+      name: Maxsim Map@100
+  - task:
+      type: _chunked-col-bertir
+      name: _Chunked Col BERTIR
+    dataset:
+      name: csn s1 php
+      type: csn-s1-php
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.769
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.813
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.816
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.824
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.769
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.27099999999999996
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.1632
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.0824
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.769
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.813
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.816
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.824
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.7998431610612987
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.7918373015873018
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.7933824555703908
+      name: Maxsim Map@100
+  - task:
+      type: _chunked-col-bertir
+      name: _Chunked Col BERTIR
+    dataset:
+      name: csn s1 ruby
+      type: csn-s1-ruby
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.849
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.932
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.944
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.954
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.849
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.31066666666666665
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.18880000000000002
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.09540000000000001
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.849
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.932
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.944
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.954
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.906791706517127
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.891051190476191
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.8921352063711024
+      name: Maxsim Map@100
+---
+
+# PyLate model based on Shuu12121/OwlColBERT-v0
+
+This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [Shuu12121/OwlColBERT-v0](https://huggingface.co/Shuu12121/OwlColBERT-v0) on the generator dataset. It maps sentences & paragraphs to sequences of 256-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:** [Shuu12121/OwlColBERT-v0](https://huggingface.co/Shuu12121/OwlColBERT-v0) <!-- at revision eac34fd44d07d5bf3de92fc2773bae3773251231 -->
+- **Document Length:** 2048 tokens
+- **Query Length:** 512 tokens
+- **Output Dimensionality:** 256 tokens
+- **Similarity Function:** MaxSim
+- **Training Dataset:**
+    - generator
+<!-- - **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': 2047, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
+  (1): Dense({'in_features': 768, 'out_features': 256, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
+)
+```
+
+## Usage
+First install the PyLate library:
+
+```bash
+pip install -U pylate
+```
+
+### Retrieval
+
+Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.
+
+#### Indexing documents
+
+Load the ColBERT model and initialize the PLAID index, then encode and index your documents:
+
+```python
+from pylate import indexes, models, retrieve
+
+# Step 1: Load the ColBERT model
+model = models.ColBERT(
+    model_name_or_path="pylate_model_id",
+)
+
+# Step 2: Initialize the PLAID index
+index = indexes.PLAID(
+    index_folder="pylate-index",
+    index_name="index",
+    override=True,  # This overwrites the existing index if any
+)
+
+# Step 3: Encode the documents
+documents_ids = ["1", "2", "3"]
+documents = ["document 1 text", "document 2 text", "document 3 text"]
+
+documents_embeddings = model.encode(
+    documents,
+    batch_size=32,
+    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
+    show_progress_bar=True,
+)
+
+# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
+index.add_documents(
+    documents_ids=documents_ids,
+    documents_embeddings=documents_embeddings,
+)
+```
+
+Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
+
+```python
+# To load an index, simply instantiate it with the correct folder/name and without overriding it
+index = indexes.PLAID(
+    index_folder="pylate-index",
+    index_name="index",
+)
+```
+
+#### Retrieving top-k documents for queries
+
+Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
+To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
+
+```python
+# Step 1: Initialize the ColBERT retriever
+retriever = retrieve.ColBERT(index=index)
+
+# Step 2: Encode the queries
+queries_embeddings = model.encode(
+    ["query for document 3", "query for document 1"],
+    batch_size=32,
+    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
+    show_progress_bar=True,
+)
+
+# Step 3: Retrieve top-k documents
+scores = retriever.retrieve(
+    queries_embeddings=queries_embeddings,
+    k=10,  # Retrieve the top 10 matches for each query
+)
+```
+
+### 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
+
+#### _Chunked Col BERTIR
+* Dataset: `['csn-s1-python', 'csn-s1-go', 'csn-s1-java', 'csn-s1-javascript', 'csn-s1-php', 'csn-s1-ruby']`
+* Evaluated with <code>__main__._ChunkedColBERTIREvaluator</code>
+
+| Metric              | csn-s1-python | csn-s1-go  | csn-s1-java | csn-s1-javascript | csn-s1-php | csn-s1-ruby |
+|:--------------------|:--------------|:-----------|:------------|:------------------|:-----------|:------------|
+| MaxSim_accuracy@1   | 0.894         | 0.863      | 0.74        | 0.745             | 0.769      | 0.849       |
+| MaxSim_accuracy@3   | 0.967         | 0.953      | 0.894       | 0.837             | 0.813      | 0.932       |
+| MaxSim_accuracy@5   | 0.981         | 0.971      | 0.92        | 0.866             | 0.816      | 0.944       |
+| MaxSim_accuracy@10  | 0.986         | 0.979      | 0.94        | 0.886             | 0.824      | 0.954       |
+| MaxSim_precision@1  | 0.894         | 0.863      | 0.74        | 0.745             | 0.769      | 0.849       |
+| MaxSim_precision@3  | 0.3223        | 0.3177     | 0.298       | 0.279             | 0.271      | 0.3107      |
+| MaxSim_precision@5  | 0.1962        | 0.1942     | 0.184       | 0.1732            | 0.1632     | 0.1888      |
+| MaxSim_precision@10 | 0.0986        | 0.0979     | 0.094       | 0.0886            | 0.0824     | 0.0954      |
+| MaxSim_recall@1     | 0.894         | 0.863      | 0.74        | 0.745             | 0.769      | 0.849       |
+| MaxSim_recall@3     | 0.967         | 0.953      | 0.894       | 0.837             | 0.813      | 0.932       |
+| MaxSim_recall@5     | 0.981         | 0.971      | 0.92        | 0.866             | 0.816      | 0.944       |
+| MaxSim_recall@10    | 0.986         | 0.979      | 0.94        | 0.886             | 0.824      | 0.954       |
+| **MaxSim_ndcg@10**  | **0.9454**    | **0.9271** | **0.8495**  | **0.8175**        | **0.7998** | **0.9068**  |
+| MaxSim_mrr@10       | 0.9318        | 0.9097     | 0.8194      | 0.7952            | 0.7918     | 0.8911      |
+| MaxSim_map@100      | 0.9321        | 0.911      | 0.8211      | 0.7983            | 0.7934     | 0.8921      |
+
+<!--
+## 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
+
+#### generator
+
+* Dataset: generator
+* Columns: <code>query</code>, <code>document</code>, <code>negative_0</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, <code>negative_5</code>, <code>negative_6</code>, <code>negative_7</code>, <code>negative_8</code>, <code>negative_9</code>, <code>negative_10</code>, <code>negative_11</code>, <code>negative_12</code>, <code>negative_13</code>, <code>negative_14</code>, <code>negative_15</code>, <code>negative_16</code>, <code>negative_17</code>, <code>negative_18</code>, <code>negative_19</code>, <code>negative_20</code>, <code>negative_21</code>, <code>negative_22</code>, <code>negative_23</code>, <code>negative_24</code>, <code>negative_25</code>, <code>negative_26</code>, <code>negative_27</code>, <code>negative_28</code>, <code>negative_29</code>, <code>negative_30</code>, <code>negative_31</code>, <code>negative_32</code>, <code>negative_33</code>, <code>negative_34</code>, <code>negative_35</code>, <code>negative_36</code>, <code>negative_37</code>, <code>negative_38</code>, <code>negative_39</code>, <code>negative_40</code>, <code>negative_41</code>, <code>negative_42</code>, <code>negative_43</code>, <code>negative_44</code>, <code>negative_45</code>, <code>negative_46</code>, <code>negative_47</code>, <code>negative_48</code>, <code>negative_49</code>, <code>negative_50</code>, <code>negative_51</code>, <code>negative_52</code>, <code>negative_53</code>, <code>negative_54</code>, <code>negative_55</code>, <code>negative_56</code>, <code>negative_57</code>, <code>negative_58</code>, <code>negative_59</code>, <code>negative_60</code>, <code>negative_61</code>, <code>negative_62</code>, <code>negative_63</code>, <code>negative_64</code>, <code>negative_65</code>, <code>negative_66</code>, <code>negative_67</code>, <code>negative_68</code>, <code>negative_69</code>, <code>negative_70</code>, <code>negative_71</code>, <code>negative_72</code>, <code>negative_73</code>, <code>negative_74</code>, <code>negative_75</code>, <code>negative_76</code>, <code>negative_77</code>, <code>negative_78</code>, <code>negative_79</code>, <code>negative_80</code>, <code>negative_81</code>, <code>negative_82</code>, <code>negative_83</code>, <code>negative_84</code>, <code>negative_85</code>, <code>negative_86</code>, <code>negative_87</code>, <code>negative_88</code>, <code>negative_89</code>, <code>negative_90</code>, <code>negative_91</code>, <code>negative_92</code>, <code>negative_93</code>, <code>negative_94</code>, <code>negative_95</code>, <code>negative_96</code>, <code>negative_97</code>, <code>negative_98</code>, <code>negative_99</code>, and <code>negative_scores</code>
+* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 120
+- `per_device_eval_batch_size`: 120
+- `learning_rate`: 3e-06
+- `weight_decay`: 0.01
+- `max_steps`: 3000
+- `lr_scheduler_type`: cosine
+- `bf16`: True
+- `gradient_checkpointing`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 120
+- `per_device_eval_batch_size`: 120
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 3e-06
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3.0
+- `max_steps`: 3000
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `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
+- `hub_revision`: None
+- `gradient_checkpointing`: True
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | csn-s1-python_MaxSim_ndcg@10 | csn-s1-go_MaxSim_ndcg@10 | csn-s1-java_MaxSim_ndcg@10 | csn-s1-javascript_MaxSim_ndcg@10 | csn-s1-php_MaxSim_ndcg@10 | csn-s1-ruby_MaxSim_ndcg@10 |
+|:------:|:----:|:-------------:|:----------------------------:|:------------------------:|:--------------------------:|:--------------------------------:|:-------------------------:|:--------------------------:|
+| 0.0333 | 100  | 0.0728        | 0.9446                       | 0.9211                   | 0.8530                     | 0.8031                           | 0.8018                    | 0.9055                     |
+| 0.0667 | 200  | 0.0644        | 0.9452                       | 0.9232                   | 0.8506                     | 0.8078                           | 0.8004                    | 0.9060                     |
+| 0.1    | 300  | 0.0602        | 0.9446                       | 0.9250                   | 0.8501                     | 0.8114                           | 0.8018                    | 0.9050                     |
+| 0.1333 | 400  | 0.0581        | 0.9446                       | 0.9247                   | 0.8513                     | 0.8125                           | 0.8014                    | 0.9056                     |
+| 0.1667 | 500  | 0.0632        | 0.9440                       | 0.9256                   | 0.8509                     | 0.8122                           | 0.8001                    | 0.9026                     |
+| 0.2    | 600  | 0.0529        | 0.9448                       | 0.9265                   | 0.8523                     | 0.8138                           | 0.8012                    | 0.9049                     |
+| 0.2333 | 700  | 0.042         | 0.9459                       | 0.9248                   | 0.8525                     | 0.8150                           | 0.8007                    | 0.9048                     |
+| 0.2667 | 800  | 0.0608        | 0.9470                       | 0.9268                   | 0.8519                     | 0.8160                           | 0.8006                    | 0.9057                     |
+| 0.3    | 900  | 0.0542        | 0.9463                       | 0.9263                   | 0.8513                     | 0.8159                           | 0.8009                    | 0.9054                     |
+| 0.3333 | 1000 | 0.044         | 0.9454                       | 0.9278                   | 0.8495                     | 0.8166                           | 0.8002                    | 0.9042                     |
+| 0.3667 | 1100 | 0.0673        | 0.9451                       | 0.9281                   | 0.8512                     | 0.8175                           | 0.8005                    | 0.9040                     |
+| 0.4    | 1200 | 0.0476        | 0.9434                       | 0.9275                   | 0.8493                     | 0.8171                           | 0.8025                    | 0.9053                     |
+| 0.4333 | 1300 | 0.0528        | 0.9439                       | 0.9273                   | 0.8506                     | 0.8185                           | 0.8003                    | 0.9043                     |
+| 0.4667 | 1400 | 0.0546        | 0.9441                       | 0.9277                   | 0.8516                     | 0.8183                           | 0.8004                    | 0.9062                     |
+| 0.5    | 1500 | 0.0497        | 0.9448                       | 0.9275                   | 0.8498                     | 0.8172                           | 0.8008                    | 0.9080                     |
+| 0.5333 | 1600 | 0.028         | 0.9453                       | 0.9268                   | 0.8498                     | 0.8188                           | 0.8004                    | 0.9063                     |
+| 0.5667 | 1700 | 0.0367        | 0.9451                       | 0.9274                   | 0.8497                     | 0.8189                           | 0.7996                    | 0.9067                     |
+| 0.6    | 1800 | 0.0506        | 0.9457                       | 0.9273                   | 0.8481                     | 0.8192                           | 0.7988                    | 0.9064                     |
+| 0.6333 | 1900 | 0.0296        | 0.9453                       | 0.9270                   | 0.8478                     | 0.8193                           | 0.7988                    | 0.9059                     |
+| 0.6667 | 2000 | 0.0561        | 0.9447                       | 0.9271                   | 0.8504                     | 0.8195                           | 0.7994                    | 0.9065                     |
+| 0.7    | 2100 | 0.0338        | 0.9456                       | 0.9268                   | 0.8503                     | 0.8195                           | 0.7992                    | 0.9064                     |
+| 0.7333 | 2200 | 0.0488        | 0.9450                       | 0.9271                   | 0.8503                     | 0.8188                           | 0.7992                    | 0.9063                     |
+| 0.7667 | 2300 | 0.04          | 0.9462                       | 0.9272                   | 0.8494                     | 0.8188                           | 0.7996                    | 0.9064                     |
+| 0.8    | 2400 | 0.0539        | 0.9460                       | 0.9272                   | 0.8509                     | 0.8178                           | 0.7992                    | 0.9064                     |
+| 0.8333 | 2500 | 0.0487        | 0.9447                       | 0.9272                   | 0.8505                     | 0.8175                           | 0.7992                    | 0.9072                     |
+| 0.8667 | 2600 | 0.0474        | 0.9454                       | 0.9272                   | 0.8505                     | 0.8174                           | 0.7996                    | 0.9069                     |
+| 0.9    | 2700 | 0.0325        | 0.9451                       | 0.9272                   | 0.8506                     | 0.8174                           | 0.7996                    | 0.9064                     |
+| 0.9333 | 2800 | 0.0636        | 0.9457                       | 0.9272                   | 0.8507                     | 0.8174                           | 0.7992                    | 0.9069                     |
+| 0.9667 | 2900 | 0.047         | 0.9457                       | 0.9272                   | 0.8506                     | 0.8174                           | 0.7997                    | 0.9069                     |
+| 1.0    | 3000 | 0.0516        | 0.9454                       | 0.9271                   | 0.8495                     | 0.8175                           | 0.7998                    | 0.9068                     |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 5.3.0
+- PyLate: 1.4.0
+- Transformers: 4.56.2
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.12.0
+- Datasets: 3.6.0
+- Tokenizers: 0.22.1
+
+
+## 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
+@inproceedings{DBLP:conf/cikm/ChaffinS25,
+  author       = {Antoine Chaffin and
+                  Rapha{"{e}}l Sourty},
+  editor       = {Meeyoung Cha and
+                  Chanyoung Park and
+                  Noseong Park and
+                  Carl Yang and
+                  Senjuti Basu Roy and
+                  Jessie Li and
+                  Jaap Kamps and
+                  Kijung Shin and
+                  Bryan Hooi and
+                  Lifang He},
+  title        = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
+  booktitle    = {Proceedings of the 34th {ACM} International Conference on Information
+                  and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
+                  10-14, 2025},
+  pages        = {6334--6339},
+  publisher    = {{ACM}},
+  year         = {2025},
+  url          = {https://github.com/lightonai/pylate},
+  doi          = {10.1145/3746252.3761608},
+}
+```
+
+#### 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

@@ -0,0 +1,47 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_activation": "gelu",
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+  "classifier_pooling": "cls",
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+  "decoder_bias": true,
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+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": null,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "hidden_activation": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "local_attention": 128,
+  "local_attention_rope_theta": 10000,
+  "local_attention_window": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
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+  "norm_eps": 1e-05,
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+  "num_hidden_layers": 19,
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+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.56.2",
+  "type_vocab_size": 2,
+  "vocab_size": 50370
+}

config_sentence_transformers.json

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+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.3.0",
+    "transformers": "4.56.2",
+    "pytorch": "2.8.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "MaxSim",
+  "query_prefix": "[Q] ",
+  "document_prefix": "[D] ",
+  "query_length": 512,
+  "document_length": 2048,
+  "attend_to_expansion_tokens": false,
+  "skiplist_words": [
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+    "\u010a\u010a\u010a\u010a\u010a",
+    "         ",
+    "                 ",
+    "\u010a",
+    "\u010a\u010a\u010a\u010a",
+    " ",
+    "               ",
+    "                           ",
+    "  ",
+    "\u010a\u010a\u010a",
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+    "        ",
+    "\u010d",
+    "\u010d\u010a\u010a",
+    "                             ",
+    "                       ",
+    "                   ",
+    "                     ",
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+    "\u0109",
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+    "     ",
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+    "                        ",
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+    "\t",
+    "\u010d\u010a",
+    "                              ",
+    "                         ",
+    "                            ",
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+    "\t\t",
+    "\u010a\u010a\u010a\u010a\u010a\u010a\u010a\u010a",
+    "          ",
+    "\t\t\t",
+    "       ",
+    "                          ",
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+    "                      ",
+    "            ",
+    "                    ",
+    "                               ",
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+  ],
+  "do_query_expansion": true
+}

model.safetensors

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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": 2047,
+    "do_lower_case": false
+}

special_tokens_map.json

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+    "[Code2]",
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+  }
+}

tokenizer_config.json

@@ -0,0 +1,3376 @@
+{
+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+}