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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:b8b7e2fbd275234a7f2906c009aa95d0ee1d86c19d2e6ea5ad37068d75ae1491
+size 393304

README.md

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+---
+language:
+- en
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:443147
+- loss:Distillation
+base_model: artiwise-ai/modernbert-base-tr-uncased
+datasets:
+- Speedsy/msmarco-cleaned-gemini-bge-tr-uncased
+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 artiwise-ai/modernbert-base-tr-uncased
+  results:
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoDBPedia
+      type: NanoDBPedia
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.8
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.92
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.96
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 1.0
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.8
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.6733333333333333
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.6
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.548
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.08578717061354299
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.1830130267260073
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.2593375700877878
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.39135854315858964
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.6725979752170759
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.8711111111111113
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.5248067100703537
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoFiQA2018
+      type: NanoFiQA2018
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.46
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.68
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.72
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.72
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.46
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.3
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.22399999999999998
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.128
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.23257936507936505
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.4590714285714285
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.5128174603174602
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.5457063492063492
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.4798674129130085
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.5623333333333332
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.4143816306136937
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoHotpotQA
+      type: NanoHotpotQA
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.9
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 1.0
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 1.0
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 1.0
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.9
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.5133333333333333
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.32799999999999996
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.16799999999999998
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.45
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.77
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.82
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.84
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.8249212341756258
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.9466666666666668
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.7682039396944715
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoMSMARCO
+      type: NanoMSMARCO
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.46
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.62
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.7
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.82
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.46
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.20666666666666667
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.14
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.08199999999999999
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.46
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.62
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.7
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.82
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.6299271879198127
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.5706666666666667
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.5763825115906536
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoNQ
+      type: NanoNQ
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.58
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.68
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.78
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.82
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.58
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.2333333333333333
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.16399999999999998
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.088
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.57
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.67
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.75
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.8
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.6865185478036829
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.6540238095238096
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.6518842133610925
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoSCIDOCS
+      type: NanoSCIDOCS
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.42
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.6
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.64
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.8
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.42
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.2866666666666666
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.22399999999999998
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.158
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.08866666666666667
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.17766666666666667
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.2306666666666667
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.32466666666666666
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.3241741723269819
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.5367777777777778
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.24410449875234425
+      name: Maxsim Map@100
+  - task:
+      type: pylate-custom-nano-beir
+      name: Pylate Custom Nano BEIR
+    dataset:
+      name: NanoBEIR mean
+      type: NanoBEIR_mean
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.6033333333333334
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.75
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.7999999999999999
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.8599999999999999
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.6033333333333334
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.3688888888888889
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.27999999999999997
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.19533333333333333
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.3145055337265958
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.4799585203273504
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.5454702828453192
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.6202885931719342
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.6030010883926978
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.6902632275132276
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.5299605840137683
+      name: Maxsim Map@100
+---
+
+# PyLate model based on artiwise-ai/modernbert-base-tr-uncased
+
+This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [artiwise-ai/modernbert-base-tr-uncased](https://huggingface.co/artiwise-ai/modernbert-base-tr-uncased) on the [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge-tr-uncased) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
+
+## Model Details
+
+### Model Description
+- **Model Type:** PyLate model
+- **Base model:** [artiwise-ai/modernbert-base-tr-uncased](https://huggingface.co/artiwise-ai/modernbert-base-tr-uncased) <!-- at revision fe2ec5fcfd7afd1e0378d295dfd7fadfb55ea965 -->
+- **Document Length:** 180 tokens
+- **Query Length:** 32 tokens
+- **Output Dimensionality:** 128 tokens
+- **Similarity Function:** MaxSim
+- **Training Dataset:**
+    - [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge-tr-uncased)
+- **Language:** en
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
+- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
+- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
+
+### Full Model Architecture
+
+```
+ColBERT(
+  (0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+)
+```
+
+## Usage
+First install the PyLate library:
+
+```bash
+pip install -U pylate
+```
+
+### Retrieval
+
+PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
+
+#### Indexing documents
+
+First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
+
+```python
+from pylate import indexes, models, retrieve
+
+# Step 1: Load the ColBERT model
+model = models.ColBERT(
+    model_name_or_path=pylate_model_id,
+)
+
+# Step 2: Initialize the Voyager index
+index = indexes.Voyager(
+    index_folder="pylate-index",
+    index_name="index",
+    override=True,  # This overwrites the existing index if any
+)
+
+# Step 3: Encode the documents
+documents_ids = ["1", "2", "3"]
+documents = ["document 1 text", "document 2 text", "document 3 text"]
+
+documents_embeddings = model.encode(
+    documents,
+    batch_size=32,
+    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
+    show_progress_bar=True,
+)
+
+# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
+index.add_documents(
+    documents_ids=documents_ids,
+    documents_embeddings=documents_embeddings,
+)
+```
+
+Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
+
+```python
+# To load an index, simply instantiate it with the correct folder/name and without overriding it
+index = indexes.Voyager(
+    index_folder="pylate-index",
+    index_name="index",
+)
+```
+
+#### Retrieving top-k documents for queries
+
+Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
+To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
+
+```python
+# Step 1: Initialize the ColBERT retriever
+retriever = retrieve.ColBERT(index=index)
+
+# Step 2: Encode the queries
+queries_embeddings = model.encode(
+    ["query for document 3", "query for document 1"],
+    batch_size=32,
+    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
+    show_progress_bar=True,
+)
+
+# Step 3: Retrieve top-k documents
+scores = retriever.retrieve(
+    queries_embeddings=queries_embeddings,
+    k=10,  # Retrieve the top 10 matches for each query
+)
+```
+
+### Reranking
+If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
+
+```python
+from pylate import rank, models
+
+queries = [
+    "query A",
+    "query B",
+]
+
+documents = [
+    ["document A", "document B"],
+    ["document 1", "document C", "document B"],
+]
+
+documents_ids = [
+    [1, 2],
+    [1, 3, 2],
+]
+
+model = models.ColBERT(
+    model_name_or_path=pylate_model_id,
+)
+
+queries_embeddings = model.encode(
+    queries,
+    is_query=True,
+)
+
+documents_embeddings = model.encode(
+    documents,
+    is_query=False,
+)
+
+reranked_documents = rank.rerank(
+    documents_ids=documents_ids,
+    queries_embeddings=queries_embeddings,
+    documents_embeddings=documents_embeddings,
+)
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Py Late Information Retrieval
+* Dataset: `['NanoDBPedia', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNQ', 'NanoSCIDOCS']`
+* Evaluated with <code>pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator</code>
+
+| Metric              | NanoDBPedia | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNQ     | NanoSCIDOCS |
+|:--------------------|:------------|:-------------|:-------------|:------------|:-----------|:------------|
+| MaxSim_accuracy@1   | 0.8         | 0.46         | 0.9          | 0.46        | 0.58       | 0.42        |
+| MaxSim_accuracy@3   | 0.92        | 0.68         | 1.0          | 0.62        | 0.68       | 0.6         |
+| MaxSim_accuracy@5   | 0.96        | 0.72         | 1.0          | 0.7         | 0.78       | 0.64        |
+| MaxSim_accuracy@10  | 1.0         | 0.72         | 1.0          | 0.82        | 0.82       | 0.8         |
+| MaxSim_precision@1  | 0.8         | 0.46         | 0.9          | 0.46        | 0.58       | 0.42        |
+| MaxSim_precision@3  | 0.6733      | 0.3          | 0.5133       | 0.2067      | 0.2333     | 0.2867      |
+| MaxSim_precision@5  | 0.6         | 0.224        | 0.328        | 0.14        | 0.164      | 0.224       |
+| MaxSim_precision@10 | 0.548       | 0.128        | 0.168        | 0.082       | 0.088      | 0.158       |
+| MaxSim_recall@1     | 0.0858      | 0.2326       | 0.45         | 0.46        | 0.57       | 0.0887      |
+| MaxSim_recall@3     | 0.183       | 0.4591       | 0.77         | 0.62        | 0.67       | 0.1777      |
+| MaxSim_recall@5     | 0.2593      | 0.5128       | 0.82         | 0.7         | 0.75       | 0.2307      |
+| MaxSim_recall@10    | 0.3914      | 0.5457       | 0.84         | 0.82        | 0.8        | 0.3247      |
+| **MaxSim_ndcg@10**  | **0.6726**  | **0.4799**   | **0.8249**   | **0.6299**  | **0.6865** | **0.3242**  |
+| MaxSim_mrr@10       | 0.8711      | 0.5623       | 0.9467       | 0.5707      | 0.654      | 0.5368      |
+| MaxSim_map@100      | 0.5248      | 0.4144       | 0.7682       | 0.5764      | 0.6519     | 0.2441      |
+
+#### Pylate Custom Nano BEIR
+* Dataset: `NanoBEIR_mean`
+* Evaluated with <code>pylate_nano_beir_evaluator.PylateCustomNanoBEIREvaluator</code>
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| MaxSim_accuracy@1   | 0.6033    |
+| MaxSim_accuracy@3   | 0.75      |
+| MaxSim_accuracy@5   | 0.8       |
+| MaxSim_accuracy@10  | 0.86      |
+| MaxSim_precision@1  | 0.6033    |
+| MaxSim_precision@3  | 0.3689    |
+| MaxSim_precision@5  | 0.28      |
+| MaxSim_precision@10 | 0.1953    |
+| MaxSim_recall@1     | 0.3145    |
+| MaxSim_recall@3     | 0.48      |
+| MaxSim_recall@5     | 0.5455    |
+| MaxSim_recall@10    | 0.6203    |
+| **MaxSim_ndcg@10**  | **0.603** |
+| MaxSim_mrr@10       | 0.6903    |
+| MaxSim_map@100      | 0.53      |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### train
+
+* Dataset: [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge-tr-uncased) at [bd034f5](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge-tr-uncased/tree/bd034f56291b3b7a7dcde55ab0bd933977cc233e)
+* Size: 443,147 training samples
+* Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query_id                                                                        | document_ids                        | scores                              |
+  |:--------|:--------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
+  | type    | string                                                                          | list                                | list                                |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 6.21 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> |
+* Samples:
+  | query_id             | document_ids                                                              | scores                                                                                                     |
+  |:---------------------|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|
+  | <code>817836</code>  | <code>['2716076', '6741935', '2681109', '5562684', '3507339', ...]</code> | <code>[1.0, 0.7059561610221863, 0.21702419221401215, 0.38270196318626404, 0.20812414586544037, ...]</code> |
+  | <code>1045170</code> | <code>['5088671', '2953295', '8783471', '4268439', '6339935', ...]</code> | <code>[1.0, 0.6493034362792969, 0.0692221149802208, 0.17963139712810516, 0.6697239875793457, ...]</code>   |
+  | <code>1069432</code> | <code>['3724008', '314949', '8657336', '7420456', '879004', ...]</code>   | <code>[1.0, 0.3706032931804657, 0.3508036434650421, 0.2823200523853302, 0.17563475668430328, ...]</code>   |
+* Loss: <code>pylate.losses.distillation.Distillation</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `gradient_accumulation_steps`: 2
+- `learning_rate`: 3e-05
+- `num_train_epochs`: 1
+- `bf16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 8
+- `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`: 3e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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}
+- `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 | NanoDBPedia_MaxSim_ndcg@10 | NanoFiQA2018_MaxSim_ndcg@10 | NanoHotpotQA_MaxSim_ndcg@10 | NanoMSMARCO_MaxSim_ndcg@10 | NanoNQ_MaxSim_ndcg@10 | NanoSCIDOCS_MaxSim_ndcg@10 | NanoBEIR_mean_MaxSim_ndcg@10 |
+|:------:|:-----:|:-------------:|:--------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------:|:--------------------------:|:----------------------------:|
+| 0.0036 | 100   | 0.0649        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0072 | 200   | 0.0559        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0108 | 300   | 0.0518        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0144 | 400   | 0.051         | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0181 | 500   | 0.0492        | 0.6421                     | 0.3808                      | 0.7993                      | 0.5565                     | 0.5826                | 0.3050                     | 0.5444                       |
+| 0.0217 | 600   | 0.0467        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0253 | 700   | 0.0451        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0289 | 800   | 0.0443        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0325 | 900   | 0.0443        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0361 | 1000  | 0.0437        | 0.6449                     | 0.4015                      | 0.8003                      | 0.5437                     | 0.6092                | 0.3134                     | 0.5522                       |
+| 0.0397 | 1100  | 0.0433        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0433 | 1200  | 0.0427        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0469 | 1300  | 0.0414        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0505 | 1400  | 0.0417        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0542 | 1500  | 0.0418        | 0.6412                     | 0.4285                      | 0.8154                      | 0.5866                     | 0.6181                | 0.3219                     | 0.5686                       |
+| 0.0578 | 1600  | 0.0404        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0614 | 1700  | 0.0417        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0650 | 1800  | 0.0407        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0686 | 1900  | 0.0398        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0722 | 2000  | 0.0401        | 0.6499                     | 0.4354                      | 0.8150                      | 0.5610                     | 0.6445                | 0.3152                     | 0.5702                       |
+| 0.0758 | 2100  | 0.0404        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0794 | 2200  | 0.0395        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0830 | 2300  | 0.0404        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0867 | 2400  | 0.0393        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0903 | 2500  | 0.0387        | 0.6571                     | 0.4435                      | 0.8112                      | 0.5786                     | 0.6809                | 0.3232                     | 0.5824                       |
+| 0.0939 | 2600  | 0.0397        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.0975 | 2700  | 0.0393        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1011 | 2800  | 0.0384        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1047 | 2900  | 0.0382        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1083 | 3000  | 0.0381        | 0.6437                     | 0.4751                      | 0.8175                      | 0.5711                     | 0.6422                | 0.3203                     | 0.5783                       |
+| 0.1119 | 3100  | 0.0382        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1155 | 3200  | 0.0381        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1191 | 3300  | 0.0385        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1228 | 3400  | 0.0374        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1264 | 3500  | 0.0382        | 0.6437                     | 0.4833                      | 0.8282                      | 0.5955                     | 0.6436                | 0.3190                     | 0.5856                       |
+| 0.1300 | 3600  | 0.0365        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1336 | 3700  | 0.0379        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1372 | 3800  | 0.0376        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1408 | 3900  | 0.0376        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1444 | 4000  | 0.0378        | 0.6511                     | 0.4760                      | 0.8151                      | 0.5806                     | 0.6874                | 0.3140                     | 0.5874                       |
+| 0.1480 | 4100  | 0.0365        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1516 | 4200  | 0.0362        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1553 | 4300  | 0.0374        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1589 | 4400  | 0.0359        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1625 | 4500  | 0.0368        | 0.6530                     | 0.4458                      | 0.8122                      | 0.6101                     | 0.6896                | 0.3174                     | 0.5880                       |
+| 0.1661 | 4600  | 0.0356        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1697 | 4700  | 0.0364        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1733 | 4800  | 0.0352        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1769 | 4900  | 0.0357        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1805 | 5000  | 0.0366        | 0.6611                     | 0.4680                      | 0.8152                      | 0.6260                     | 0.6715                | 0.3252                     | 0.5945                       |
+| 0.1841 | 5100  | 0.0358        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1877 | 5200  | 0.0366        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1914 | 5300  | 0.0348        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1950 | 5400  | 0.036         | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.1986 | 5500  | 0.0337        | 0.6595                     | 0.4823                      | 0.8162                      | 0.6241                     | 0.6620                | 0.3216                     | 0.5943                       |
+| 0.2022 | 5600  | 0.0347        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2058 | 5700  | 0.0361        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2094 | 5800  | 0.0356        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2130 | 5900  | 0.0359        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2166 | 6000  | 0.0359        | 0.6560                     | 0.4820                      | 0.8121                      | 0.6457                     | 0.6587                | 0.3181                     | 0.5954                       |
+| 0.2202 | 6100  | 0.0347        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2239 | 6200  | 0.0355        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2275 | 6300  | 0.0356        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2311 | 6400  | 0.0351        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2347 | 6500  | 0.0351        | 0.6650                     | 0.4658                      | 0.8291                      | 0.6167                     | 0.6742                | 0.3146                     | 0.5942                       |
+| 0.2383 | 6600  | 0.0361        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2419 | 6700  | 0.0352        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2455 | 6800  | 0.0358        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2491 | 6900  | 0.0339        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2527 | 7000  | 0.0345        | 0.6600                     | 0.4700                      | 0.8413                      | 0.6449                     | 0.6862                | 0.3163                     | 0.6031                       |
+| 0.2563 | 7100  | 0.0347        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2600 | 7200  | 0.0346        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2636 | 7300  | 0.0342        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2672 | 7400  | 0.0346        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2708 | 7500  | 0.0339        | 0.6583                     | 0.4792                      | 0.8295                      | 0.6257                     | 0.6788                | 0.3204                     | 0.5986                       |
+| 0.2744 | 7600  | 0.0344        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2780 | 7700  | 0.0323        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2816 | 7800  | 0.0333        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2852 | 7900  | 0.0334        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2888 | 8000  | 0.0333        | 0.6633                     | 0.4660                      | 0.8257                      | 0.6251                     | 0.6847                | 0.3229                     | 0.5979                       |
+| 0.2925 | 8100  | 0.0337        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2961 | 8200  | 0.0339        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.2997 | 8300  | 0.0332        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3033 | 8400  | 0.0334        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3069 | 8500  | 0.0334        | 0.6744                     | 0.4791                      | 0.8204                      | 0.6139                     | 0.6654                | 0.3130                     | 0.5944                       |
+| 0.3105 | 8600  | 0.032         | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3141 | 8700  | 0.0342        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3177 | 8800  | 0.0337        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3213 | 8900  | 0.0343        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3249 | 9000  | 0.0342        | 0.6643                     | 0.4395                      | 0.8270                      | 0.6252                     | 0.6828                | 0.3146                     | 0.5922                       |
+| 0.3286 | 9100  | 0.0332        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3322 | 9200  | 0.0337        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3358 | 9300  | 0.033         | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3394 | 9400  | 0.0327        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3430 | 9500  | 0.0332        | 0.6676                     | 0.4530                      | 0.8400                      | 0.6220                     | 0.6753                | 0.3139                     | 0.5953                       |
+| 0.3466 | 9600  | 0.0315        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3502 | 9700  | 0.033         | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3538 | 9800  | 0.0331        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3574 | 9900  | 0.0341        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3610 | 10000 | 0.0327        | 0.6602                     | 0.4887                      | 0.8308                      | 0.6267                     | 0.6806                | 0.3241                     | 0.6018                       |
+| 0.3647 | 10100 | 0.0338        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3683 | 10200 | 0.0327        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3719 | 10300 | 0.0325        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3755 | 10400 | 0.0342        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3791 | 10500 | 0.034         | 0.6659                     | 0.4723                      | 0.8313                      | 0.6156                     | 0.6803                | 0.3240                     | 0.5982                       |
+| 0.3827 | 10600 | 0.0323        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3863 | 10700 | 0.0329        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3899 | 10800 | 0.0328        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3935 | 10900 | 0.0324        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.3972 | 11000 | 0.0321        | 0.6628                     | 0.4937                      | 0.8340                      | 0.6373                     | 0.6945                | 0.3268                     | 0.6082                       |
+| 0.4008 | 11100 | 0.0329        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.4044 | 11200 | 0.0329        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.4080 | 11300 | 0.0325        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.4116 | 11400 | 0.0321        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.4152 | 11500 | 0.0325        | 0.6617                     | 0.4698                      | 0.8419                      | 0.6231                     | 0.6853                | 0.3191                     | 0.6002                       |
+| 0.4188 | 11600 | 0.0327        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.4224 | 11700 | 0.0327        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.4260 | 11800 | 0.0326        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.4296 | 11900 | 0.0329        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
+| 0.4333 | 12000 | 0.0332        | 0.6559                     | 0.4860                      | 0.8324                      | 0.6160                     | 0.6966                | 0.3219                     | 0.6015                       |
+| 0.4369 | 12100 | 0.0323        | -                          | -                           | -                           | -                          | -                     | -                          | -                            |
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+
+</details>
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 4.0.2
+- PyLate: 1.2.0
+- Transformers: 4.48.2
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- 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

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+{
+  "_name_or_path": "artiwise-ai/modernbert-base-tr-uncased",
+  "architectures": [
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+  "hidden_activation": "gelu",
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+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
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+  "model_type": "modernbert",
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+  "num_hidden_layers": 22,
+  "pad_token_id": 0,
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+  "torch_dtype": "float32",
+  "transformers_version": "4.48.2",
+  "unk_token_id": 1,
+  "vocab_size": 32002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
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+    "transformers": "4.48.2",
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+}

model.safetensors

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modules.json

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

optimizer.pt

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rng_state.pth

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sentence_bert_config.json

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+  "max_seq_length": 179,
+  "do_lower_case": false
+}

special_tokens_map.json

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tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+  "tokenizer_class": "PreTrainedTokenizerFast",
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