Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+language:
+- nep
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1046
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: jangedoo/all-MiniLM-L6-v2-nepali
+widget:
+- source_sentence: राहदानीको लागि कागजात सत्यापनमा कस्तो मनोनयनपत्र चाहिन्छ?
+  sentences:
+  - सिम्यान्स अभिलेख किताबको लागि निवेदन फाराम अनुसूची-२क बमोजिमको ढाँचामा आधारित
+    हुन्छ।
+  - कुटनीतिक वा विशेष राहदानीको लागि कागजात सत्यापनमा सम्बन्धित पदमा नियुक्तिको मनोनयनपत्रको
+    प्रमाणित प्रतिलिपि चाहिन्छ।
+  - राहदानी रद्द गर्न महानिर्देशकले स्वीकृति दिन्छ।
+- source_sentence: राहदानी वितरणमा त्रुटि सच्याउन कति समय लाग्छ?
+  sentences:
+  - राहदानी नियमावली, २०७७ मा अभिलेखको गोपनीयताको उल्लङ्घनको जाँचको नतिजाको अपीलको
+    नतिजाको कार्यान्वयनको अभिलेख बाह्र वर्षसम्म राखिन्छ।
+  - राहदानी वितरणमा त्रुटि सच्याउन सामान्यतः सात कार्यदिन लाग्छ, तर प्रक्रिया जटिल
+    भएमा बढी समय लाग्न सक्छ।
+  - राहदानीको लागि निवेदनमा जाँच गर्ने अधिकारीको नाम, सही, पद, र मिति उल्लेख गर्नुपर्छ।
+- source_sentence: राहदानीको लागि निवेदनमा कस्तो आवेदन स्रोत उल्लेख गर्नुपर्छ?
+  sentences:
+  - राहदानीको लागि निवेदनमा आवेदन स्रोत (विभाग, जिल्ला, वा नियोग) उल्लेख गर्नुपर्छ।
+  - राहदानी बुझाउने प्रक्रियामा त्रुटि सच्याउन सामान्यतः सात कार्यदिन लाग्छ, तर प्रक्रिया
+    जटिल भएमा बढी समय लाग्न सक्छ।
+  - राहदानीको लिए अनलाइन निवेदनमा निकटतम व्यक्तिसँगको सम्बन्ध (Relationship) उल्लेख
+    गर्नुपर्छ।
+- source_sentence: विशेष राहदानी कसलाई जारी गरिन्छ?
+  sentences:
+  - राहदानी रद्द गर्न बाहक वा सम्बन्धित निकायको लिखित निवेदन चाहिन्छ।
+  - राहदानी नियमावली, २०७७ मा अभिलेखको गोपनीयताको उल्लङ्घनको जाँचको नतिजाको अपीलको
+    लागि जाँच गर्ने अधिकारीको नाम, सही, पद, र मिति उल्लेख गर्नुपर्छ।
+  - विशेष राहदानी नगरपालिकाका प्रमुख, सहसचिव, जिल्ला न्यायाधीश, प्रदेश लोकसेवा आयोगका
+    सदस्य, लगायतका पदाधिकारीलाई जारी गरिन्छ।
+- source_sentence: कुटनीतिक राहदानीको लागि निवेदनमा कस्तो ठेगाना विवरण चाहिन्छ?
+  sentences:
+  - कुटनीतिक राहदानीको लागि निवेदनमा जिल्ला, गाउँ/नगरपालिका, वडा नम्बर, गाउँ/सडक,
+    र घर नम्बरको ठेगाना विवरण चाहिन्छ।
+  - राहदानीको लागि कागजात धुल्याउने प्रक्रिया महानिर्देशकको स्वीकृतिमा हुन्छ।
+  - राहदानीको विद्युतीय अभिलेख अनुसूची-७ बमोजिमको ढाँचामा आधारित हुन्छ।
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: sentenceTransformer_nepali_embedding
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 384
+      type: dim_384
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.41025641025641024
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.6581196581196581
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7350427350427351
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8461538461538461
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.41025641025641024
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.21937321937321935
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14700854700854699
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0846153846153846
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.41025641025641024
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.6581196581196581
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7350427350427351
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8461538461538461
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6218282635615644
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5504409171075837
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5571750406212126
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.42735042735042733
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.6410256410256411
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.717948717948718
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8290598290598291
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.42735042735042733
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.21367521367521364
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14358974358974358
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08290598290598289
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.42735042735042733
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.6410256410256411
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.717948717948718
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8290598290598291
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6159996592171239
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5487959571292905
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5563599760664051
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.39316239316239315
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5811965811965812
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6752136752136753
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8034188034188035
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.39316239316239315
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.19373219373219372
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.135042735042735
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08034188034188033
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.39316239316239315
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5811965811965812
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6752136752136753
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8034188034188035
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5799237272193319
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5100054266720935
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5176470843483384
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.38461538461538464
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5811965811965812
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6410256410256411
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7606837606837606
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.38461538461538464
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.1937321937321937
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.12820512820512817
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07606837606837605
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.38461538461538464
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5811965811965812
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6410256410256411
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7606837606837606
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.565217766093051
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5036663953330621
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5140223584530523
+      name: Cosine Map@100
+---
+
+# sentenceTransformer_nepali_embedding
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jangedoo/all-MiniLM-L6-v2-nepali](https://huggingface.co/jangedoo/all-MiniLM-L6-v2-nepali) on the json dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [jangedoo/all-MiniLM-L6-v2-nepali](https://huggingface.co/jangedoo/all-MiniLM-L6-v2-nepali) <!-- at revision 418f7cf08ecbbc2ff0e8460bb6eb6457291102df -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+- **Language:** nep
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("ritesh-07/fine_tuned_model_02")
+# Run inference
+sentences = [
+    'कुटनीतिक राहदानीको लागि निवेदनमा कस्तो ठेगाना विवरण चाहिन्छ?',
+    'कुटनीतिक राहदानीको लागि निवेदनमा जिल्ला, गाउँ/नगरपालिका, वडा नम्बर, गाउँ/सडक, र घर नम्बरको ठेगाना विवरण चाहिन्छ।',
+    'राहदानीको लागि कागजात धुल्याउने प्रक्रिया महानिर्देशकको स्वीकृतिमा हुन्छ।',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### 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
+
+#### Information Retrieval
+
+* Dataset: `dim_384`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 384
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.4103     |
+| cosine_accuracy@3   | 0.6581     |
+| cosine_accuracy@5   | 0.735      |
+| cosine_accuracy@10  | 0.8462     |
+| cosine_precision@1  | 0.4103     |
+| cosine_precision@3  | 0.2194     |
+| cosine_precision@5  | 0.147      |
+| cosine_precision@10 | 0.0846     |
+| cosine_recall@1     | 0.4103     |
+| cosine_recall@3     | 0.6581     |
+| cosine_recall@5     | 0.735      |
+| cosine_recall@10    | 0.8462     |
+| **cosine_ndcg@10**  | **0.6218** |
+| cosine_mrr@10       | 0.5504     |
+| cosine_map@100      | 0.5572     |
+
+#### Information Retrieval
+
+* Dataset: `dim_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 256
+  }
+  ```
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| cosine_accuracy@1   | 0.4274    |
+| cosine_accuracy@3   | 0.641     |
+| cosine_accuracy@5   | 0.7179    |
+| cosine_accuracy@10  | 0.8291    |
+| cosine_precision@1  | 0.4274    |
+| cosine_precision@3  | 0.2137    |
+| cosine_precision@5  | 0.1436    |
+| cosine_precision@10 | 0.0829    |
+| cosine_recall@1     | 0.4274    |
+| cosine_recall@3     | 0.641     |
+| cosine_recall@5     | 0.7179    |
+| cosine_recall@10    | 0.8291    |
+| **cosine_ndcg@10**  | **0.616** |
+| cosine_mrr@10       | 0.5488    |
+| cosine_map@100      | 0.5564    |
+
+#### Information Retrieval
+
+* Dataset: `dim_128`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 128
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.3932     |
+| cosine_accuracy@3   | 0.5812     |
+| cosine_accuracy@5   | 0.6752     |
+| cosine_accuracy@10  | 0.8034     |
+| cosine_precision@1  | 0.3932     |
+| cosine_precision@3  | 0.1937     |
+| cosine_precision@5  | 0.135      |
+| cosine_precision@10 | 0.0803     |
+| cosine_recall@1     | 0.3932     |
+| cosine_recall@3     | 0.5812     |
+| cosine_recall@5     | 0.6752     |
+| cosine_recall@10    | 0.8034     |
+| **cosine_ndcg@10**  | **0.5799** |
+| cosine_mrr@10       | 0.51       |
+| cosine_map@100      | 0.5176     |
+
+#### Information Retrieval
+
+* Dataset: `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 64
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.3846     |
+| cosine_accuracy@3   | 0.5812     |
+| cosine_accuracy@5   | 0.641      |
+| cosine_accuracy@10  | 0.7607     |
+| cosine_precision@1  | 0.3846     |
+| cosine_precision@3  | 0.1937     |
+| cosine_precision@5  | 0.1282     |
+| cosine_precision@10 | 0.0761     |
+| cosine_recall@1     | 0.3846     |
+| cosine_recall@3     | 0.5812     |
+| cosine_recall@5     | 0.641      |
+| cosine_recall@10    | 0.7607     |
+| **cosine_ndcg@10**  | **0.5652** |
+| cosine_mrr@10       | 0.5037     |
+| cosine_map@100      | 0.514      |
+
+<!--
+## 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
+
+#### json
+
+* Dataset: json
+* Size: 1,046 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                            |
+  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                              |
+  | details | <ul><li>min: 18 tokens</li><li>mean: 40.9 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 65.74 tokens</li><li>max: 235 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                    | positive                                                                                                                        |
+  |:----------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|
+  | <code>राहदानी नियमावली, २०७७ मा अभिलेखको गोपनीयताको उल्लङ्घनको जाँचको नतिजाको अपील कसले जाँच गर्छ?</code> | <code>राहदानी नियमावली, २०७७ मा अभिलेखको गोपनीयताको उल्लङ्घनको जाँचको नतिजाको अपील मन्त्रालयले तोकेको समितिले जाँच गर्छ।</code> |
+  | <code>राहदानी नियमावली, २०७७ मा सत्यापनको लागि कस्तो सही चाहिन्छ?</code>                                  | <code>राहदानी नियमावली, २०७७ मा सत्यापनको लागि निवेदकको सही, र नाबालकको हकमा बाबु, आमा, वा संरक्षकको सही चाहिन्छ।</code>        |
+  | <code>राहदानी नियमावली, २०७७ मा कस्तो निकायले राहदानी जारी गर्छ?</code>                                   | <code>राहदानी नियमावली, २०७७ मा विभाग, नियोग, वा जिल्ला प्रशासन कार्यालयले राहदानी जारी गर्छ।</code>                            |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          384,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `tf32`: False
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 16
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-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`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: False
+- `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`: True
+- `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_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`: 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
+- `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`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch   | Step  | Training Loss | dim_384_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
+|:-------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 1.0     | 3     | -             | 0.5232                 | 0.5074                 | 0.4679                 | 0.4451                |
+| 2.0     | 6     | -             | 0.5891                 | 0.5703                 | 0.5555                 | 0.5275                |
+| **3.0** | **9** | **-**         | **0.6108**             | **0.6052**             | **0.5815**             | **0.5594**            |
+| 3.4848  | 10    | 2.5112        | -                      | -                      | -                      | -                     |
+| 4.0     | 12    | -             | 0.6218                 | 0.6160                 | 0.5799                 | 0.5652                |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.13
+- Sentence Transformers: 4.1.0
+- Transformers: 4.53.2
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.9.0
+- Datasets: 4.0.0
+- Tokenizers: 0.21.2
+
+## 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.53.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c521f7144fadf221f3347519bff63b47643e3720cf83a54c0a69a7ea0c7a8045
+size 90864192

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}