Add new SentenceTransformer model.

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "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:
+- en
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- loss:OnlineContrastiveLoss
+base_model: sentence-transformers/stsb-distilbert-base
+metrics:
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- dot_accuracy
+- dot_accuracy_threshold
+- dot_f1
+- dot_f1_threshold
+- dot_precision
+- dot_recall
+- dot_ap
+- manhattan_accuracy
+- manhattan_accuracy_threshold
+- manhattan_f1
+- manhattan_f1_threshold
+- manhattan_precision
+- manhattan_recall
+- manhattan_ap
+- euclidean_accuracy
+- euclidean_accuracy_threshold
+- euclidean_f1
+- euclidean_f1_threshold
+- euclidean_precision
+- euclidean_recall
+- euclidean_ap
+- max_accuracy
+- max_accuracy_threshold
+- max_f1
+- max_f1_threshold
+- max_precision
+- max_recall
+- max_ap
+- average_precision
+- f1
+- precision
+- recall
+- threshold
+- 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
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+widget:
+- source_sentence: Why did he go MIA?
+  sentences:
+  - Why did Yahoo kill Konfabulator?
+  - Why do people get angry with me?
+  - What are the best waterproof guns?
+- source_sentence: Who is a soulmate?
+  sentences:
+  - Is she the “One”?
+  - Who is Pakistan's biggest enemy?
+  - Will smoking weed help with my anxiety?
+- source_sentence: Is this poem good?
+  sentences:
+  - Is my poem any good?
+  - How can I become a good speaker?
+  - What is feminism?
+- source_sentence: Who invented Yoga?
+  sentences:
+  - How was yoga invented?
+  - Who owns this number 3152150252?
+  - What is Dynamics CRM Services?
+- source_sentence: Is stretching bad?
+  sentences:
+  - Is stretching good for you?
+  - If i=0; what will i=i++ do to i?
+  - What is the Output of this C program ?
+pipeline_tag: sentence-similarity
+co2_eq_emissions:
+  emissions: 15.707175691967695
+  energy_consumed: 0.040409299905757354
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 0.202
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+model-index:
+- name: SentenceTransformer based on sentence-transformers/stsb-distilbert-base
+  results:
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: quora duplicates
+      type: quora-duplicates
+    metrics:
+    - type: cosine_accuracy
+      value: 0.86
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.8104104995727539
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.8250591016548463
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.7247534394264221
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.7347368421052631
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.9407008086253369
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.887247904332921
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.828
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 157.35491943359375
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.7898550724637681
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 145.7113037109375
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.7155361050328227
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.8814016172506739
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.8369433397850002
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.868
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 208.00347900390625
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.8307692307692308
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 208.00347900390625
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.7921760391198044
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.8733153638814016
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.8868217413983182
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.867
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 9.269388198852539
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.8301404853128991
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 9.525729179382324
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.7888349514563107
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.876010781671159
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.8884154240019244
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.868
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 208.00347900390625
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.8307692307692308
+      name: Max F1
+    - type: max_f1_threshold
+      value: 208.00347900390625
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.7921760391198044
+      name: Max Precision
+    - type: max_recall
+      value: 0.9407008086253369
+      name: Max Recall
+    - type: max_ap
+      value: 0.8884154240019244
+      name: Max Ap
+  - task:
+      type: paraphrase-mining
+      name: Paraphrase Mining
+    dataset:
+      name: quora duplicates dev
+      type: quora-duplicates-dev
+    metrics:
+    - type: average_precision
+      value: 0.534436244125929
+      name: Average Precision
+    - type: f1
+      value: 0.5447997274541295
+      name: F1
+    - type: precision
+      value: 0.5311002514589362
+      name: Precision
+    - type: recall
+      value: 0.5592246590398161
+      name: Recall
+    - type: threshold
+      value: 0.8626040816307068
+      name: Threshold
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.928
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9712
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9782
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9874
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.928
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.4151333333333334
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.26656
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.14166
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7993523853760618
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9341884771405065
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9560896250710075
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9766088525134997
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9516150309696244
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9509392857142857
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9390263696194139
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.8926
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.9518
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.9658
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.9768
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.8926
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.40273333333333333
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.26076
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.13882
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.7679620996617761
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.9105756956997251
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.9402185219519044
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.9623418143294613
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.9263520741106431
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.9243020634920638
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.9094019438194247
+      name: Dot Map@100
+---
+
+# SentenceTransformer based on sentence-transformers/stsb-distilbert-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) on the [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) dataset. It maps sentences & paragraphs to a 768-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:** [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) <!-- at revision 82ad392c08f81be9be9bf065339670b23f2e1493 -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
+- **Language:** en
+<!-- - **License:** Unknown -->
+
+### 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': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, '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})
+)
+```
+
+## 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("tomaarsen/stsb-distilbert-base-ocl")
+# Run inference
+sentences = [
+    'Is stretching bad?',
+    'Is stretching good for you?',
+    'If i=0; what will i=i++ do to i?',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(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
+
+#### Binary Classification
+* Dataset: `quora-duplicates`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                       | Value      |
+|:-----------------------------|:-----------|
+| cosine_accuracy              | 0.86       |
+| cosine_accuracy_threshold    | 0.8104     |
+| cosine_f1                    | 0.8251     |
+| cosine_f1_threshold          | 0.7248     |
+| cosine_precision             | 0.7347     |
+| cosine_recall                | 0.9407     |
+| cosine_ap                    | 0.8872     |
+| dot_accuracy                 | 0.828      |
+| dot_accuracy_threshold       | 157.3549   |
+| dot_f1                       | 0.7899     |
+| dot_f1_threshold             | 145.7113   |
+| dot_precision                | 0.7155     |
+| dot_recall                   | 0.8814     |
+| dot_ap                       | 0.8369     |
+| manhattan_accuracy           | 0.868      |
+| manhattan_accuracy_threshold | 208.0035   |
+| manhattan_f1                 | 0.8308     |
+| manhattan_f1_threshold       | 208.0035   |
+| manhattan_precision          | 0.7922     |
+| manhattan_recall             | 0.8733     |
+| manhattan_ap                 | 0.8868     |
+| euclidean_accuracy           | 0.867      |
+| euclidean_accuracy_threshold | 9.2694     |
+| euclidean_f1                 | 0.8301     |
+| euclidean_f1_threshold       | 9.5257     |
+| euclidean_precision          | 0.7888     |
+| euclidean_recall             | 0.876      |
+| euclidean_ap                 | 0.8884     |
+| max_accuracy                 | 0.868      |
+| max_accuracy_threshold       | 208.0035   |
+| max_f1                       | 0.8308     |
+| max_f1_threshold             | 208.0035   |
+| max_precision                | 0.7922     |
+| max_recall                   | 0.9407     |
+| **max_ap**                   | **0.8884** |
+
+#### Paraphrase Mining
+* Dataset: `quora-duplicates-dev`
+* Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator)
+
+| Metric                | Value      |
+|:----------------------|:-----------|
+| **average_precision** | **0.5344** |
+| f1                    | 0.5448     |
+| precision             | 0.5311     |
+| recall                | 0.5592     |
+| threshold             | 0.8626     |
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| cosine_accuracy@1   | 0.928     |
+| cosine_accuracy@3   | 0.9712    |
+| cosine_accuracy@5   | 0.9782    |
+| cosine_accuracy@10  | 0.9874    |
+| cosine_precision@1  | 0.928     |
+| cosine_precision@3  | 0.4151    |
+| cosine_precision@5  | 0.2666    |
+| cosine_precision@10 | 0.1417    |
+| cosine_recall@1     | 0.7994    |
+| cosine_recall@3     | 0.9342    |
+| cosine_recall@5     | 0.9561    |
+| cosine_recall@10    | 0.9766    |
+| cosine_ndcg@10      | 0.9516    |
+| cosine_mrr@10       | 0.9509    |
+| **cosine_map@100**  | **0.939** |
+| dot_accuracy@1      | 0.8926    |
+| dot_accuracy@3      | 0.9518    |
+| dot_accuracy@5      | 0.9658    |
+| dot_accuracy@10     | 0.9768    |
+| dot_precision@1     | 0.8926    |
+| dot_precision@3     | 0.4027    |
+| dot_precision@5     | 0.2608    |
+| dot_precision@10    | 0.1388    |
+| dot_recall@1        | 0.768     |
+| dot_recall@3        | 0.9106    |
+| dot_recall@5        | 0.9402    |
+| dot_recall@10       | 0.9623    |
+| dot_ndcg@10         | 0.9264    |
+| dot_mrr@10          | 0.9243    |
+| dot_map@100         | 0.9094    |
+
+<!--
+## 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
+
+#### sentence-transformers/quora-duplicates
+
+* Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
+* Size: 100,000 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                         | label                                           |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                           | string                                                                            | int                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 15.5 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.46 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>0: ~64.10%</li><li>1: ~35.90%</li></ul> |
+* Samples:
+  | sentence1                                                                                          | sentence2                                                                         | label          |
+  |:---------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------|
+  | <code>What are the best ecommerce blogs to do guest posts on about SEO to gain new clients?</code> | <code>Interested in being a guest blogger for an ecommerce marketing blog?</code> | <code>0</code> |
+  | <code>How do I learn Informatica online training?</code>                                           | <code>What is Informatica online training?</code>                                 | <code>0</code> |
+  | <code>What effects does marijuana use have on the flu?</code>                                      | <code>What effects does Marijuana use have on the common cold?</code>             | <code>0</code> |
+* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/losses.html#onlinecontrastiveloss)
+
+### Evaluation Dataset
+
+#### sentence-transformers/quora-duplicates
+
+* Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
+* Size: 1,000 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | label                                           |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | int                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 15.82 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.91 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~62.90%</li><li>1: ~37.10%</li></ul> |
+* Samples:
+  | sentence1                                             | sentence2                                          | label          |
+  |:------------------------------------------------------|:---------------------------------------------------|:---------------|
+  | <code>How should I prepare for JEE Mains 2017?</code> | <code>How do I prepare for the JEE 2016?</code>    | <code>0</code> |
+  | <code>What is the gate exam?</code>                   | <code>What is the GATE exam in engineering?</code> | <code>0</code> |
+  | <code>Where do IRS officers get posted?</code>        | <code>Does IRS Officers get posted abroad?</code>  | <code>0</code> |
+* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/losses.html#onlinecontrastiveloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: False
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 5e-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`: 0.1
+- `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
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: 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`: None
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   | cosine_map@100 | quora-duplicates-dev_average_precision | quora-duplicates_max_ap |
+|:------:|:----:|:-------------:|:------:|:--------------:|:--------------------------------------:|:-----------------------:|
+| 0      | 0    | -             | -      | 0.9235         | 0.4200                                 | 0.7276                  |
+| 0.0640 | 100  | 2.5123        | -      | -              | -                                      | -                       |
+| 0.1280 | 200  | 2.0534        | -      | -              | -                                      | -                       |
+| 0.1599 | 250  | -             | 1.7914 | 0.9127         | 0.4082                                 | 0.8301                  |
+| 0.1919 | 300  | 1.9505        | -      | -              | -                                      | -                       |
+| 0.2559 | 400  | 1.9836        | -      | -              | -                                      | -                       |
+| 0.3199 | 500  | 1.8462        | 1.5923 | 0.9190         | 0.4445                                 | 0.8688                  |
+| 0.3839 | 600  | 1.7734        | -      | -              | -                                      | -                       |
+| 0.4479 | 700  | 1.7918        | -      | -              | -                                      | -                       |
+| 0.4798 | 750  | -             | 1.5461 | 0.9291         | 0.4943                                 | 0.8707                  |
+| 0.5118 | 800  | 1.6157        | -      | -              | -                                      | -                       |
+| 0.5758 | 900  | 1.7244        | -      | -              | -                                      | -                       |
+| 0.6398 | 1000 | 1.7322        | 1.5294 | 0.9309         | 0.5048                                 | 0.8808                  |
+| 0.7038 | 1100 | 1.6825        | -      | -              | -                                      | -                       |
+| 0.7678 | 1200 | 1.6823        | -      | -              | -                                      | -                       |
+| 0.7997 | 1250 | -             | 1.4812 | 0.9351         | 0.5126                                 | 0.8865                  |
+| 0.8317 | 1300 | 1.5707        | -      | -              | -                                      | -                       |
+| 0.8957 | 1400 | 1.6145        | -      | -              | -                                      | -                       |
+| 0.9597 | 1500 | 1.5795        | 1.4705 | 0.9390         | 0.5344                                 | 0.8884                  |
+
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 0.040 kWh
+- **Carbon Emitted**: 0.016 kg of CO2
+- **Hours Used**: 0.202 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+
+### Framework Versions
+- Python: 3.11.6
+- Sentence Transformers: 3.0.0.dev0
+- Transformers: 4.41.0.dev0
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.26.1
+- Datasets: 2.18.0
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+<!--
+## 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 +1,24 @@
+{
+  "_name_or_path": "sentence-transformers/stsb-distilbert-base",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertModel"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.0.dev0",
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+oid sha256:4d03b2524152e39f65f10176d2e5fa7b0f261cf9b9a1e7f66c3d49829099318c
+size 265462608

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "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": {
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+    "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 +1,58 @@
+{
+  "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": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": false,
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+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "full_tokenizer_file": null,
+  "mask_token": "[MASK]",
+  "model_max_length": 128,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}