Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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:MultipleNegativesRankingLoss
+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: How metro works?
+  sentences:
+  - How can Turing machine works?
+  - What are the best C++ books?
+  - What should I learn first in PHP?
+- source_sentence: How fast is fast?
+  sentences:
+  - How does light travel so fast?
+  - How could I become an actor?
+  - Was Muhammad a pedophile?
+- source_sentence: What is a kernel?
+  sentences:
+  - What is a tensor?
+  - What does copyright protect?
+  - Can we increase height after 23?
+- source_sentence: What is a tensor?
+  sentences:
+  - What is reliance jio?
+  - What are the reasons of war?
+  - Does speed reading really work?
+- source_sentence: Is Cicret a scam?
+  sentences:
+  - Is the Cicret Bracelet a scam?
+  - Can you eat only once a day?
+  - What books should every man read?
+pipeline_tag: sentence-similarity
+co2_eq_emissions:
+  emissions: 15.153912802318576
+  energy_consumed: 0.038985939877640395
+  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: 0.169
+  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: 0.816
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.7866689562797546
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.7285714285714286
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.735264778137207
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.6746031746031746
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.7919254658385093
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.7731120768804719
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.807
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 150.97946166992188
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.7223796033994335
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 137.3444366455078
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.6640625
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.7919254658385093
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.749212069604305
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.81
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 195.88662719726562
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.7246376811594203
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 237.68594360351562
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.6292906178489702
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.8540372670807453
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.7610544151599187
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.81
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 8.773942947387695
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.7260812581913498
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 10.843769073486328
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.6281179138321995
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.860248447204969
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.7611533877712096
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.816
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 195.88662719726562
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.7285714285714286
+      name: Max F1
+    - type: max_f1_threshold
+      value: 237.68594360351562
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.6746031746031746
+      name: Max Precision
+    - type: max_recall
+      value: 0.860248447204969
+      name: Max Recall
+    - type: max_ap
+      value: 0.7731120768804719
+      name: Max Ap
+  - task:
+      type: paraphrase-mining
+      name: Paraphrase Mining
+    dataset:
+      name: quora duplicates dev
+      type: quora-duplicates-dev
+    metrics:
+    - type: average_precision
+      value: 0.5348666252858723
+      name: Average Precision
+    - type: f1
+      value: 0.5395064090300363
+      name: F1
+    - type: precision
+      value: 0.5174549291251892
+      name: Precision
+    - type: recall
+      value: 0.5635210071439276
+      name: Recall
+    - type: threshold
+      value: 0.762035459280014
+      name: Threshold
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.9646
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9926
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9956
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9986
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9646
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.4293333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.2754
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.14515999999999998
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.830104138622815
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9609072390452685
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9808022997296821
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9934541226453286
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9795490191788223
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9789640476190478
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.971751123151301
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.9574
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.9876
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.9924
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.9978
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.9574
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.4257333333333334
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.27368000000000003
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.14468000000000003
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.8237692901379665
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.9538191510221804
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.9764249670623496
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.9918117957075603
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.9740754474178193
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.9731360317460321
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.9646398037726347
+      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-mnrl")
+# Run inference
+sentences = [
+    'Is Cicret a scam?',
+    'Is the Cicret Bracelet a scam?',
+    'Can you eat only once a day?',
+]
+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              | 0.816      |
+| cosine_accuracy_threshold    | 0.7867     |
+| cosine_f1                    | 0.7286     |
+| cosine_f1_threshold          | 0.7353     |
+| cosine_precision             | 0.6746     |
+| cosine_recall                | 0.7919     |
+| cosine_ap                    | 0.7731     |
+| dot_accuracy                 | 0.807      |
+| dot_accuracy_threshold       | 150.9795   |
+| dot_f1                       | 0.7224     |
+| dot_f1_threshold             | 137.3444   |
+| dot_precision                | 0.6641     |
+| dot_recall                   | 0.7919     |
+| dot_ap                       | 0.7492     |
+| manhattan_accuracy           | 0.81       |
+| manhattan_accuracy_threshold | 195.8866   |
+| manhattan_f1                 | 0.7246     |
+| manhattan_f1_threshold       | 237.6859   |
+| manhattan_precision          | 0.6293     |
+| manhattan_recall             | 0.854      |
+| manhattan_ap                 | 0.7611     |
+| euclidean_accuracy           | 0.81       |
+| euclidean_accuracy_threshold | 8.7739     |
+| euclidean_f1                 | 0.7261     |
+| euclidean_f1_threshold       | 10.8438    |
+| euclidean_precision          | 0.6281     |
+| euclidean_recall             | 0.8602     |
+| euclidean_ap                 | 0.7612     |
+| max_accuracy                 | 0.816      |
+| max_accuracy_threshold       | 195.8866   |
+| max_f1                       | 0.7286     |
+| max_f1_threshold             | 237.6859   |
+| max_precision                | 0.6746     |
+| max_recall                   | 0.8602     |
+| **max_ap**                   | **0.7731** |
+
+#### 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** | **0.5349** |
+| f1                    | 0.5395     |
+| precision             | 0.5175     |
+| recall                | 0.5635     |
+| threshold             | 0.762      |
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.9646     |
+| cosine_accuracy@3   | 0.9926     |
+| cosine_accuracy@5   | 0.9956     |
+| cosine_accuracy@10  | 0.9986     |
+| cosine_precision@1  | 0.9646     |
+| cosine_precision@3  | 0.4293     |
+| cosine_precision@5  | 0.2754     |
+| cosine_precision@10 | 0.1452     |
+| cosine_recall@1     | 0.8301     |
+| cosine_recall@3     | 0.9609     |
+| cosine_recall@5     | 0.9808     |
+| cosine_recall@10    | 0.9935     |
+| cosine_ndcg@10      | 0.9795     |
+| cosine_mrr@10       | 0.979      |
+| **cosine_map@100**  | **0.9718** |
+| dot_accuracy@1      | 0.9574     |
+| dot_accuracy@3      | 0.9876     |
+| dot_accuracy@5      | 0.9924     |
+| dot_accuracy@10     | 0.9978     |
+| dot_precision@1     | 0.9574     |
+| dot_precision@3     | 0.4257     |
+| dot_precision@5     | 0.2737     |
+| dot_precision@10    | 0.1447     |
+| dot_recall@1        | 0.8238     |
+| dot_recall@3        | 0.9538     |
+| dot_recall@5        | 0.9764     |
+| dot_recall@10       | 0.9918     |
+| dot_ndcg@10         | 0.9741     |
+| dot_mrr@10          | 0.9731     |
+| dot_map@100         | 0.9646     |
+
+<!--
+## 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>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 13.85 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.65 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.76 tokens</li><li>max: 64 tokens</li></ul> |
+* Samples:
+  | anchor                                                                          | positive                                                                                       | negative                                                                                                         |
+  |:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|
+  | <code>Why in India do we not have one on one political debate as in USA?</code> | <code>Why cant we have a public debate between politicians in India like the one in US?</code> | <code>Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk?</code> |
+  | <code>What is OnePlus One?</code>                                               | <code>How is oneplus one?</code>                                                               | <code>Why is OnePlus One so good?</code>                                                                         |
+  | <code>Does our mind control our emotions?</code>                                | <code>How do smart and successful people control their emotions?</code>                        | <code>How can I control my positive emotions for the people whom I love but they don't care about me?</code>     |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### 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>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                            |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 13.84 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.8 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.71 tokens</li><li>max: 56 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                   | positive                                                                         | negative                                                                                                                                                                                                                                                                          |
+  |:---------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Which programming language is best for developing low-end games?</code>                            | <code>What coding language should I learn first for making games?</code>         | <code>I am entering the world of video game programming and want to know what language I should learn? Because there are so many languages ​​I do not know which one to start with. Can you recommend a language that's easy to learn and can be used with many platforms?</code> |
+  | <code>Was it appropriate for Meryl Streep to use her Golden Globes speech to attack Donald Trump?</code> | <code>Should Meryl Streep be using her position to attack the president?</code>  | <code>Why did Kelly Ann Conway say that Meryl Streep incited peoples worst feelings?</code>                                                                                                                                                                                       |
+  | <code>Where can I found excellent commercial fridges in Sydney?</code>                                   | <code>Where can I found impressive range of commercial fridges in Sydney?</code> | <code>What is the best grocery delivery service in Sydney?</code>                                                                                                                                                                                                                 |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### 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    | -             | -      | 0.9245         | 0.4200                                 | 0.6890                  |
+| 0.0640 | 100  | 0.2535        | -      | -              | -                                      | -                       |
+| 0.1280 | 200  | 0.1732        | -      | -              | -                                      | -                       |
+| 0.1599 | 250  | -             | 0.1021 | 0.9601         | 0.5033                                 | 0.7342                  |
+| 0.1919 | 300  | 0.1465        | -      | -              | -                                      | -                       |
+| 0.2559 | 400  | 0.1186        | -      | -              | -                                      | -                       |
+| 0.3199 | 500  | 0.1159        | 0.0773 | 0.9653         | 0.5247                                 | 0.7453                  |
+| 0.3839 | 600  | 0.1088        | -      | -              | -                                      | -                       |
+| 0.4479 | 700  | 0.0993        | -      | -              | -                                      | -                       |
+| 0.4798 | 750  | -             | 0.0665 | 0.9666         | 0.5264                                 | 0.7655                  |
+| 0.5118 | 800  | 0.0952        | -      | -              | -                                      | -                       |
+| 0.5758 | 900  | 0.0799        | -      | -              | -                                      | -                       |
+| 0.6398 | 1000 | 0.0855        | 0.0570 | 0.9709         | 0.5391                                 | 0.7717                  |
+| 0.7038 | 1100 | 0.0804        | -      | -              | -                                      | -                       |
+| 0.7678 | 1200 | 0.073         | -      | -              | -                                      | -                       |
+| 0.7997 | 1250 | -             | 0.0513 | 0.9719         | 0.5329                                 | 0.7662                  |
+| 0.8317 | 1300 | 0.0741        | -      | -              | -                                      | -                       |
+| 0.8957 | 1400 | 0.0699        | -      | -              | -                                      | -                       |
+| 0.9597 | 1500 | 0.0755        | 0.0476 | 0.9718         | 0.5349                                 | 0.7731                  |
+
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 0.039 kWh
+- **Carbon Emitted**: 0.015 kg of CO2
+- **Hours Used**: 0.169 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",
+}
+```
+
+#### 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 +1,24 @@
+{
+  "_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 sha256:2b076910accfb5da8b27f844d88655b7ce78dc0429622dc19f1aae801e18346e
+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": {
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
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+    "single_word": false
+  },
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+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "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]"
+}