Add new SentenceTransformer model

1_Pooling/config.json

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+{
+    "embedding_dimension": 768,
+    "pooling_mode": "cls",
+    "include_prompt": true
+}

README.md

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+---
+language:
+- en
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:20000
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: BAAI/bge-base-en-v1.5
+widget:
+- source_sentence: when does star trek discovery show on tv
+  sentences:
+  - 'Star Trek: Discovery Star Trek: Discovery premiered on September 19, 2017, at
+    ArcLight Hollywood, before debuting on CBS and All Access on September 24. The
+    rest of the 15-episode first season are streaming weekly on All Access. The series''
+    release led to record subscriptions for All Access, and positive reviews from
+    critics who highlighted Martin-Green''s performance. A second season was ordered
+    in October 2017.'
+  - Foster's Home for Imaginary Friends McCracken conceived the series after adopting
+    two dogs from an animal shelter and applying the concept to imaginary friends.
+    The show first premiered on Cartoon Network on August 13, 2004, as a 90-minute
+    television film. On August 20, it began its normal run of twenty-to-thirty-minute
+    episodes on Fridays, at 7 pm. The series finished its run on May 3, 2009, with
+    a total of six seasons and seventy-nine episodes. McCracken left Cartoon Network
+    shortly after the series ended. Reruns have aired on Boomerang from August 11,
+    2012 to November 3, 2013 and again from June 1, 2014 to April 3, 2017.
+  - 2017 ICC Champions Trophy England became the first team to qualify for the semi-finals
+    by virtue of two wins in its first two Group A games, and with other teams of
+    the group either losing a game or ending games without a result.[36] Bangladesh
+    qualified for the semi-finals following their win against New Zealand, and Australia
+    failing to beat England in the final match of Group A.[37] From Group B, India
+    and Pakistan qualified for the semi-finals following victories in their final
+    group matches against South Africa and Sri Lanka respectively.[38][39]
+- source_sentence: what was the earlier name of the times of india
+  sentences:
+  - The Times of India The Times of India issued its first edition on 3 November 1838
+    as The Bombay Times and Journal of Commerce.[10][11] The paper published Wednesdays
+    and Saturdays under the direction of Raobahadur Narayan Dinanath Velkar, a Maharashtrian
+    Reformist, and contained news from Britain and the world, as well as the Indian
+    Subcontinent. J.E. Brennan was its first editor.[12][13] In 1850, it began to
+    publish daily editions.
+  - The Joy Luck Club (novel) The Joy Luck Club is a 1989 novel written by Amy Tan.
+    It focuses on four Chinese American immigrant families in San Francisco who start
+    a club known as The Joy Luck Club, playing the Chinese game of mahjong for money
+    while feasting on a variety of foods. The book is structured somewhat like a mahjong
+    game, with four parts divided into four sections to create sixteen chapters. The
+    three mothers and four daughters (one mother, Suyuan Woo, dies before the novel
+    opens) share stories about their lives in the form of vignettes. Each part is
+    preceded by a parable relating to the game.
+  - Where Are You Going, Where Have You Been? The story was originally named "Death
+    and the Maiden".[citation needed] The main character of Oates' story is Connie,
+    a beautiful, self-absorbed 15-year-old girl, who is at odds with her mother—once
+    a beauty herself—and with her dutiful, "steady", and homely older sister. Without
+    her parents' knowledge, she spends most of her evenings picking up boys at a Big
+    Boy restaurant, and one evening captures the attention of a stranger in a gold
+    convertible covered with cryptic writing. While her parents are away at her aunt's
+    barbecue, two men pull up in front of Connie's house and call her out. She recognizes
+    the driver, Arnold Friend, as the man from the drive-in restaurant, and is initially
+    charmed by the smooth-talking, charismatic stranger. He tells Connie he is 18
+    and has come to take her for a ride in his car with his sidekick Ellie. Connie
+    slowly realizes that he is actually much older,[4] and grows afraid. When she
+    refuses to go with them, Friend becomes more forceful and threatening, saying
+    that he will harm her family, while at the same time appealing to her vanity,
+    saying that she is too good for them. Connie is compelled to leave with him and
+    do what he demands of her.
+- source_sentence: world englishes definition
+  sentences:
+  - 'How to Prevent Appendicitis: A Summary. Common symptoms of appendicitis include:
+    1  Pain in the abdomen, first around the belly button, then moving to the lower
+    right area. 2  Loss of appetite. 3  Nausea. 4  Vomiting. 5  Constipation or diarrhea.
+    6  Inability to pass gas. 7  Low fever that begins after other symptoms. 8  Abdominal
+    swelling.lthough there is no way to prevent appendicitis, people who are able
+    to recognize appendicitis symptoms may be able to prevent more serious appendicitis
+    symptoms from occurring.'
+  - "This conceptualization of world Englishes is a socially realistic approach to\
+    \ the observed global spread and functions of English within the Three Circles\
+    \ model (Kachru, 1985: 12â\x80\x9313)."
+  - By Richard Nordquist. Definition. The English language as it is variously used
+    throughout the world. Sometimes called Word Englishes. Also known as international
+    English and Global English. The English language is now spoken in more than 100
+    countries.
+- source_sentence: definition of law of cosines
+  sentences:
+  - The term thagomizer was coined by Gary Larson in humor, in a 1982 Far Side comic,
+    in which a group of cavemen in a faux-modern lecture hall are taught by their
+    caveman professor that the spikes on a stegosaur's tail are so named after the
+    late Thag Simmons.
+  - Definition of Law of Cosines. Law of Cosines is an equation relating the lengths
+    of the sides of a cosine of one of its angles. More About Law of Cosines. Law
+    of cosines is also called as cosine rule or cosine formula.
+  - The law of cosines is useful for computing the third side of a triangle when two
+    sides and their enclosed angle are known, and in computing the angles of a triangle
+    if all three sides are known.
+- source_sentence: which act requires manufacturers to disclose the origins of their
+    minerals?
+  sentences:
+  - Gastric balloon is a reversible, incision free weight loss procedure. There is
+    one such device approved in the U.S.  the ReShape Integrated Dual Balloon System  but
+    gastric balloons have been approved and used for years in some parts of Europe,
+    as well as Canada, Australia, Mexico and South America.dvocates believe that it
+    can still result in lasting and ongoing weight loss after removal because it helps
+    exact behavioral changes; their theory holds that people get used to eating smaller
+    portions of food and are therefore more inclined to continue to eat in this manner
+    long after the balloon is removed.
+  - "In 2010, Congress passed the Dodd-Frank Act, which directs the Commission to\
+    \ issue rules requiring certain companies to disclose their use of conflict minerals\
+    \ if those minerals are â\x80\x9Cnecessary to the functionality or production\
+    \ of a productâ\x80\x9D manufactured by those companies. Under the Act, those\
+    \ minerals include tantalum, tin, gold or tungsten."
+  - "International efforts to reduce trade in conflict resources, tried to reduce\
+    \ incentives to extract and fight over them. For example, in the United States,\
+    \ the 2010 Doddâ\x80\x93Frank Wall Street Reform and Consumer Protection Act required\
+    \ manufacturers to audit their supply chains and report use of conflict minerals.\
+    \ In 2015 a US federal appeals court struck down some aspects of the reporting\
+    \ requirements as a violation of corporationsâ\x80\x99 freedom of speech, but\
+    \ left others in place."
+datasets:
+- sentence-transformers/msmarco-bm25
+- sentence-transformers/natural-questions
+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 based on BAAI/bge-base-en-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: NanoMSMARCO
+      type: NanoMSMARCO
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.46
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.66
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.72
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.82
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.46
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.22
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14400000000000002
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08199999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.46
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.66
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.72
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.82
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6315831525195733
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5724444444444444
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.582524009616115
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: NanoNQ
+      type: NanoNQ
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.54
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.68
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.78
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.54
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.22666666666666668
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08399999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.52
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.65
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.66
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.75
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6444461935799786
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6236666666666667
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6114042443354039
+      name: Cosine Map@100
+  - task:
+      type: nano-beir
+      name: Nano BEIR
+    dataset:
+      name: NanoBEIR mean
+      type: NanoBEIR_mean
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.67
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.71
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.22333333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14200000000000002
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08299999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.49
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.655
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.69
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7849999999999999
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.638014673049776
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5980555555555556
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5969641269757594
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on BAAI/bge-base-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25) and [nq](https://huggingface.co/datasets/sentence-transformers/natural-questions) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Supported Modality:** Text
+- **Training Datasets:**
+    - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25)
+    - [nq](https://huggingface.co/datasets/sentence-transformers/natural-questions)
+- **Language:** en
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
+  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'cls', '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("7j/bge-base-en-v1.5-rag-query-adapter")
+# Run inference
+queries = [
+    'which act requires manufacturers to disclose the origins of their minerals?',
+]
+documents = [
+    'International efforts to reduce trade in conflict resources, tried to reduce incentives to extract and fight over them. For example, in the United States, the 2010 Doddâ\x80\x93Frank Wall Street Reform and Consumer Protection Act required manufacturers to audit their supply chains and report use of conflict minerals. In 2015 a US federal appeals court struck down some aspects of the reporting requirements as a violation of corporationsâ\x80\x99 freedom of speech, but left others in place.',
+    'In 2010, Congress passed the Dodd-Frank Act, which directs the Commission to issue rules requiring certain companies to disclose their use of conflict minerals if those minerals are â\x80\x9cnecessary to the functionality or production of a productâ\x80\x9d manufactured by those companies. Under the Act, those minerals include tantalum, tin, gold or tungsten.',
+    'Gastric balloon is a reversible, incision free weight loss procedure. There is one such device approved in the U.S.  the ReShape Integrated Dual Balloon System  but gastric balloons have been approved and used for years in some parts of Europe, as well as Canada, Australia, Mexico and South America.dvocates believe that it can still result in lasting and ongoing weight loss after removal because it helps exact behavioral changes; their theory holds that people get used to eating smaller portions of food and are therefore more inclined to continue to eat in this manner long after the balloon is removed.',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 768] [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[0.6827, 0.7777, 0.3553]])
+```
+<!--
+### 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
+
+* Datasets: `NanoMSMARCO` and `NanoNQ`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | NanoMSMARCO | NanoNQ     |
+|:--------------------|:------------|:-----------|
+| cosine_accuracy@1   | 0.46        | 0.54       |
+| cosine_accuracy@3   | 0.66        | 0.68       |
+| cosine_accuracy@5   | 0.72        | 0.7        |
+| cosine_accuracy@10  | 0.82        | 0.78       |
+| cosine_precision@1  | 0.46        | 0.54       |
+| cosine_precision@3  | 0.22        | 0.2267     |
+| cosine_precision@5  | 0.144       | 0.14       |
+| cosine_precision@10 | 0.082       | 0.084      |
+| cosine_recall@1     | 0.46        | 0.52       |
+| cosine_recall@3     | 0.66        | 0.65       |
+| cosine_recall@5     | 0.72        | 0.66       |
+| cosine_recall@10    | 0.82        | 0.75       |
+| **cosine_ndcg@10**  | **0.6316**  | **0.6444** |
+| cosine_mrr@10       | 0.5724      | 0.6237     |
+| cosine_map@100      | 0.5825      | 0.6114     |
+
+#### Nano BEIR
+
+* Dataset: `NanoBEIR_mean`
+* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.NanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "MSMARCO",
+          "NQ"
+      ],
+      "dataset_id": "sentence-transformers/NanoBEIR-en"
+  }
+  ```
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| cosine_accuracy@1   | 0.5       |
+| cosine_accuracy@3   | 0.67      |
+| cosine_accuracy@5   | 0.71      |
+| cosine_accuracy@10  | 0.8       |
+| cosine_precision@1  | 0.5       |
+| cosine_precision@3  | 0.2233    |
+| cosine_precision@5  | 0.142     |
+| cosine_precision@10 | 0.083     |
+| cosine_recall@1     | 0.49      |
+| cosine_recall@3     | 0.655     |
+| cosine_recall@5     | 0.69      |
+| cosine_recall@10    | 0.785     |
+| **cosine_ndcg@10**  | **0.638** |
+| cosine_mrr@10       | 0.5981    |
+| cosine_map@100      | 0.597     |
+
+<!--
+## 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 Datasets
+
+#### msmarco
+
+* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25) at [ce8a493](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25/tree/ce8a493a65af5e872c3c92f72a89e2e99e175f02)
+* Size: 10,000 training samples
+* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                            | positive                                                                            | negative                                                                            |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                              | string                                                                              |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 9.05 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 76.85 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 74.76 tokens</li><li>max: 128 tokens</li></ul> |
+* Samples:
+  | query                                                                               | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+  |:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>what are the liberal arts?</code>                                             | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                        | <code>The New York State Education Department requires 60 Liberal Arts credits in a Bachelor of Science program and 90 Liberal Arts credits in a Bachelor of Arts program. In the list of course descriptions, courses which are liberal arts for all students are identified by (Liberal Arts) after the course number.</code>                                                                                                                                                                                                                                                                                                         |
+  | <code>what is the mechanism of action of fibrinolytic or thrombolytic drugs?</code> | <code>Baillière's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure.</code>                                                                                                                                                                                                                                                                                                                                           | <code>Fibrinolytic drug. Fibrinolytic drug, also called thrombolytic drug, any agent that is capable of stimulating the dissolution of a blood clot (thrombus). Fibrinolytic drugs work by activating the so-called fibrinolytic pathway.</code>                                                                                                                                                                                                                                                                                                                                                                                        |
+  | <code>what is normal plat count</code>                                              | <code>78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).</code> | <code>Your blood test results should be written in your maternity notes. Your platelet count will look something like Plat. 160x10.9/L, which means you have a platelet count of 160, which is in the normal range.If your platelet count is low, the blood test should be done again.This will keep track of whether or not your count is dropping.our platelet count will look something like Plat. 160x10.9/L, which means you have a platelet count of 160, which is in the normal range. If your platelet count is low, the blood test should be done again. This will keep track of whether or not your count is dropping.</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+#### nq
+
+* Dataset: [nq](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
+* Size: 10,000 training samples
+* Columns: <code>query</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                              | answer                                                                               |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 11.74 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 105.83 tokens</li><li>max: 128 tokens</li></ul> |
+* Samples:
+  | query                                                           | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  |:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>when did richmond last play in a preliminary final</code> | <code>Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tig...</code> |
+  | <code>who sang what in the world's come over you</code>         | <code>Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.</code>                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  | <code>who produces the most wool in the world</code>            | <code>Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.</code>                                                                                                                                                                                                                                                                                                                                                                                                           |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 1
+- `learning_rate`: 2e-05
+- `warmup_steps`: 0.1
+- `weight_decay`: 0.01
+- `gradient_accumulation_steps`: 4
+- `disable_tqdm`: True
+- `per_device_eval_batch_size`: 32
+- `push_to_hub`: True
+- `hub_model_id`: 7j/bge-base-en-v1.5-rag-query-adapter
+- `dataloader_num_workers`: 2
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 0.1
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 4
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1.0
+- `label_smoothing_factor`: 0.0
+- `bf16`: False
+- `fp16`: False
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: True
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `per_device_eval_batch_size`: 32
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: True
+- `hub_private_repo`: None
+- `hub_model_id`: 7j/bge-base-en-v1.5-rag-query-adapter
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: False
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 2
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:--------------------------:|:---------------------:|:----------------------------:|
+| 0.0032 | 1    | 3.7036        | -                          | -                     | -                            |
+| 0.032  | 10   | 1.8804        | -                          | -                     | -                            |
+| 0.064  | 20   | 2.3442        | -                          | -                     | -                            |
+| 0.096  | 30   | 2.0224        | -                          | -                     | -                            |
+| 0.128  | 40   | 2.1542        | -                          | -                     | -                            |
+| 0.16   | 50   | 1.9589        | -                          | -                     | -                            |
+| 0.192  | 60   | 2.0630        | -                          | -                     | -                            |
+| 0.224  | 70   | 2.2774        | -                          | -                     | -                            |
+| 0.256  | 80   | 1.7651        | -                          | -                     | -                            |
+| 0.288  | 90   | 2.0196        | -                          | -                     | -                            |
+| 0.32   | 100  | 1.8836        | -                          | -                     | -                            |
+| 0.352  | 110  | 2.2037        | -                          | -                     | -                            |
+| 0.384  | 120  | 2.3894        | -                          | -                     | -                            |
+| 0.416  | 130  | 1.7873        | -                          | -                     | -                            |
+| 0.448  | 140  | 1.9101        | -                          | -                     | -                            |
+| 0.48   | 150  | 1.9055        | -                          | -                     | -                            |
+| 0.512  | 160  | 1.7553        | -                          | -                     | -                            |
+| 0.544  | 170  | 2.1702        | -                          | -                     | -                            |
+| 0.576  | 180  | 1.9363        | -                          | -                     | -                            |
+| 0.608  | 190  | 2.0139        | -                          | -                     | -                            |
+| 0.64   | 200  | 1.6861        | -                          | -                     | -                            |
+| 0.672  | 210  | 1.7386        | -                          | -                     | -                            |
+| 0.704  | 220  | 2.0747        | -                          | -                     | -                            |
+| 0.736  | 230  | 2.1088        | -                          | -                     | -                            |
+| 0.768  | 240  | 1.7917        | -                          | -                     | -                            |
+| 0.8    | 250  | 1.6875        | -                          | -                     | -                            |
+| 0.832  | 260  | 2.2581        | -                          | -                     | -                            |
+| 0.864  | 270  | 1.7539        | -                          | -                     | -                            |
+| 0.896  | 280  | 1.6714        | -                          | -                     | -                            |
+| 0.928  | 290  | 1.5976        | -                          | -                     | -                            |
+| 0.96   | 300  | 1.7032        | -                          | -                     | -                            |
+| 0.992  | 310  | 1.6623        | -                          | -                     | -                            |
+| -1     | -1   | -             | 0.6316                     | 0.6444                | 0.6380                       |
+
+
+### Training Time
+- **Training**: 10.4 hours
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.4.1
+- Transformers: 5.5.4
+- PyTorch: 2.11.0+cu130
+- Accelerate: 1.13.0
+- Datasets: 4.8.4
+- Tokenizers: 0.22.2
+
+## 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{oord2019representationlearningcontrastivepredictive,
+      title={Representation Learning with Contrastive Predictive Coding},
+      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
+      year={2019},
+      eprint={1807.03748},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1807.03748},
+}
+```
+
+<!--
+## 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 +1,36 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "pytorch": "2.11.0+cu130",
+    "sentence_transformers": "5.4.1",
+    "transformers": "5.5.4"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d60025ff74fc46e9284323ec4f716d36e6944f407010dade282b7f80c0bfa07
+size 437951304

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,10 @@
+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

tokenizer_config.json

@@ -0,0 +1,17 @@
+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "is_local": false,
+  "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": "BertTokenizer",
+  "unk_token": "[UNK]"
+}