Add new SparseEncoder model

README.md

@@ -1,47 +1,37 @@
 ---
-language:
-- en
-license: mit
 tags:
 - sentence-transformers
 - sparse-encoder
 - sparse
 - splade
 - generated_from_trainer
-- dataset_size:250000
+- dataset_size:1350000
 - loss:SpladeLoss
 - loss:SparseMarginMSELoss
 - loss:FlopsLoss
-base_model: prajjwal1/bert-mini
+base_model: yosefw/SPLADE-BERT-Mini-BS256
 widget:
-- text: what did marlo thomas play on
-- text: >-
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-    out at termination. Please Note: This table applies to employees in
-    positions with 100% FTE. The number of hours/days of vacation are pro-rated
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-    of hours they would. accrue each month would be 6 (8 x .75 = 6) and not 8.
-- text: >-
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-- text: >-
-    Fermentation is a metabolic pathway that produce ATP molecules under
-    anaerobic conditions (only undergoes glycolysis), NAD+ is used directly in
-    glycolysis to form ATP molecules, which is not as efficient as cellular
-    respiration because only 2ATP molecules are formed during the glycolysis.
+- text: Coinsurance is a health care cost sharing between you and your insurance company.
+    The cost sharing ranges from 80/20 to even 50/50. For example, if your coinsurance
+    is 80/20, that means that your insurer covers 80% of annual medical expenses and
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+- text: The Definition of Success. 1  In 1806, the definition of Success in the Webster
+    dictionary was to be fortunate, happy, kind and prosperous. In 2013 the definition
+    of success is the attainment of wealth, fame and power. 2  The purpose of forming
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+- text: 'It wouldn''t be completely accurate to say 10 syllables, because in English
+    Sonnet writing, they are written in iambic pentameter, which is ten syllables,
+    but it''s not just any syllables, they have to be in rhythm.da-DA-da-DA-da-DA-da-DA-da-DA.
+    And the rhymes are ABAB/CDCD/EFEF/GG for each stanza.hat makes a sonnet a sonnet
+    is the rhyme scheme and the 10 syllable lines. Check out this site and it may
+    help you: http://www.elfwood.com/farp/thewriting/2... Sptfyr · 7 years ago. Thumbs
+    up.'
+- text: Dragon horn. A dragon horn is a sorcerous horn that is used to control dragons.
+- text: Social Sciences. Background research refers to accessing the collection of
+    previously published and unpublished information about a site, region, or particular
+    topic of interest and it is the first step of all good archaeological investigations,
+    as well as that of all writers of any kind of research paper.
 pipeline_tag: feature-extraction
 library_name: sentence-transformers
 metrics:
@@ -65,7 +55,7 @@ metrics:
 - corpus_active_dims
 - corpus_sparsity_ratio
 model-index:
-- name: SPLADE-BERT-Mini-Distil
+- name: SPLADE Sparse Encoder
   results:
   - task:
       type: sparse-information-retrieval
@@ -75,86 +65,94 @@ model-index:
       type: unknown
     metrics:
     - type: dot_accuracy@1
-      value: 0.4828
+      value: 0.4976
       name: Dot Accuracy@1
     - type: dot_accuracy@3
-      value: 0.8052
+      value: 0.8154
       name: Dot Accuracy@3
     - type: dot_accuracy@5
-      value: 0.9046
+      value: 0.9122
       name: Dot Accuracy@5
     - type: dot_accuracy@10
-      value: 0.9666
+      value: 0.9684
       name: Dot Accuracy@10
     - type: dot_precision@1
-      value: 0.4828
+      value: 0.4976
       name: Dot Precision@1
     - type: dot_precision@3
-      value: 0.27566666666666667
+      value: 0.2791333333333333
       name: Dot Precision@3
     - type: dot_precision@5
-      value: 0.18787999999999996
+      value: 0.18991999999999998
       name: Dot Precision@5
     - type: dot_precision@10
-      value: 0.10156
+      value: 0.10178
       name: Dot Precision@10
     - type: dot_recall@1
-      value: 0.4673
+      value: 0.4821
       name: Dot Recall@1
     - type: dot_recall@3
-      value: 0.792
+      value: 0.80205
       name: Dot Recall@3
     - type: dot_recall@5
-      value: 0.8949
+      value: 0.9034833333333334
       name: Dot Recall@5
     - type: dot_recall@10
-      value: 0.9624166666666668
+      value: 0.9639
       name: Dot Recall@10
     - type: dot_ndcg@10
-      value: 0.7302009825334612
+      value: 0.739184491374207
       name: Dot Ndcg@10
     - type: dot_mrr@10
-      value: 0.6579904761904781
+      value: 0.6690194444444474
       name: Dot Mrr@10
     - type: dot_map@100
-      value: 0.6534502206938125
+      value: 0.6646610700105045
       name: Dot Map@100
     - type: query_active_dims
-      value: 19.52400016784668
+      value: 16.810400009155273
       name: Query Active Dims
     - type: query_sparsity_ratio
-      value: 0.9993603302480883
+      value: 0.9994492366159113
       name: Query Sparsity Ratio
     - type: corpus_active_dims
-      value: 113.4705113862854
+      value: 100.62213478240855
       name: Corpus Active Dims
     - type: corpus_sparsity_ratio
-      value: 0.9962823369573983
+      value: 0.996703291567315
       name: Corpus Sparsity Ratio
-datasets:
-- microsoft/ms_marco
 ---
 
-# SPLADE-BERT-Mini-Distil
+# SPLADE Sparse Encoder
 
-This is a SPLADE sparse retrieval model based on BERT-Mini (11M) that was trained by distilling a Cross-Encoder on the MSMARCO dataset. The cross-encoder used was [ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2). 
+This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [yosefw/SPLADE-BERT-Mini-BS256](https://huggingface.co/yosefw/SPLADE-BERT-Mini-BS256) using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space   and can be used for semantic search and sparse retrieval.
+## Model Details
 
-This mini SPLADE model is `6x` smaller than Naver's official `splade-v3-distilbert` while having `85%` of it's performance on the MSMARCO benchmark. This model is small enough to be used without a GPU on a dataset of a few thousand documents. 
+### Model Description
+- **Model Type:** SPLADE Sparse Encoder
+- **Base model:** [yosefw/SPLADE-BERT-Mini-BS256](https://huggingface.co/yosefw/SPLADE-BERT-Mini-BS256) <!-- at revision 986bc55b61d9f0559f86423fb5807b9f4a3b7094 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 30522 dimensions
+- **Similarity Function:** Dot Product
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
 
-- `Collection:` https://huggingface.co/collections/rasyosef/splade-tiny-msmarco-687c548c0691d95babf65b70
-- `Distillation Dataset:` https://huggingface.co/datasets/yosefw/msmarco-train-distil-v2
-- `Code:` https://github.com/rasyosef/splade-tiny-msmarco
+### Model Sources
 
-## Performance
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
 
-The splade models were evaluated on 55 thousand queries and 8 million documents from the [MSMARCO](https://huggingface.co/datasets/microsoft/ms_marco) dataset.
+### Full Model Architecture
 
-||Size (# Params)|MRR@10 (MS MARCO dev)|
-|:---|:----|:-------------------|
-|`BM25`|-|18.6|-|-|
-|`rasyosef/splade-tiny`|4.4M|30.8|
-|`rasyosef/splade-mini`|11.2M|32.8|
-|`naver/splade-v3-distilbert`|67.0M|38.7|
+```
+SparseEncoder(
+  (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertForMaskedLM'})
+  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
+)
+```
 
 ## Usage
 
@@ -171,15 +169,15 @@ Then you can load this model and run inference.
 from sentence_transformers import SparseEncoder
 
 # Download from the 🤗 Hub
-model = SparseEncoder("rasyosef/splade-mini")
+model = SparseEncoder("yosefw/SPLADE-BERT-Mini-BS256-distil-v2")
 # Run inference
 queries = [
-    "definition of fermentation in the lab",
+    "research background definition",
 ]
 documents = [
-    'Fermentation is a metabolic pathway that produce ATP molecules under anaerobic conditions (only undergoes glycolysis), NAD+ is used directly in glycolysis to form ATP molecules, which is not as efficient as cellular respiration because only 2ATP molecules are formed during the glycolysis.',
-    'Essay on Yeast Fermentation ... Yeast Fermentation Lab Report The purpose of this experiment was to observe the process in which cells must partake in a respiration process called anaerobic fermentation and as the name suggests, oxygen is not required.',
-    '\ufeffYeast Fermentation Lab Report The purpose of this experiment was to observe the process in which cells must partake in a respiration process called anaerobic fermentation and as the name suggests, oxygen is not required.',
+    'Social Sciences. Background research refers to accessing the collection of previously published and unpublished information about a site, region, or particular topic of interest and it is the first step of all good archaeological investigations, as well as that of all writers of any kind of research paper.',
+    'This Research Paper Background and Problem Definition and other 62,000+ term papers, college essay examples and free essays are available now on ReviewEssays.com. Autor: dharath1 • July 22, 2014 • Research Paper • 442 Words (2 Pages) • 448 Views.',
+    'About the Month of February. February is the 2nd month of the year and has 28 or 29 days. The 29th day is every 4 years during leap year. Season (Northern Hemisphere): Winter. Holidays. Chinese New Year. National Freedom Day. Groundhog Day.',
 ]
 query_embeddings = model.encode_query(queries)
 document_embeddings = model.encode_document(documents)
@@ -189,7 +187,7 @@ print(query_embeddings.shape, document_embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(query_embeddings, document_embeddings)
 print(similarities)
-# tensor([[20.0220, 17.1372, 15.9159]])
+# tensor([[22.7011, 11.1635,  0.0000]])
 ```
 
 <!--
@@ -216,36 +214,6 @@ You can finetune this model on your own dataset.
 *List how the model may foreseeably be misused and address what users ought not to do with the model.*
 -->
 
-## Model Details
-
-### Model Description
-- **Model Type:** SPLADE Sparse Encoder
-- **Base model:** [prajjwal1/bert-mini](https://huggingface.co/prajjwal1/bert-mini) <!-- at revision 5e123abc2480f0c4b4cac186d3b3f09299c258fc -->
-- **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 30522 dimensions
-- **Similarity Function:** Dot Product
-<!-- - **Training Dataset:** Unknown -->
-- **Language:** en
-- **License:** mit
-
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
-
-### Full Model Architecture
-
-```
-SparseEncoder(
-  (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertForMaskedLM'})
-  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
-)
-```
-
-## More
-<details><summary>Click to expand</summary>
 ## Evaluation
 
 ### Metrics
@@ -256,25 +224,25 @@ SparseEncoder(
 
 | Metric                | Value      |
 |:----------------------|:-----------|
-| dot_accuracy@1        | 0.4828     |
-| dot_accuracy@3        | 0.8052     |
-| dot_accuracy@5        | 0.9046     |
-| dot_accuracy@10       | 0.9666     |
-| dot_precision@1       | 0.4828     |
-| dot_precision@3       | 0.2757     |
-| dot_precision@5       | 0.1879     |
-| dot_precision@10      | 0.1016     |
-| dot_recall@1          | 0.4673     |
-| dot_recall@3          | 0.792      |
-| dot_recall@5          | 0.8949     |
-| dot_recall@10         | 0.9624     |
-| **dot_ndcg@10**       | **0.7302** |
-| dot_mrr@10            | 0.658      |
-| dot_map@100           | 0.6535     |
-| query_active_dims     | 19.524     |
+| dot_accuracy@1        | 0.4976     |
+| dot_accuracy@3        | 0.8154     |
+| dot_accuracy@5        | 0.9122     |
+| dot_accuracy@10       | 0.9684     |
+| dot_precision@1       | 0.4976     |
+| dot_precision@3       | 0.2791     |
+| dot_precision@5       | 0.1899     |
+| dot_precision@10      | 0.1018     |
+| dot_recall@1          | 0.4821     |
+| dot_recall@3          | 0.8021     |
+| dot_recall@5          | 0.9035     |
+| dot_recall@10         | 0.9639     |
+| **dot_ndcg@10**       | **0.7392** |
+| dot_mrr@10            | 0.669      |
+| dot_map@100           | 0.6647     |
+| query_active_dims     | 16.8104    |
 | query_sparsity_ratio  | 0.9994     |
-| corpus_active_dims    | 113.4705   |
-| corpus_sparsity_ratio | 0.9963     |
+| corpus_active_dims    | 100.6221   |
+| corpus_sparsity_ratio | 0.9967     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -294,25 +262,25 @@ SparseEncoder(
 
 #### Unnamed Dataset
 
-* Size: 250,000 training samples
-* Columns: <code>query</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, and <code>label</code>
+* Size: 1,350,000 training samples
+* Columns: <code>query</code>, <code>positive</code>, <code>negative</code>, and <code>label</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | query                                                                            | positive                                                                            | negative_1                                                                          | negative_2                                                                          | negative_3                                                                          | negative_4                                                                          | label                              |
-  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------|
-  | type    | string                                                                           | string                                                                              | string                                                                              | string                                                                              | string                                                                              | string                                                                              | list                               |
-  | details | <ul><li>min: 4 tokens</li><li>mean: 8.87 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 81.23 tokens</li><li>max: 259 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 79.21 tokens</li><li>max: 197 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 77.89 tokens</li><li>max: 207 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 76.38 tokens</li><li>max: 271 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 75.46 tokens</li><li>max: 214 tokens</li></ul> | <ul><li>size: 4 elements</li></ul> |
+  |         | query                                                                            | positive                                                                            | negative                                                                            | label                              |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------|
+  | type    | string                                                                           | string                                                                              | string                                                                              | list                               |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 8.95 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 79.36 tokens</li><li>max: 215 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 78.39 tokens</li><li>max: 233 tokens</li></ul> | <ul><li>size: 1 elements</li></ul> |
 * Samples:
-  | query                                                 | positive                                                                                                                                                                                                                                                                           | negative_1                                                                                                                                                                                                                                                                                                                                               | negative_2                                                                                                                                                                                                                                                                                                                                          | negative_3                                                                                                                                                                                                                                                                                                                                                                                   | negative_4                                                                                                                                                                                                                                                                                       | label                                                                                      |
-  |:------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------|
-  | <code>heart specialists in ridgeland ms</code>        | <code>Dr. George Reynolds Jr, MD is a cardiology specialist in Ridgeland, MS and has been practicing for 35 years. He graduated from Vanderbilt University School Of Medicine in 1977 and specializes in cardiology and internal medicine.</code>                                  | <code>Dr. James Kramer is a Internist in Ridgeland, MS. Find Dr. Kramer's phone number, address and more.</code>                                                                                                                                                                                                                                         | <code>Dr. James Kramer is an internist in Ridgeland, Mississippi. He received his medical degree from Loma Linda University School of Medicine and has been in practice for more than 20 years. Dr. James Kramer's Details</code>                                                                                                                   | <code>Chronic Pulmonary Heart Diseases (incl. Pulmonary Hypertension) Coarctation of the Aorta; Congenital Aortic Valve Disorders; Congenital Heart Defects; Congenital Heart Disease; Congestive Heart Failure; Coronary Artery Disease (CAD) Endocarditis; Heart Attack (Acute Myocardial Infarction) Heart Disease; Heart Murmur; Heart Palpitations; Hyperlipidemia; Hypertension</code> | <code>A growing shortage of primary care doctors means you might have to look harder for ongoing care. How to Read an OTC Medication Label Purvi Parikh, M.D. | Feb. 12, 2018</code>                                                                                                             | <code>[6.058592796325684, 6.587987422943115, 19.88274383544922, 20.211898803710938]</code> |
-  | <code>does baytril otic require a prescription</code> | <code>Baytril Otic Ear Drops-Enrofloxacin/Silver Sulfadiazine-Prices & Information. A prescription is required for this item. A prescription is required for this item. Brand medication is not available at this time.</code>                                                     | <code>RX required for this item. Click here for our full Prescription Policy and Form. Baytril Otic (enrofloxacin/silver sulfadiazine) Emulsion from Bayer is the first fluoroquinolone approved by the Food and Drug Administration for the topical treatment of canine otitis externa.</code>                                                          | <code>Product Details. Baytril Otic is a highly effective treatment prescribed by many veterinarians when your pet has an ear infection caused by susceptible bacteria or fungus. Baytril Otic is: a liquid emulsion that is used topically directly in the ear or on the skin in order to treat susceptible bacterial and yeast infections.</code> | <code>Baytril for dogs is an antibiotic often prescribed for bacterial infections, particularly those involving the ears. Ear infections are rare in many animals, but quite common in dogs. This is particularly true for dogs with long droopy ears, where it will stay very warm and moist.</code>                                                                                        | <code>Administer 5-10 Baytril ear drops per treatment in dogs 35 lbs or less and 10-15 drops per treatment in dogs more than 35 lbs.</code>                                                                                                                                                      | <code>[1.0, 3.640146493911743, 6.450072288513184, 11.96937084197998]</code>                |
-  | <code>what is on a gyro</code>                        | <code>Report Abuse. Gyros or gyro (giros) (pronounced /ˈjɪəroʊ/ or /ˈdʒaɪroʊ/, Greek: γύρος turn) is a Greek dish consisting of meat (typically lamb and/or beef), tomato, onion, and tzatziki sauce, and is served with pita bread. Chicken and pork meat can be used too.</code> | <code>A gyroscope (from Ancient Greek γῦρος gûros, circle and σκοπέω skopéō, to look) is a spinning wheel or disc in which the axis of rotation is free to assume any orientation by itself. When rotating, the orientation of this axis is unaffected by tilting or rotation of the mounting, according to the conservation of angular momentum.</code> | <code>Diagram of a gyro wheel. Reaction arrows about the output axis (blue) correspond to forces applied about the input axis (green), and vice versa. A gyroscope is a wheel mounted in two or three gimbals, which are a pivoted supports that allow the rotation of the wheel about a single axis.</code>                                        | <code>A fair number of our users are unsure of how to pronounce gyro. This isn't surprising, since there are two different gyros and they have two different pronunciations. The earlier gyro is the one that is a shortened form of gyrocompass or gyroscope, and it has a pronunciation that conforms to one's expectations: /JEYE-roh/.</code>                                            | <code>Vibration Gyro Sensors. Vibration gyro sensors sense angular velocity from the Coriolis force applied to a vibrating element. For this reason, the accuracy with which angular velocity is measured differs significantly depending on element material and structural differences.</code> | <code>[2.1750364303588867, 2.634796142578125, 4.30520486831665, 6.382436752319336]</code>  |
+  | query                                       | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | negative                                                                                                                                                                                                                                                                                                                                                   | label                             |
+  |:--------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
+  | <code>what causes protruding stomach</code> | <code>Some of the less common causes of Protruding abdomen may include: 1  Constipation. 2  Chronic constipation. 3  Poor muscle tone.  Poor muscle tone after 1  childbirth. Lactose intolerance.  Food 1  allergies. Food intolerances. 2  Pregnancy. 3  Hernia. Malabsorption.  Irritable bowel 1  syndrome. Colonic bacterial fermentation. 2  Gastroparesis. Diabetic gastroparesis.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | <code>Protruding abdomen: Introduction. Protruding abdomen: abdominal distension. See detailed information below for a list of 56 causes of Protruding abdomen, Symptom Checker, including diseases and drug side effect causes. » Review Causes of Protruding abdomen: Causes | Symptom Checker ». Home Diagnostic Testing and Protruding abdomen.</code> | <code>[3.2738194465637207]</code> |
+  | <code>what is bialys</code>                 | <code>The bialy is not a sub-type of bagel, it’s a thing all to itself. Round with a depressed middle filled with cooked onions and sometimes poppy seeds, it is simply baked (bagels are boiled then baked). Purists prefer them straight up, preferably no more than five hours after being pulled from the oven. Extinction.Like the Lowland gorilla, the cassette tape and Madagascar forest coconuts, the bialy is rapidly becoming extinct. Sure, if you live in New York (where the Jewish tenements on the Lower East Side once overflowed with Eastern European foodstuffs that are now hard to locate), you have a few decent options.he bialy is not a sub-type of bagel, it’s a thing all to itself. Round with a depressed middle filled with cooked onions and sometimes poppy seeds, it is simply baked (bagels are boiled then baked). Purists prefer them straight up, preferably no more than five hours after being pulled from the oven. Extinction.</code> | <code>This homemade bialy recipe is even easier to make than a bagel because it doesn’t require boiling prior to baking.his homemade bialy recipe is even easier to make than a bagel because it doesn’t require boiling prior to baking.</code>                                                                                                           | <code>[5.632390975952148]</code>  |
+  | <code>dhow definition</code>                | <code>Definition of dhow. : an Arab lateen-rigged boat usually having a long overhang forward, a high poop, and a low waist.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | <code>Freebase(0.00 / 0 votes)Rate this definition: Dhow. Dhow is the generic name of a number of traditional sailing vessels with one or more masts with lateen sails used in the Red Sea and Indian Ocean region. Historians are divided as to whether the dhow was invented by Arabs or Indians.</code>                                                 | <code>[0.8292264938354492]</code> |
 * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
   ```json
   {
       "loss": "SparseMarginMSELoss",
-      "document_regularizer_weight": 0.3,
-      "query_regularizer_weight": 0.5
+      "document_regularizer_weight": 0.2,
+      "query_regularizer_weight": 0.3
   }
   ```
 
@@ -320,15 +288,16 @@ SparseEncoder(
 #### Non-Default Hyperparameters
 
 - `eval_strategy`: epoch
-- `per_device_train_batch_size`: 48
-- `per_device_eval_batch_size`: 48
-- `learning_rate`: 8e-05
-- `num_train_epochs`: 6
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `learning_rate`: 6e-05
+- `num_train_epochs`: 4
 - `lr_scheduler_type`: cosine
-- `warmup_ratio`: 0.025
+- `warmup_ratio`: 0.05
 - `fp16`: True
 - `load_best_model_at_end`: True
 - `optim`: adamw_torch_fused
+- `push_to_hub`: True
 
 #### All Hyperparameters
 <details><summary>Click to expand</summary>
@@ -337,24 +306,24 @@ SparseEncoder(
 - `do_predict`: False
 - `eval_strategy`: epoch
 - `prediction_loss_only`: True
-- `per_device_train_batch_size`: 48
-- `per_device_eval_batch_size`: 48
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
 - `per_gpu_train_batch_size`: None
 - `per_gpu_eval_batch_size`: None
 - `gradient_accumulation_steps`: 1
 - `eval_accumulation_steps`: None
 - `torch_empty_cache_steps`: None
-- `learning_rate`: 8e-05
+- `learning_rate`: 6e-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`: 6
+- `num_train_epochs`: 4
 - `max_steps`: -1
 - `lr_scheduler_type`: cosine
 - `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.025
+- `warmup_ratio`: 0.05
 - `warmup_steps`: 0
 - `log_level`: passive
 - `log_level_replica`: warning
@@ -411,7 +380,7 @@ SparseEncoder(
 - `dataloader_persistent_workers`: False
 - `skip_memory_metrics`: True
 - `use_legacy_prediction_loop`: False
-- `push_to_hub`: False
+- `push_to_hub`: True
 - `resume_from_checkpoint`: None
 - `hub_model_id`: None
 - `hub_strategy`: every_save
@@ -454,21 +423,19 @@ SparseEncoder(
 </details>
 
 ### Training Logs
-| Epoch   | Step      | Training Loss | dot_ndcg@10 |
-|:-------:|:---------:|:-------------:|:-----------:|
-| 1.0     | 5209      | 30541.8683    | 0.6969      |
-| 2.0     | 10418     | 13.3966       | 0.7167      |
-| 3.0     | 15627     | 11.6531       | 0.7262      |
-| 4.0     | 20836     | 9.9781        | 0.7280      |
-| 5.0     | 26045     | 8.881         | 0.7289      |
-| **6.0** | **31254** | **8.3454**    | **0.7302**  |
+| Epoch   | Step       | Training Loss | dot_ndcg@10 |
+|:-------:|:----------:|:-------------:|:-----------:|
+| 1.0     | 42188      | 8.6242        | 0.7262      |
+| 2.0     | 84376      | 7.0404        | 0.7362      |
+| 3.0     | 126564     | 5.3661        | 0.7388      |
+| **4.0** | **168752** | **4.4807**    | **0.7392**  |
 
 * The bold row denotes the saved checkpoint.
 
 ### Framework Versions
 - Python: 3.11.13
 - Sentence Transformers: 5.0.0
-- Transformers: 4.53.2
+- Transformers: 4.53.3
 - PyTorch: 2.6.0+cu124
 - Accelerate: 1.8.1
 - Datasets: 4.0.0
@@ -542,5 +509,4 @@ SparseEncoder(
 ## Model Card Contact
 
 *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
-</details>
+--> 

config.json

@@ -17,7 +17,7 @@
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.53.2",
+  "transformers_version": "4.54.0",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

config_sentence_transformers.json

@@ -2,7 +2,7 @@
   "model_type": "SparseEncoder",
   "__version__": {
     "sentence_transformers": "5.0.0",
-    "transformers": "4.53.2",
+    "transformers": "4.54.0",
     "pytorch": "2.6.0+cu124"
   },
   "prompts": {

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:eca5ef6ed2e950b988214239e64f87b79f40b939a74ad47b6994ffa4b5de2c25
+oid sha256:4050564a649d96030d5ec42b38ae323f47c9454d87af057a42721bf892ed32a7
 size 44814856