Add new SparseEncoder model

README.md

@@ -5,42 +5,33 @@ tags:
 - sparse
 - splade
 - generated_from_trainer
-- dataset_size:1350000
+- dataset_size:1000000
 - loss:SpladeLoss
 - loss:SparseMarginMSELoss
 - loss:FlopsLoss
-base_model:
-- prajjwal1/bert-mini
+base_model: yosefw/SPLADE-BERT-Mini-BS256
 widget:
-- 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 you pay the remaining 20%. The cost sharing stops when
-    medical expenses reach your out-of-pocket maximum, which usually is between
-    $1,000 and $5,000.
-- 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 a company is not to obtain substantial wealth.
-- 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-    previously published and unpublished information about a site, region, or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.
+- text: Caffeine is a central nervous system stimulant. It works by stimulating the
+    brain. Caffeine is found naturally in foods and beverages such as coffee, tea,
+    colas, energy and chocolate. Botanical sources of caffeine include kola nuts,
+    guarana, and yerba mate.
+- text: Tim Hardaway, Jr. Compared To My 5ft 10in (177cm) Height. Tim Hardaway, Jr.'s
+    height is 6ft 6in or 198cm while I am 5ft 10in or 177cm. I am shorter compared
+    to him. To find out how much shorter I am, we would have to subtract my height
+    from Tim Hardaway, Jr.'s height. Therefore I am shorter to him for about 21cm.
+- text: benefits of honey and lemon
+- text: 'How To Cook Corn on the Cob in the Microwave What You Need. Ingredients 1
+    or more ears fresh, un-shucked sweet corn Equipment Microwave Cooling rack or
+    cutting board Instructions. Place 1 to 4 ears of corn in the microwave: Arrange
+    1 to 4 ears of corn, un-shucked, in the microwave. If you prefer, you can set
+    them on a microwaveable plate or tray. If you need to cook more than 4 ears of
+    corn, cook them in batches. Microwave for 3 to 5 minutes: For just 1 or 2 ears
+    of corn, microwave for 3 minutes. For 3 or 4 ears, microwave for 4 minutes. If
+    you like softer corn or if your ears are particularly large, microwave for an
+    additional minute.'
+- text: The law recognizes two basic kinds of warrantiesimplied warranties and express
+    warranties. Implied Warranties. Implied warranties are unspoken, unwritten promises,
+    created by state law, that go from you, as a seller or merchant, to your customers.
 pipeline_tag: feature-extraction
 library_name: sentence-transformers
 metrics:
@@ -74,89 +65,94 @@ model-index:
       type: unknown
     metrics:
     - type: dot_accuracy@1
-      value: 0.4976
+      value: 0.5018
       name: Dot Accuracy@1
     - type: dot_accuracy@3
-      value: 0.8154
+      value: 0.8286
       name: Dot Accuracy@3
     - type: dot_accuracy@5
-      value: 0.9122
+      value: 0.9194
       name: Dot Accuracy@5
     - type: dot_accuracy@10
-      value: 0.9684
+      value: 0.9746
       name: Dot Accuracy@10
     - type: dot_precision@1
-      value: 0.4976
+      value: 0.5018
       name: Dot Precision@1
     - type: dot_precision@3
-      value: 0.2791333333333333
+      value: 0.2839333333333333
       name: Dot Precision@3
     - type: dot_precision@5
-      value: 0.18991999999999998
+      value: 0.19103999999999996
       name: Dot Precision@5
     - type: dot_precision@10
-      value: 0.10178
+      value: 0.10255999999999998
       name: Dot Precision@10
     - type: dot_recall@1
-      value: 0.4821
+      value: 0.4867666666666667
       name: Dot Recall@1
     - type: dot_recall@3
-      value: 0.80205
+      value: 0.81485
       name: Dot Recall@3
     - type: dot_recall@5
-      value: 0.9034833333333334
+      value: 0.9096166666666667
       name: Dot Recall@5
     - type: dot_recall@10
-      value: 0.9639
+      value: 0.9709333333333334
       name: Dot Recall@10
     - type: dot_ndcg@10
-      value: 0.739184491374207
+      value: 0.7457042059559617
       name: Dot Ndcg@10
     - type: dot_mrr@10
-      value: 0.6690194444444474
+      value: 0.6749323809523842
       name: Dot Mrr@10
     - type: dot_map@100
-      value: 0.6646610700105045
+      value: 0.670785161566693
       name: Dot Map@100
     - type: query_active_dims
-      value: 16.810400009155273
+      value: 22.584999084472656
       name: Query Active Dims
     - type: query_sparsity_ratio
-      value: 0.9994492366159113
+      value: 0.9992600419669592
       name: Query Sparsity Ratio
     - type: corpus_active_dims
-      value: 100.62213478240855
+      value: 174.85202722777373
       name: Corpus Active Dims
     - type: corpus_sparsity_ratio
-      value: 0.996703291567315
+      value: 0.9942712788405814
       name: Corpus Sparsity Ratio
-license: mit
-datasets:
-- microsoft/ms_marco
-language:
-- en
 ---
 
-# 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 tiny 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 8.84 million documents from the [MSMARCO](https://huggingface.co/datasets/microsoft/ms_marco) dataset.
+### Full Model Architecture
 
-||Size (# Params)|MRR@10 (MS MARCO dev)|
-|:---|:----|:-------------------|
-|`BM25`|-|18.0|-|-|
-|`rasyosef/splade-tiny`|4.4M|30.9|
-|`rasyosef/splade-mini`|11.2M|33.2|
-|`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
 
@@ -173,15 +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")
 # Run inference
 queries = [
-    "research background definition",
+    "common law implied warranty",
 ]
 documents = [
-    '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.',
+    'The law recognizes two basic kinds of warrantiesimplied warranties and express warranties. Implied Warranties. Implied warranties are unspoken, unwritten promises, created by state law, that go from you, as a seller or merchant, to your customers.',
+    'An implied warranty is a contract law term for certain assurances that are presumed in the sale of products or real property.',
+    'The implied warranty of fitness for a particular purpose is a promise that the law says you, as a seller, make when your customer relies on your advice that a product can be used for some specific purpose.',
 ]
 query_embeddings = model.encode_query(queries)
 document_embeddings = model.encode_document(documents)
@@ -191,7 +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([[22.7011, 11.1635,  0.0000]])
+# tensor([[22.4364, 22.7160, 21.7330]])
 ```
 
 <!--
@@ -218,37 +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)
-- **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 30522 dimensions
-- **Similarity Function:** Dot Product
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-
-### 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
@@ -259,25 +224,25 @@ SparseEncoder(
 
 | Metric                | Value      |
 |:----------------------|:-----------|
-| 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    | 100.6221   |
-| corpus_sparsity_ratio | 0.9967     |
+| dot_accuracy@1        | 0.5018     |
+| dot_accuracy@3        | 0.8286     |
+| dot_accuracy@5        | 0.9194     |
+| dot_accuracy@10       | 0.9746     |
+| dot_precision@1       | 0.5018     |
+| dot_precision@3       | 0.2839     |
+| dot_precision@5       | 0.191      |
+| dot_precision@10      | 0.1026     |
+| dot_recall@1          | 0.4868     |
+| dot_recall@3          | 0.8148     |
+| dot_recall@5          | 0.9096     |
+| dot_recall@10         | 0.9709     |
+| **dot_ndcg@10**       | **0.7457** |
+| dot_mrr@10            | 0.6749     |
+| dot_map@100           | 0.6708     |
+| query_active_dims     | 22.585     |
+| query_sparsity_ratio  | 0.9993     |
+| corpus_active_dims    | 174.852    |
+| corpus_sparsity_ratio | 0.9943     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -297,25 +262,25 @@ SparseEncoder(
 
 #### Unnamed Dataset
 
-* Size: 1,350,000 training samples
-* Columns: <code>query</code>, <code>positive</code>, <code>negative</code>, and <code>label</code>
+* Size: 1,000,000 training samples
+* Columns: <code>query</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, and <code>label</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | 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> |
+  |         | query                                                                            | positive                                                                            | negative_1                                                                          | negative_2                                                                          | label                              |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------|
+  | type    | string                                                                           | string                                                                              | string                                                                              | string                                                                              | list                               |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 9.01 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 80.48 tokens</li><li>max: 247 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 79.27 tokens</li><li>max: 213 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 75.56 tokens</li><li>max: 190 tokens</li></ul> | <ul><li>size: 2 elements</li></ul> |
 * Samples:
-  | 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> |
+  | query                                                      | positive                                                                                                                                                                                                                                                                                                | negative_1                                                                                                                                                                                                                                                                                                    | negative_2                                                                                                                                                                                                                                                            | label                                                |
+  |:-----------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------|
+  | <code>friendly home health care</code>                     | <code>Medicare Evaluation of the Quality of Care. The quality of care given at Friendly Care Home Health Services is periodically evaluated by Medicare. The results of the most recent evaluation period are listed below to help you compare home care agencies in your area. More Info.</code>       | <code>Every participant took the same survey so it is a useful way to compare Friendly Care Home Health Services to other home care agencies.</code>                                                                                                                                                          | <code>It covers a wide range of services and can often delay the need for long-term nursing home care. More specifically, home health care may include occupational and physical therapy, speech therapy, and even skilled nursing.</code>                            | <code>[1.2647171020507812, 9.144136428833008]</code> |
+  | <code>how much does the xbox elite controller weigh</code> | <code>How much does an Xbox 360 weigh? A: The weight of an Xbox 360 depends on the different model purchased, with an original Xbox 360 or Xbox 360 Elite weighing 7.7 pounds with a hard drive and a newer Xbox 360 Slim weighing 6.3 pounds. An Xbox 360 without a hard drive weighs 7 pounds.</code> | <code>How much does 6 xbox 360 games/cases weigh? How much does an xbox 360 elite weigh (in the box)? How much does an xbox 360 weigh? im going to fedex one? I am considering purchasing an Xbox 360, or a Playstation 3...</code>                                                                           | <code>1 You can only upload videos smaller than 600 MB. 2  You can only upload a photo (png, jpg, jpeg) or video (3gp, 3gpp, mp4, mov, avi, mpg, mpeg, rm). 3  You can only upload a photo or video.  Video should be smaller than <b>600 MB/5 minutes</b>.</code>    | <code>[4.903870582580566, 18.162578582763672]</code> |
+  | <code>what county is norfolk, ct in</code>                 | <code>Norfolk, Connecticut. Norfolk (local /ˈnɔːrfɔːrk/) is a town in Litchfield County, Connecticut, United States. The population was 1,787 at the 2010 census.</code>                                                                                                                                | <code>Norfolk Historic District. The Norfolk Historic District was listed on the National Register of Historic Places in 1979. Portions of the content on this web page were adapted from a copy of the original nomination document. [†] Adaptation copyright © 2010, The Gombach Group. Description.</code> | <code>Terms begin the first day of the month. Grand Juries, 1st and 3rd Wednesday of each month. Civil cases set by agreement of counsel and consent of the court; scheduling orders are mandatory in most cases. Civil and Criminal trials begin at 9:30 a.m.</code> | <code>[12.4237699508667, 21.46290397644043]</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": 0.2,
-      "query_regularizer_weight": 0.3
+      "document_regularizer_weight": 0.12,
+      "query_regularizer_weight": 0.2
   }
   ```
 
@@ -323,16 +288,15 @@ SparseEncoder(
 #### Non-Default Hyperparameters
 
 - `eval_strategy`: epoch
-- `per_device_train_batch_size`: 32
-- `per_device_eval_batch_size`: 32
-- `learning_rate`: 6e-05
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 4e-05
 - `num_train_epochs`: 4
 - `lr_scheduler_type`: cosine
-- `warmup_ratio`: 0.05
+- `warmup_ratio`: 0.025
 - `fp16`: True
 - `load_best_model_at_end`: True
 - `optim`: adamw_torch_fused
-- `push_to_hub`: True
 
 #### All Hyperparameters
 <details><summary>Click to expand</summary>
@@ -341,14 +305,14 @@ SparseEncoder(
 - `do_predict`: False
 - `eval_strategy`: epoch
 - `prediction_loss_only`: True
-- `per_device_train_batch_size`: 32
-- `per_device_eval_batch_size`: 32
+- `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
 - `torch_empty_cache_steps`: None
-- `learning_rate`: 6e-05
+- `learning_rate`: 4e-05
 - `weight_decay`: 0.0
 - `adam_beta1`: 0.9
 - `adam_beta2`: 0.999
@@ -358,7 +322,7 @@ SparseEncoder(
 - `max_steps`: -1
 - `lr_scheduler_type`: cosine
 - `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.05
+- `warmup_ratio`: 0.025
 - `warmup_steps`: 0
 - `log_level`: passive
 - `log_level_replica`: warning
@@ -415,7 +379,7 @@ SparseEncoder(
 - `dataloader_persistent_workers`: False
 - `skip_memory_metrics`: True
 - `use_legacy_prediction_loop`: False
-- `push_to_hub`: True
+- `push_to_hub`: False
 - `resume_from_checkpoint`: None
 - `hub_model_id`: None
 - `hub_strategy`: every_save
@@ -458,21 +422,19 @@ SparseEncoder(
 </details>
 
 ### Training Logs
-| 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**  |
+| Epoch | Step  | Training Loss | dot_ndcg@10 |
+|:-----:|:-----:|:-------------:|:-----------:|
+| 1.0   | 15625 | 9.3147        | 0.7353      |
+| 2.0   | 31250 | 7.5267        | 0.7429      |
+| 3.0   | 46875 | 6.3289        | 0.7457      |
 
-* The bold row denotes the saved checkpoint.
 
 ### Framework Versions
 - Python: 3.11.13
 - Sentence Transformers: 5.0.0
 - Transformers: 4.53.3
 - PyTorch: 2.6.0+cu124
-- Accelerate: 1.8.1
+- Accelerate: 1.9.0
 - Datasets: 4.0.0
 - Tokenizers: 0.21.2
 
@@ -544,6 +506,4 @@ 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": "4.54.0",
+  "transformers_version": "4.55.2",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

config_sentence_transformers.json

@@ -1,9 +1,9 @@
 {
   "model_type": "SparseEncoder",
   "__version__": {
-    "sentence_transformers": "5.0.0",
-    "transformers": "4.54.0",
-    "pytorch": "2.6.0+cu124"
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.55.2",
+    "pytorch": "2.8.0+cu126"
   },
   "prompts": {
     "query": "",

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4050564a649d96030d5ec42b38ae323f47c9454d87af057a42721bf892ed32a7
+oid sha256:2a7f2791a6ae8c6c07c51f95d67f52c7514cecf0fe59f0381969e3add0751f9d
 size 44814856