Add new SparseEncoder model

README.md

@@ -0,0 +1,881 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- asymmetric
+- inference-free
+- splade
+- generated_from_trainer
+- dataset_size:99000
+- loss:SpladeLoss
+widget:
+- source_sentence: where is the tiber river located in italy
+  sentences:
+  - Sales taxes in British Columbia On 1 July 2010, the PST and GST were combined
+    into the Harmonized Sales Tax (HST) levied according to the provisions of the
+    GST. The conversion to HST was controversial. Popular opposition led to a referendum
+    on the tax system, the first such referendum in the Commonwealth of Nations, resulting
+    in the province reverting to the former PST/GST model on 1 April 2013.
+  - 'Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2]
+    is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna
+    and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it
+    is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3]
+    It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river
+    has achieved lasting fame as the main watercourse of the city of Rome, founded
+    on its eastern banks.'
+  - 'Water in California California''s limited water supply comes from two main sources:
+    surface water, or water that travels or gathers on the ground, like rivers, streams,
+    and lakes; and groundwater, which is water that is pumped out from the ground.
+    California has also begun producing a small amount of desalinated water, water
+    that was once sea water, but has been purified.'
+- source_sentence: what kind of car does jay gatsby drive
+  sentences:
+  - Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide
+    to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to
+    the city. On the way to New York City, Tom makes a detour at a gas station in
+    "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson,
+    shares his concern that his wife, Myrtle, may be having an affair. This unnerves
+    Tom, who has been having an affair with Myrtle, and he leaves in a hurry.
+  - 'Panama Canal The Panama Canal (Spanish: Canal de Panamá) is an artificial 77 km
+    (48 mi) waterway in Panama that connects the Atlantic Ocean with the Pacific Ocean.
+    The canal cuts across the Isthmus of Panama and is a conduit for maritime trade.
+    Canal locks are at each end to lift ships up to Gatun Lake, an artificial lake
+    created to reduce the amount of excavation work required for the canal, 26 m (85
+    ft) above sea level, and then lower the ships at the other end. The original locks
+    are 34 m (110 ft) wide. A third, wider lane of locks was constructed between September
+    2007 and May 2016. The expanded canal began commercial operation on June 26, 2016.
+    The new locks allow transit of larger, post-Panamax ships, capable of handling
+    more cargo.[1]'
+  - Solar maximum Predictions of a future maximum's timing and strength are very difficult;
+    predictions vary widely. There was a solar maximum in 2000. In 2006 NASA initially
+    expected a solar maximum in 2010 or 2011, and thought that it could be the strongest
+    since 1958.[3] However, the solar maximum was not declared to have occurred until
+    2014, and even then was ranked among the weakest on record.[4]
+- source_sentence: who sings if i can dream about you
+  sentences:
+  - Wesley Jonathan Wesley Jonathan Waples (born October 18, 1978), known professionally
+    as Wesley Jonathan, is an American actor. He is best known for his starring role
+    as Jamal Grant on the NBC Saturday morning comedy-drama series City Guys, Sweetness
+    in the 2005 film Roll Bounce, as well as Burrell "Stamps" Ballentine on TV Land's
+    The Soul Man.
+  - I Can Dream About You "I Can Dream About You" is a song performed by American
+    singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released
+    in 1984 as a single from the soundtrack, and included on Hartman's album I Can
+    Dream About You, it reached number 6 on the Billboard Hot 100.[1]
+  - Blood is thicker than water In modern society, the proverb "blood is thicker than
+    water" is used to imply that family relationships are always more important than
+    friends.
+- source_sentence: who did jesse palmer end up with on the bachelor
+  sentences:
+  - Jesse Palmer In 2004, Palmer was the first professional athlete to appear on The
+    Bachelor television program and the first non-American bachelor, in which he was
+    given his choice of eligible single women. He eventually selected Jessica Bowlin,
+    but their courtship lasted for only a few months after the end of the show.[19][20]
+  - Wave base In seawater, the water particles are moved in a circular orbital motion
+    when a wave passes. The radius of the circle of motion for any given water molecule
+    decreases exponentially with increasing depth. The wave base, which is the depth
+    of influence of a water wave, is about half the wavelength.
+  - Do You Remember the First Time? (The Vampire Diaries) Elena, after everyone continues
+    to convince her that she had once loved damon decides to run through the magic
+    free, mystic falls border. So she does, and she gets glimpses of her and Damon
+    but never fully remembers yet that she loves him. Damon pulls her back across
+    the line and she asks about a kiss in the rain. He continues to try to get her
+    to remember.
+- source_sentence: when did the american civil rights movement end
+  sentences:
+  - 'A Sunday Afternoon on the Island of La Grande Jatte A Sunday Afternoon on the
+    Island of La Grande Jatte (French: Un dimanche après-midi à l''Île de la Grande
+    Jatte) painted in 1884, is one of Georges Seurat''s most famous works. It is a
+    leading example of pointillist technique, executed on a large canvas. Seurat''s
+    composition includes a number of Parisians at a park on the banks of the River
+    Seine.'
+  - Paleolithic Paleolithic humans made tools of stone, bone, and wood.[23] The early
+    paleolithic hominins, Australopithecus, were the first users of stone tools. Excavations
+    in Gona, Ethiopia have produced thousands of artifacts, and through radioisotopic
+    dating and magnetostratigraphy, the sites can be firmly dated to 2.6 million
+    years ago. Evidence shows these early hominins intentionally selected raw materials
+    with good flaking qualities and chose appropriate sized stones for their needs
+    to produce sharp-edged tools for cutting.[29]
+  - African-American civil rights movement (1954–1968) The Civil Rights Movement (also
+    known as the American civil rights movement, African-American civil rights movement,
+    and other terms,[b]) was a human rights movement from 1954–1968 that encompassed
+    strategies, groups, and social movements to accomplish its goal of ending legalized
+    racial segregation and discrimination laws in the United States. The movement
+    secured the legal recognition and federal protection of black Americans in the
+    United States Constitution and federal law.
+datasets:
+- sentence-transformers/natural-questions
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+metrics:
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+co2_eq_emissions:
+  emissions: 11.486068711625794
+  energy_consumed: 0.029549806050974257
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 0.087
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+model-index:
+- name: Inference-free SPLADE BERT-tiny trained on Natural-Questions tuples
+  results:
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoMSMARCO
+      type: NanoMSMARCO
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.3
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.46
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.52
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.66
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.3
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.15333333333333332
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.10400000000000001
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.066
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.3
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.46
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.52
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.66
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.4613583823584531
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.3998809523809524
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.4174283897822019
+      name: Dot Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNFCorpus
+      type: NanoNFCorpus
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.46
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.56
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.6
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.66
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.46
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.36
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.31200000000000006
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.248
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.0437532639932766
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.07287376477005543
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.109733905482588
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.13321789504016796
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.32305198562670556
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.5245
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.13942100043534664
+      name: Dot Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNQ
+      type: NanoNQ
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.24
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.5
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.64
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.74
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.24
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.16666666666666663
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.128
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.07400000000000001
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.24
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.48
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.61
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.7
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.4672527583679101
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.40066666666666656
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.3953139370042203
+      name: Dot Map@100
+  - task:
+      type: sparse-nano-beir
+      name: Sparse Nano BEIR
+    dataset:
+      name: NanoBEIR mean
+      type: NanoBEIR_mean
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.3333333333333333
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.5066666666666667
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.5866666666666668
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.6866666666666666
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.3333333333333333
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.22666666666666666
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.18133333333333335
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.12933333333333333
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.1945844213310922
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.3376245882566851
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.4132446351608627
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.49773929834672265
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.41722104211768957
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.4416825396825396
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.31738777574058963
+      name: Dot Map@100
+---
+
+# Inference-free SPLADE BERT-tiny trained on Natural-Questions tuples
+
+This is a [Asymmetric Inference-free SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model trained on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset 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
+
+### Model Description
+- **Model Type:** Asymmetric Inference-free SPLADE Sparse Encoder
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 30522 dimensions
+- **Similarity Function:** Dot Product
+- **Training Dataset:**
+    - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
+- **Language:** en
+- **License:** apache-2.0
+
+### 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): Asym(
+    (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
+    (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
+    (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
+  )
+)
+```
+
+## 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 SparseEncoder
+
+# Download from the 🤗 Hub
+model = SparseEncoder("tomaarsen/inference-free-splade-bert-tiny-nq-3e-3-lambda-corpus")
+# Run inference
+sentences = [
+    'when did the american civil rights movement end',
+    'African-American civil rights movement (1954–1968) The Civil Rights Movement (also known as the American civil rights movement, African-American civil rights movement, and other terms,[b]) was a human rights movement from 1954–1968 that encompassed strategies, groups, and social movements to accomplish its goal of ending legalized racial segregation and discrimination laws in the United States. The movement secured the legal recognition and federal protection of black Americans in the United States Constitution and federal law.',
+    'Paleolithic Paleolithic humans made tools of stone, bone, and wood.[23] The early paleolithic hominins, Australopithecus, were the first users of stone tools. Excavations in Gona, Ethiopia have produced thousands of artifacts, and through radioisotopic dating and magnetostratigraphy, the sites can be firmly dated to 2.6Â\xa0million years ago. Evidence shows these early hominins intentionally selected raw materials with good flaking qualities and chose appropriate sized stones for their needs to produce sharp-edged tools for cutting.[29]',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# (3, 30522)
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Sparse Information Retrieval
+
+* Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ`
+* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
+
+| Metric           | NanoMSMARCO | NanoNFCorpus | NanoNQ     |
+|:-----------------|:------------|:-------------|:-----------|
+| dot_accuracy@1   | 0.3         | 0.46         | 0.24       |
+| dot_accuracy@3   | 0.46        | 0.56         | 0.5        |
+| dot_accuracy@5   | 0.52        | 0.6          | 0.64       |
+| dot_accuracy@10  | 0.66        | 0.66         | 0.74       |
+| dot_precision@1  | 0.3         | 0.46         | 0.24       |
+| dot_precision@3  | 0.1533      | 0.36         | 0.1667     |
+| dot_precision@5  | 0.104       | 0.312        | 0.128      |
+| dot_precision@10 | 0.066       | 0.248        | 0.074      |
+| dot_recall@1     | 0.3         | 0.0438       | 0.24       |
+| dot_recall@3     | 0.46        | 0.0729       | 0.48       |
+| dot_recall@5     | 0.52        | 0.1097       | 0.61       |
+| dot_recall@10    | 0.66        | 0.1332       | 0.7        |
+| **dot_ndcg@10**  | **0.4614**  | **0.3231**   | **0.4673** |
+| dot_mrr@10       | 0.3999      | 0.5245       | 0.4007     |
+| dot_map@100      | 0.4174      | 0.1394       | 0.3953     |
+
+#### Sparse Nano BEIR
+
+* Dataset: `NanoBEIR_mean`
+* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ]
+  }
+  ```
+
+| Metric           | Value      |
+|:-----------------|:-----------|
+| dot_accuracy@1   | 0.3333     |
+| dot_accuracy@3   | 0.5067     |
+| dot_accuracy@5   | 0.5867     |
+| dot_accuracy@10  | 0.6867     |
+| dot_precision@1  | 0.3333     |
+| dot_precision@3  | 0.2267     |
+| dot_precision@5  | 0.1813     |
+| dot_precision@10 | 0.1293     |
+| dot_recall@1     | 0.1946     |
+| dot_recall@3     | 0.3376     |
+| dot_recall@5     | 0.4132     |
+| dot_recall@10    | 0.4977     |
+| **dot_ndcg@10**  | **0.4172** |
+| dot_mrr@10       | 0.4417     |
+| dot_map@100      | 0.3174     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### natural-questions
+
+* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
+* Size: 99,000 training samples
+* Columns: <code>query</code> and <code>corpus</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query              | corpus             |
+  |:--------|:-------------------|:-------------------|
+  | type    | dict               | dict               |
+  | details | <ul><li></li></ul> | <ul><li></li></ul> |
+* Samples:
+  | query                                                                      | corpus                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+  |:---------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>{'query': "who played the father in papa don't preach"}</code>       | <code>{'corpus': 'Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.'}</code>                                                                                                                                                                                                                                                                                                                                                     |
+  | <code>{'query': 'where was the location of the battle of hastings'}</code> | <code>{'corpus': 'Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.'}</code> |
+  | <code>{'query': 'how many puppies can a dog give birth to'}</code>         | <code>{'corpus': 'Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]'}</code>                                                                                                                                                                                                                                                      |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {'loss': SparseMultipleNegativesRankingLoss(
+    (model): SparseEncoder(
+      (0): Asym(
+        (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
+        (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
+        (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
+      )
+    )
+    (cross_entropy_loss): CrossEntropyLoss()
+  ), 'lambda_corpus': 0.003, 'lambda_query': 0, 'corpus_regularizer': FlopsLoss(
+    (model): SparseEncoder(
+      (0): Asym(
+        (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
+        (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
+        (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
+      )
+    )
+  ), 'query_regularizer': None}
+  ```
+
+### Evaluation Dataset
+
+#### natural-questions
+
+* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
+* Size: 1,000 evaluation samples
+* Columns: <code>query</code> and <code>corpus</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query              | corpus             |
+  |:--------|:-------------------|:-------------------|
+  | type    | dict               | dict               |
+  | details | <ul><li></li></ul> | <ul><li></li></ul> |
+* Samples:
+  | query                                                               | corpus                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+  |:--------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>{'query': 'where is the tiber river located in italy'}</code> | <code>{'corpus': 'Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252\xa0mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709\xa0sq\xa0mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.'}</code> |
+  | <code>{'query': 'what kind of car does jay gatsby drive'}</code>    | <code>{'corpus': 'Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby\'s yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.'}</code>                                               |
+  | <code>{'query': 'who sings if i can dream about you'}</code>        | <code>{'corpus': 'I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman\'s album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]'}</code>                                                                                                                                                                                                                           |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {'loss': SparseMultipleNegativesRankingLoss(
+    (model): SparseEncoder(
+      (0): Asym(
+        (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
+        (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
+        (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
+      )
+    )
+    (cross_entropy_loss): CrossEntropyLoss()
+  ), 'lambda_corpus': 0.003, 'lambda_query': 0, 'corpus_regularizer': FlopsLoss(
+    (model): SparseEncoder(
+      (0): Asym(
+        (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
+        (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
+        (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
+      )
+    )
+  ), 'query_regularizer': None}
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `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`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 |
+|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|
+| 0.0129 | 20   | 1.5874        | -               | -                       | -                        | -                  | -                         |
+| 0.0259 | 40   | 1.7845        | -               | -                       | -                        | -                  | -                         |
+| 0.0388 | 60   | 1.924         | -               | -                       | -                        | -                  | -                         |
+| 0.0517 | 80   | 1.6441        | -               | -                       | -                        | -                  | -                         |
+| 0.0646 | 100  | 1.1842        | -               | -                       | -                        | -                  | -                         |
+| 0.0776 | 120  | 0.9556        | -               | -                       | -                        | -                  | -                         |
+| 0.0905 | 140  | 0.8623        | -               | -                       | -                        | -                  | -                         |
+| 0.1034 | 160  | 0.7888        | -               | -                       | -                        | -                  | -                         |
+| 0.1164 | 180  | 0.7923        | -               | -                       | -                        | -                  | -                         |
+| 0.1293 | 200  | 0.7464        | 0.6235          | 0.4629                  | 0.3201                   | 0.4511             | 0.4113                    |
+| 0.1422 | 220  | 0.7121        | -               | -                       | -                        | -                  | -                         |
+| 0.1551 | 240  | 0.6795        | -               | -                       | -                        | -                  | -                         |
+| 0.1681 | 260  | 0.7187        | -               | -                       | -                        | -                  | -                         |
+| 0.1810 | 280  | 0.6786        | -               | -                       | -                        | -                  | -                         |
+| 0.1939 | 300  | 0.6608        | -               | -                       | -                        | -                  | -                         |
+| 0.2069 | 320  | 0.6625        | -               | -                       | -                        | -                  | -                         |
+| 0.2198 | 340  | 0.651         | -               | -                       | -                        | -                  | -                         |
+| 0.2327 | 360  | 0.6671        | -               | -                       | -                        | -                  | -                         |
+| 0.2456 | 380  | 0.6732        | -               | -                       | -                        | -                  | -                         |
+| 0.2586 | 400  | 0.6301        | 0.5740          | 0.4243                  | 0.3278                   | 0.4273             | 0.3931                    |
+| 0.2715 | 420  | 0.6375        | -               | -                       | -                        | -                  | -                         |
+| 0.2844 | 440  | 0.6651        | -               | -                       | -                        | -                  | -                         |
+| 0.2973 | 460  | 0.6378        | -               | -                       | -                        | -                  | -                         |
+| 0.3103 | 480  | 0.6592        | -               | -                       | -                        | -                  | -                         |
+| 0.3232 | 500  | 0.6404        | -               | -                       | -                        | -                  | -                         |
+| 0.3361 | 520  | 0.6216        | -               | -                       | -                        | -                  | -                         |
+| 0.3491 | 540  | 0.6072        | -               | -                       | -                        | -                  | -                         |
+| 0.3620 | 560  | 0.6508        | -               | -                       | -                        | -                  | -                         |
+| 0.3749 | 580  | 0.5645        | -               | -                       | -                        | -                  | -                         |
+| 0.3878 | 600  | 0.6275        | 0.4993          | 0.4352                  | 0.3241                   | 0.4227             | 0.3940                    |
+| 0.4008 | 620  | 0.566         | -               | -                       | -                        | -                  | -                         |
+| 0.4137 | 640  | 0.5063        | -               | -                       | -                        | -                  | -                         |
+| 0.4266 | 660  | 0.5297        | -               | -                       | -                        | -                  | -                         |
+| 0.4396 | 680  | 0.5448        | -               | -                       | -                        | -                  | -                         |
+| 0.4525 | 700  | 0.5436        | -               | -                       | -                        | -                  | -                         |
+| 0.4654 | 720  | 0.4771        | -               | -                       | -                        | -                  | -                         |
+| 0.4783 | 740  | 0.5035        | -               | -                       | -                        | -                  | -                         |
+| 0.4913 | 760  | 0.5005        | -               | -                       | -                        | -                  | -                         |
+| 0.5042 | 780  | 0.4509        | -               | -                       | -                        | -                  | -                         |
+| 0.5171 | 800  | 0.4956        | 0.4341          | 0.4596                  | 0.3280                   | 0.4357             | 0.4078                    |
+| 0.5301 | 820  | 0.4876        | -               | -                       | -                        | -                  | -                         |
+| 0.5430 | 840  | 0.4622        | -               | -                       | -                        | -                  | -                         |
+| 0.5559 | 860  | 0.4791        | -               | -                       | -                        | -                  | -                         |
+| 0.5688 | 880  | 0.4608        | -               | -                       | -                        | -                  | -                         |
+| 0.5818 | 900  | 0.451         | -               | -                       | -                        | -                  | -                         |
+| 0.5947 | 920  | 0.4537        | -               | -                       | -                        | -                  | -                         |
+| 0.6076 | 940  | 0.4233        | -               | -                       | -                        | -                  | -                         |
+| 0.6206 | 960  | 0.4534        | -               | -                       | -                        | -                  | -                         |
+| 0.6335 | 980  | 0.4701        | -               | -                       | -                        | -                  | -                         |
+| 0.6464 | 1000 | 0.4017        | 0.4052          | 0.4692                  | 0.3271                   | 0.4452             | 0.4138                    |
+| 0.6593 | 1020 | 0.4518        | -               | -                       | -                        | -                  | -                         |
+| 0.6723 | 1040 | 0.4173        | -               | -                       | -                        | -                  | -                         |
+| 0.6852 | 1060 | 0.4369        | -               | -                       | -                        | -                  | -                         |
+| 0.6981 | 1080 | 0.456         | -               | -                       | -                        | -                  | -                         |
+| 0.7111 | 1100 | 0.448         | -               | -                       | -                        | -                  | -                         |
+| 0.7240 | 1120 | 0.4369        | -               | -                       | -                        | -                  | -                         |
+| 0.7369 | 1140 | 0.4394        | -               | -                       | -                        | -                  | -                         |
+| 0.7498 | 1160 | 0.437         | -               | -                       | -                        | -                  | -                         |
+| 0.7628 | 1180 | 0.4402        | -               | -                       | -                        | -                  | -                         |
+| 0.7757 | 1200 | 0.4382        | 0.3901          | 0.4623                  | 0.3238                   | 0.4664             | 0.4175                    |
+| 0.7886 | 1220 | 0.4111        | -               | -                       | -                        | -                  | -                         |
+| 0.8016 | 1240 | 0.4386        | -               | -                       | -                        | -                  | -                         |
+| 0.8145 | 1260 | 0.4136        | -               | -                       | -                        | -                  | -                         |
+| 0.8274 | 1280 | 0.4439        | -               | -                       | -                        | -                  | -                         |
+| 0.8403 | 1300 | 0.4423        | -               | -                       | -                        | -                  | -                         |
+| 0.8533 | 1320 | 0.4339        | -               | -                       | -                        | -                  | -                         |
+| 0.8662 | 1340 | 0.4124        | -               | -                       | -                        | -                  | -                         |
+| 0.8791 | 1360 | 0.417         | -               | -                       | -                        | -                  | -                         |
+| 0.8920 | 1380 | 0.4067        | -               | -                       | -                        | -                  | -                         |
+| 0.9050 | 1400 | 0.414         | 0.3854          | 0.4591                  | 0.3234                   | 0.4660             | 0.4162                    |
+| 0.9179 | 1420 | 0.4153        | -               | -                       | -                        | -                  | -                         |
+| 0.9308 | 1440 | 0.3889        | -               | -                       | -                        | -                  | -                         |
+| 0.9438 | 1460 | 0.4368        | -               | -                       | -                        | -                  | -                         |
+| 0.9567 | 1480 | 0.4241        | -               | -                       | -                        | -                  | -                         |
+| 0.9696 | 1500 | 0.423         | -               | -                       | -                        | -                  | -                         |
+| 0.9825 | 1520 | 0.4287        | -               | -                       | -                        | -                  | -                         |
+| 0.9955 | 1540 | 0.4282        | -               | -                       | -                        | -                  | -                         |
+| -1     | -1   | -             | -               | 0.4614                  | 0.3231                   | 0.4673             | 0.4172                    |
+
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 0.030 kWh
+- **Carbon Emitted**: 0.011 kg of CO2
+- **Hours Used**: 0.087 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+
+### Framework Versions
+- Python: 3.11.6
+- Sentence Transformers: 4.2.0.dev0
+- Transformers: 4.49.0
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.1
+- Datasets: 2.21.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### SpladeLoss
+```bibtex
+@misc{formal2022distillationhardnegativesampling,
+      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
+      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
+      year={2022},
+      eprint={2205.04733},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2205.04733},
+}
+```
+
+<!--
+## 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 +1,19 @@
+{
+  "types": {
+    "query_0_IDF": "sentence_transformers.sparse_encoder.models.IDF",
+    "corpus_0_MLMTransformer": "sentence_transformers.sparse_encoder.models.MLMTransformer",
+    "corpus_1_SpladePooling": "sentence_transformers.sparse_encoder.models.SpladePooling"
+  },
+  "structure": {
+    "query": [
+      "query_0_IDF"
+    ],
+    "corpus": [
+      "corpus_0_MLMTransformer",
+      "corpus_1_SpladePooling"
+    ]
+  },
+  "parameters": {
+    "allow_empty_key": true
+  }
+}

config_sentence_transformers.json

@@ -0,0 +1,11 @@
+{
+  "model_type": "SparseEncoder",
+  "__version__": {
+    "sentence_transformers": "4.2.0.dev0",
+    "transformers": "4.49.0",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "dot"
+}

corpus_0_MLMTransformer/config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "prajjwal1/bert-tiny",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 128,
+  "initializer_range": 0.02,
+  "intermediate_size": 512,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 2,
+  "num_hidden_layers": 2,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

corpus_0_MLMTransformer/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d2983fbf960a43769fb6808ffe6b9972c43ffab11a0da172241e1c618f25f37
+size 17671560

corpus_0_MLMTransformer/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

corpus_0_MLMTransformer/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

corpus_0_MLMTransformer/tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

corpus_1_SpladePooling/config.json

@@ -0,0 +1,5 @@
+{
+    "pooling_strategy": "max",
+    "activation_function": "relu",
+    "word_embedding_dimension": 30522
+}

modules.json

@@ -0,0 +1,8 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Asym"
+  }
+]

query_0_IDF/config.json

@@ -0,0 +1,3 @@
+{
+    "frozen": false
+}

query_0_IDF/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8f8f624db485734035ed2d9600a93d6a134ccbd25ff4b14618179cfc5bef948
+size 122168

query_0_IDF/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

query_0_IDF/tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}