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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:99000
- loss:SpladeLoss
widget:
- source_sentence: who are the dancers in the limp bizkit rollin video
sentences:
- Voting age Before the Second World War, the voting age in almost all countries
was 21 years or higher. Czechoslovakia was the first to reduce the voting age
to 20 years in 1946, and by 1968 a total of 17 countries had lowered their voting
age.[1] Many countries, particularly in Western Europe, reduced their voting ages
to 18 years during the 1970s, starting with the United Kingdom (1969),[2] with
the United States (26th Amendment) (1971), Canada, West Germany (1972), Australia
(1974), France (1974), and others following soon afterwards. By the end of the
20th century, 18 had become by far the most common voting age. However, a few
countries maintain a voting age of 20 years or higher. It was argued that young
men could be drafted to go to war at 18, and many people felt they should be able
to vote at the age of 18.[3]
- Rollin' (Limp Bizkit song) The music video was filmed atop the South Tower of
the former World Trade Center in New York City. The introduction features Ben
Stiller and Stephen Dorff mistaking Fred Durst for the valet and giving him the
keys to their Bentley Azure. Also making a cameo is break dancer Mr. Wiggles.
The rest of the video has several cuts to Durst and his bandmates hanging out
of the Bentley as they drive about Manhattan. The song Ben Stiller is playing
at the beginning is "My Generation" from the same album. The video also features
scenes of Fred Durst with five girls dancing in a room. The video was filmed around
the same time as the film Zoolander, which explains Stiller and Dorff's appearance.
Fred Durst has a small cameo in that film.
- Eobard Thawne When Thawne reappears, he murders the revived Johnny Quick,[9] before
proceeding to trap Barry and the revived Max Mercury inside the negative Speed
Force. Thawne then attempts to kill Wally West's children through their connection
to the Speed Force in front of Linda Park-West, only to be stopped by Jay Garrick
and Bart Allen. Thawne defeats Jay and prepares to kill Bart, but Barry, Max,
Wally, Jesse Quick, and Impulse arrive to prevent the villain from doing so.[8][10]
In the ensuing fight, Thawne reveals that he is responsible for every tragedy
that has occurred in Barry's life, including the death of his mother. Thawne then
decides to destroy everything the Flash holds dear by killing Barry's wife, Iris,
before they even met.[10]
- source_sentence: who wins season 14 of hell's kitchen
sentences:
- Hell's Kitchen (U.S. season 14) Season 14 of the American competitive reality
television series Hell's Kitchen premiered on March 3, 2015 on Fox. The prize
is a head chef position at Gordon Ramsay Pub & Grill in Caesars Atlantic City.[1]
Gordon Ramsay returned as head chef with Andi Van Willigan and James Avery returning
as sous-chefs for both their respective kitchens as well as Marino Monferrato
as the maître d'. Executive chef Meghan Gill from Roanoke, Virginia, won the
competition, thus becoming the fourteenth winner of Hell's Kitchen.
- 'Maze Runner: The Death Cure On April 22, 2017, the studio delayed the release
date once again, to February 9, 2018, in order to allow more time for post-production;
months later, on August 25, the studio moved the release forward two weeks.[17]
The film will premiere on January 26, 2018 in 3D, IMAX and IMAX 3D.[18][19]'
- North American Plate On its western edge, the Farallon Plate has been subducting
under the North American Plate since the Jurassic Period. The Farallon Plate has
almost completely subducted beneath the western portion of the North American
Plate leaving that part of the North American Plate in contact with the Pacific
Plate as the San Andreas Fault. The Juan de Fuca, Explorer, Gorda, Rivera, Cocos
and Nazca plates are remnants of the Farallon Plate.
- source_sentence: who played the dj in the movie the warriors
sentences:
- List of Arrow episodes As of May 17, 2018,[update] 138 episodes of Arrow have
aired, concluding the sixth season. On April 2, 2018, the CW renewed the series
for a seventh season.[1]
- Lynne Thigpen Cherlynne Theresa "Lynne" Thigpen (December 22, 1948 – March 12,
2003) was an American actress, best known for her role as "The Chief" of ACME
in the various Carmen Sandiego television series and computer games from 1991
to 1997. For her varied television work, Thigpen was nominated for six Daytime
Emmy Awards; she won a Tony Award in 1997 for portraying Dr. Judith Kaufman in
An American Daughter.
- The Washington Post The Washington Post is an American daily newspaper. It is
the most widely circulated newspaper published in Washington, D.C., and was founded
on December 6, 1877,[7] making it the area's oldest extant newspaper. In February
2017, amid a barrage of criticism from President Donald Trump over the paper's
coverage of his campaign and early presidency as well as concerns among the American
press about Trump's criticism and threats against journalists who provide coverage
he deems unfavorable, the Post adopted the slogan "Democracy Dies in Darkness".[8]
- source_sentence: how old was messi when he started his career
sentences:
- Lionel Messi Born and raised in central Argentina, Messi was diagnosed with a
growth hormone deficiency as a child. At age 13, he relocated to Spain to join
Barcelona, who agreed to pay for his medical treatment. After a fast progression
through Barcelona's youth academy, Messi made his competitive debut aged 17 in
October 2004. Despite being injury-prone during his early career, he established
himself as an integral player for the club within the next three years, finishing
2007 as a finalist for both the Ballon d'Or and FIFA World Player of the Year
award, a feat he repeated the following year. His first uninterrupted campaign
came in the 2008–09 season, during which he helped Barcelona achieve the first
treble in Spanish football. At 22 years old, Messi won the Ballon d'Or and FIFA
World Player of the Year award by record voting margins.
- We Are Marshall Filming of We Are Marshall commenced on April 3, 2006, in Huntington,
West Virginia, and was completed in Atlanta, Georgia. The premiere for the film
was held at the Keith Albee Theater on December 12, 2006, in Huntington; other
special screenings were held at Pullman Square. The movie was released nationwide
on December 22, 2006.
- One Fish, Two Fish, Red Fish, Blue Fish One Fish, Two Fish, Red Fish, Blue Fish
is a 1960 children's book by Dr. Seuss. It is a simple rhyming book for beginning
readers, with a freewheeling plot about a boy and a girl named Jay and Kay and
the many amazing creatures they have for friends and pets. Interspersed are some
rather surreal and unrelated skits, such as a man named Ned whose feet stick out
from his bed, and a creature who has a bird in his ear. As of 2001, over 6 million
copies of the book had been sold, placing it 13th on a list of "All-Time Bestselling
Children's Books" from Publishers Weekly.[1] Based on a 2007 online poll, the
United States' National Education Association labor union named the book one of
its "Teachers' Top 100 Books for Children."[2]
- source_sentence: is send in the clowns from a musical
sentences:
- Money in the Bank ladder match The first match was contested in 2005 at WrestleMania
21, after being invented (in kayfabe) by Chris Jericho.[1] At the time, it was
exclusive to wrestlers of the Raw brand, and Edge won the inaugural match.[1]
From then until 2010, the Money in the Bank ladder match, now open to all WWE
brands, became a WrestleMania mainstay. 2010 saw a second and third Money in the
Bank ladder match when the Money in the Bank pay-per-view debuted in July. Unlike
the matches at WrestleMania, this new event featured two such ladder matches –
one each for a contract for the WWE Championship and World Heavyweight Championship,
respectively.
- The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired
on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off
of the Disney Channel Original Series The Suite Life of Zack & Cody. The series
follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in
a new setting, the SS Tipton, where they attend classes at "Seven Seas High School"
and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around
the world to nations such as Italy, France, Greece, India, Sweden and the United
Kingdom where the characters experience different cultures, adventures, and situations.[1]
- 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim
for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman''s
film Smiles of a Summer Night. It is a ballad from Act Two, in which the character
Desirée reflects on the ironies and disappointments of her life. Among other things,
she looks back on an affair years earlier with the lawyer Fredrik, who was deeply
in love with her but whose marriage proposals she had rejected. Meeting him after
so long, she realizes she is in love with him and finally ready to marry him,
but now it is he who rejects her: he is in an unconsummated marriage with a much
younger woman. Desirée proposes marriage to rescue him from this situation, but
he declines, citing his dedication to his bride. Reacting to his rejection, Desirée
sings this song. The song is later reprised as a coda after Fredrik''s young wife
runs away with his son, and Fredrik is finally free to accept Desirée''s offer.[1]'
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: 10.656630177765601
energy_consumed: 0.027415938631047954
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: 0.082
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: 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: 0.22
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.36
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.4
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.54
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.22
name: Dot Precision@1
- type: dot_precision@3
value: 0.11999999999999998
name: Dot Precision@3
- type: dot_precision@5
value: 0.08000000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.054000000000000006
name: Dot Precision@10
- type: dot_recall@1
value: 0.22
name: Dot Recall@1
- type: dot_recall@3
value: 0.36
name: Dot Recall@3
- type: dot_recall@5
value: 0.4
name: Dot Recall@5
- type: dot_recall@10
value: 0.54
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3571008976876618
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.30113492063492064
name: Dot Mrr@10
- type: dot_map@100
value: 0.31600753362153616
name: Dot Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.24
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.38
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.48
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.58
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.24
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.184
name: Dot Precision@5
- type: dot_precision@10
value: 0.16
name: Dot Precision@10
- type: dot_recall@1
value: 0.01910619386686893
name: Dot Recall@1
- type: dot_recall@3
value: 0.03647891009411463
name: Dot Recall@3
- type: dot_recall@5
value: 0.043286562520389434
name: Dot Recall@5
- type: dot_recall@10
value: 0.0624423217616165
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.1824659316003306
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3309444444444445
name: Dot Mrr@10
- type: dot_map@100
value: 0.0640015611933746
name: Dot Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.1
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.24
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.34
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.44
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.1
name: Dot Precision@1
- type: dot_precision@3
value: 0.08
name: Dot Precision@3
- type: dot_precision@5
value: 0.068
name: Dot Precision@5
- type: dot_precision@10
value: 0.046000000000000006
name: Dot Precision@10
- type: dot_recall@1
value: 0.09
name: Dot Recall@1
- type: dot_recall@3
value: 0.22
name: Dot Recall@3
- type: dot_recall@5
value: 0.32
name: Dot Recall@5
- type: dot_recall@10
value: 0.41
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.24844109892252747
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.20507142857142857
name: Dot Mrr@10
- type: dot_map@100
value: 0.208667797146501
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: 0.18666666666666665
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.32666666666666666
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.4066666666666667
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.52
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.18666666666666665
name: Dot Precision@1
- type: dot_precision@3
value: 0.13777777777777778
name: Dot Precision@3
- type: dot_precision@5
value: 0.11066666666666668
name: Dot Precision@5
- type: dot_precision@10
value: 0.08666666666666667
name: Dot Precision@10
- type: dot_recall@1
value: 0.10970206462228964
name: Dot Recall@1
- type: dot_recall@3
value: 0.20549297003137154
name: Dot Recall@3
- type: dot_recall@5
value: 0.25442885417346317
name: Dot Recall@5
- type: dot_recall@10
value: 0.3374807739205388
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.26266930940350663
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.27905026455026455
name: Dot Mrr@10
- type: dot_map@100
value: 0.1962256306538039
name: Dot Map@100
---
# SPLADE BERT-tiny trained on Natural-Questions tuples
This is a [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.
This model was trained using [train_script.py](train_script.py).
## Model Details
### Model Description
- **Model Type:** 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): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
(1): 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("sparse-encoder-testing/splade-bert-tiny-nq")
# Run inference
sentences = [
'is send in the clowns from a musical',
'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]',
'The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off of the Disney Channel Original Series The Suite Life of Zack & Cody. The series follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in a new setting, the SS Tipton, where they attend classes at "Seven Seas High School" and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around the world to nations such as Italy, France, Greece, India, Sweden and the United Kingdom where the characters experience different cultures, adventures, and situations.[1]',
]
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 | 0.22 | 0.24 | 0.1 |
| dot_accuracy@3 | 0.36 | 0.38 | 0.24 |
| dot_accuracy@5 | 0.4 | 0.48 | 0.34 |
| dot_accuracy@10 | 0.54 | 0.58 | 0.44 |
| dot_precision@1 | 0.22 | 0.24 | 0.1 |
| dot_precision@3 | 0.12 | 0.2133 | 0.08 |
| dot_precision@5 | 0.08 | 0.184 | 0.068 |
| dot_precision@10 | 0.054 | 0.16 | 0.046 |
| dot_recall@1 | 0.22 | 0.0191 | 0.09 |
| dot_recall@3 | 0.36 | 0.0365 | 0.22 |
| dot_recall@5 | 0.4 | 0.0433 | 0.32 |
| dot_recall@10 | 0.54 | 0.0624 | 0.41 |
| **dot_ndcg@10** | **0.3571** | **0.1825** | **0.2484** |
| dot_mrr@10 | 0.3011 | 0.3309 | 0.2051 |
| dot_map@100 | 0.316 | 0.064 | 0.2087 |
#### 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 | 0.1867 |
| dot_accuracy@3 | 0.3267 |
| dot_accuracy@5 | 0.4067 |
| dot_accuracy@10 | 0.52 |
| dot_precision@1 | 0.1867 |
| dot_precision@3 | 0.1378 |
| dot_precision@5 | 0.1107 |
| dot_precision@10 | 0.0867 |
| dot_recall@1 | 0.1097 |
| dot_recall@3 | 0.2055 |
| dot_recall@5 | 0.2544 |
| dot_recall@10 | 0.3375 |
| **dot_ndcg@10** | **0.2627** |
| dot_mrr@10 | 0.2791 |
| dot_map@100 | 0.1962 |
<!--
## Bias, Risks and Limitations
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## 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>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 29 characters</li><li>mean: 46.96 characters</li><li>max: 93 characters</li></ul> | <ul><li>min: 10 characters</li><li>mean: 582.13 characters</li><li>max: 2141 characters</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> |
| <code>where was the location of the battle of hastings</code> | <code>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>how many puppies can a dog give birth to</code> | <code>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): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
(cross_entropy_loss): CrossEntropyLoss()
), 'lambda_corpus': 3e-05, 'lambda_query': 5e-05, 'corpus_regularizer': FlopsLoss(
(model): SparseEncoder(
(0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
), 'query_regularizer': FlopsLoss(
(model): SparseEncoder(
(0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
)}
```
### 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>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 30 characters</li><li>mean: 47.2 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 58 characters</li><li>mean: 598.96 characters</li><li>max: 2480 characters</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>where is the tiber river located in italy</code> | <code>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.</code> |
| <code>what kind of car does jay gatsby drive</code> | <code>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>who sings if i can dream about you</code> | <code>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): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
(cross_entropy_loss): CrossEntropyLoss()
), 'lambda_corpus': 3e-05, 'lambda_query': 5e-05, 'corpus_regularizer': FlopsLoss(
(model): SparseEncoder(
(0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
), 'query_regularizer': FlopsLoss(
(model): SparseEncoder(
(0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
)}
```
### 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 | 1688.0217 | - | - | - | - | - |
| 0.0259 | 40 | 1557.9103 | - | - | - | - | - |
| 0.0388 | 60 | 1205.4178 | - | - | - | - | - |
| 0.0517 | 80 | 692.3048 | - | - | - | - | - |
| 0.0646 | 100 | 297.4244 | - | - | - | - | - |
| 0.0776 | 120 | 144.2392 | - | - | - | - | - |
| 0.0905 | 140 | 75.7438 | - | - | - | - | - |
| 0.1034 | 160 | 35.3506 | - | - | - | - | - |
| 0.1164 | 180 | 20.7095 | - | - | - | - | - |
| 0.1293 | 200 | 12.5446 | 6.9048 | 0.1524 | 0.0784 | 0.0574 | 0.0961 |
| 0.1422 | 220 | 8.1351 | - | - | - | - | - |
| 0.1551 | 240 | 6.1495 | - | - | - | - | - |
| 0.1681 | 260 | 4.4986 | - | - | - | - | - |
| 0.1810 | 280 | 3.5353 | - | - | - | - | - |
| 0.1939 | 300 | 3.0714 | - | - | - | - | - |
| 0.2069 | 320 | 2.4237 | - | - | - | - | - |
| 0.2198 | 340 | 1.9325 | - | - | - | - | - |
| 0.2327 | 360 | 1.8585 | - | - | - | - | - |
| 0.2456 | 380 | 1.491 | - | - | - | - | - |
| 0.2586 | 400 | 1.4503 | 0.8541 | 0.2248 | 0.1322 | 0.1045 | 0.1538 |
| 0.2715 | 420 | 1.3789 | - | - | - | - | - |
| 0.2844 | 440 | 1.3195 | - | - | - | - | - |
| 0.2973 | 460 | 1.198 | - | - | - | - | - |
| 0.3103 | 480 | 1.1532 | - | - | - | - | - |
| 0.3232 | 500 | 1.1931 | - | - | - | - | - |
| 0.3361 | 520 | 1.1989 | - | - | - | - | - |
| 0.3491 | 540 | 1.008 | - | - | - | - | - |
| 0.3620 | 560 | 0.9798 | - | - | - | - | - |
| 0.3749 | 580 | 0.9551 | - | - | - | - | - |
| 0.3878 | 600 | 0.9687 | 0.4356 | 0.2709 | 0.1438 | 0.1519 | 0.1888 |
| 0.4008 | 620 | 0.8331 | - | - | - | - | - |
| 0.4137 | 640 | 0.6947 | - | - | - | - | - |
| 0.4266 | 660 | 0.7768 | - | - | - | - | - |
| 0.4396 | 680 | 0.7101 | - | - | - | - | - |
| 0.4525 | 700 | 0.6902 | - | - | - | - | - |
| 0.4654 | 720 | 0.6766 | - | - | - | - | - |
| 0.4783 | 740 | 0.6001 | - | - | - | - | - |
| 0.4913 | 760 | 0.6231 | - | - | - | - | - |
| 0.5042 | 780 | 0.5953 | - | - | - | - | - |
| 0.5171 | 800 | 0.6846 | 0.3068 | 0.2958 | 0.1543 | 0.2071 | 0.2190 |
| 0.5301 | 820 | 0.5851 | - | - | - | - | - |
| 0.5430 | 840 | 0.579 | - | - | - | - | - |
| 0.5559 | 860 | 0.5659 | - | - | - | - | - |
| 0.5688 | 880 | 0.553 | - | - | - | - | - |
| 0.5818 | 900 | 0.4812 | - | - | - | - | - |
| 0.5947 | 920 | 0.5389 | - | - | - | - | - |
| 0.6076 | 940 | 0.4658 | - | - | - | - | - |
| 0.6206 | 960 | 0.5309 | - | - | - | - | - |
| 0.6335 | 980 | 0.484 | - | - | - | - | - |
| 0.6464 | 1000 | 0.4655 | 0.2527 | 0.3131 | 0.1660 | 0.2294 | 0.2362 |
| 0.6593 | 1020 | 0.5617 | - | - | - | - | - |
| 0.6723 | 1040 | 0.4786 | - | - | - | - | - |
| 0.6852 | 1060 | 0.5561 | - | - | - | - | - |
| 0.6981 | 1080 | 0.4869 | - | - | - | - | - |
| 0.7111 | 1100 | 0.5134 | - | - | - | - | - |
| 0.7240 | 1120 | 0.4702 | - | - | - | - | - |
| 0.7369 | 1140 | 0.4481 | - | - | - | - | - |
| 0.7498 | 1160 | 0.4758 | - | - | - | - | - |
| 0.7628 | 1180 | 0.4625 | - | - | - | - | - |
| 0.7757 | 1200 | 0.4733 | 0.2330 | 0.3498 | 0.1748 | 0.2357 | 0.2534 |
| 0.7886 | 1220 | 0.4527 | - | - | - | - | - |
| 0.8016 | 1240 | 0.4735 | - | - | - | - | - |
| 0.8145 | 1260 | 0.3818 | - | - | - | - | - |
| 0.8274 | 1280 | 0.4546 | - | - | - | - | - |
| 0.8403 | 1300 | 0.4724 | - | - | - | - | - |
| 0.8533 | 1320 | 0.4194 | - | - | - | - | - |
| 0.8662 | 1340 | 0.4352 | - | - | - | - | - |
| 0.8791 | 1360 | 0.3926 | - | - | - | - | - |
| 0.8920 | 1380 | 0.397 | - | - | - | - | - |
| 0.9050 | 1400 | 0.4157 | 0.2206 | 0.3558 | 0.1785 | 0.2495 | 0.2613 |
| 0.9179 | 1420 | 0.4426 | - | - | - | - | - |
| 0.9308 | 1440 | 0.4077 | - | - | - | - | - |
| 0.9438 | 1460 | 0.4227 | - | - | - | - | - |
| 0.9567 | 1480 | 0.4184 | - | - | - | - | - |
| 0.9696 | 1500 | 0.4838 | - | - | - | - | - |
| 0.9825 | 1520 | 0.4991 | - | - | - | - | - |
| 0.9955 | 1540 | 0.3889 | - | - | - | - | - |
| -1 | -1 | - | - | 0.3571 | 0.1825 | 0.2484 | 0.2627 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.027 kWh
- **Carbon Emitted**: 0.011 kg of CO2
- **Hours Used**: 0.082 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},
}
```
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