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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- asymmetric
- inference-free
- splade
- generated_from_trainer
- dataset_size:99000
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
widget:
- text: 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.
- text: '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]'
- text: who played the dj in the movie the warriors
- text: 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.
- text: '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
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 12.160208585908531
energy_consumed: 0.03128414205717627
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: 0.09
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: 0.28
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.48
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.54
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.28
name: Dot Precision@1
- type: dot_precision@3
value: 0.15999999999999998
name: Dot Precision@3
- type: dot_precision@5
value: 0.10800000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.068
name: Dot Precision@10
- type: dot_recall@1
value: 0.28
name: Dot Recall@1
- type: dot_recall@3
value: 0.48
name: Dot Recall@3
- type: dot_recall@5
value: 0.54
name: Dot Recall@5
- type: dot_recall@10
value: 0.68
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4712098455669033
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4061269841269841
name: Dot Mrr@10
- type: dot_map@100
value: 0.42123222050853626
name: Dot Map@100
- type: query_active_dims
value: 7.360000133514404
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9997588624554906
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 181.48126220703125
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9940540835395113
name: Corpus Sparsity Ratio
- 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: 0.6
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.46
name: Dot Precision@1
- type: dot_precision@3
value: 0.38666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.34
name: Dot Precision@5
- type: dot_precision@10
value: 0.264
name: Dot Precision@10
- type: dot_recall@1
value: 0.04441960931285628
name: Dot Recall@1
- type: dot_recall@3
value: 0.07855216438081768
name: Dot Recall@3
- type: dot_recall@5
value: 0.11501385338572513
name: Dot Recall@5
- type: dot_recall@10
value: 0.13799680508768822
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3363725092471443
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5348571428571429
name: Dot Mrr@10
- type: dot_map@100
value: 0.14273057100379044
name: Dot Map@100
- type: query_active_dims
value: 5.739999771118164
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9998119389367958
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 267.9966125488281
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9912195592507428
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.18666666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.14
name: Dot Precision@5
- type: dot_precision@10
value: 0.07200000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.3
name: Dot Recall@1
- type: dot_recall@3
value: 0.54
name: Dot Recall@3
- type: dot_recall@5
value: 0.66
name: Dot Recall@5
- type: dot_recall@10
value: 0.67
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.498972350043216
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4476666666666666
name: Dot Mrr@10
- type: dot_map@100
value: 0.44805918373861153
name: Dot Map@100
- type: query_active_dims
value: 10.420000076293945
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.999658606903994
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 156.2409210205078
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9948810392169416
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: dot_accuracy@1
value: 0.3466666666666667
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5466666666666667
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6266666666666667
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.6933333333333334
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3466666666666667
name: Dot Precision@1
- type: dot_precision@3
value: 0.24444444444444444
name: Dot Precision@3
- type: dot_precision@5
value: 0.19600000000000004
name: Dot Precision@5
- type: dot_precision@10
value: 0.13466666666666668
name: Dot Precision@10
- type: dot_recall@1
value: 0.20813986977095209
name: Dot Recall@1
- type: dot_recall@3
value: 0.3661840547936059
name: Dot Recall@3
- type: dot_recall@5
value: 0.43833795112857504
name: Dot Recall@5
- type: dot_recall@10
value: 0.4959989350292295
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4355182349524212
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4628835978835979
name: Dot Mrr@10
- type: dot_map@100
value: 0.33734065841697936
name: Dot Map@100
- type: query_active_dims
value: 7.839999993642171
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9997431360987601
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 191.33428282595386
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9937312665347632
name: Corpus Sparsity Ratio
---
# 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): Router(
(query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
(document_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
(document_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-fresh-3e-2-lambda-corpus-1e-3-idf-lr-2e-5-lr")
# 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 | 0.28 | 0.46 | 0.3 |
| dot_accuracy@3 | 0.48 | 0.6 | 0.56 |
| dot_accuracy@5 | 0.54 | 0.64 | 0.7 |
| dot_accuracy@10 | 0.68 | 0.7 | 0.7 |
| dot_precision@1 | 0.28 | 0.46 | 0.3 |
| dot_precision@3 | 0.16 | 0.3867 | 0.1867 |
| dot_precision@5 | 0.108 | 0.34 | 0.14 |
| dot_precision@10 | 0.068 | 0.264 | 0.072 |
| dot_recall@1 | 0.28 | 0.0444 | 0.3 |
| dot_recall@3 | 0.48 | 0.0786 | 0.54 |
| dot_recall@5 | 0.54 | 0.115 | 0.66 |
| dot_recall@10 | 0.68 | 0.138 | 0.67 |
| **dot_ndcg@10** | **0.4712** | **0.3364** | **0.499** |
| dot_mrr@10 | 0.4061 | 0.5349 | 0.4477 |
| dot_map@100 | 0.4212 | 0.1427 | 0.4481 |
| query_active_dims | 7.36 | 5.74 | 10.42 |
| query_sparsity_ratio | 0.9998 | 0.9998 | 0.9997 |
| corpus_active_dims | 181.4813 | 267.9966 | 156.2409 |
| corpus_sparsity_ratio | 0.9941 | 0.9912 | 0.9949 |
#### 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 | 0.3467 |
| dot_accuracy@3 | 0.5467 |
| dot_accuracy@5 | 0.6267 |
| dot_accuracy@10 | 0.6933 |
| dot_precision@1 | 0.3467 |
| dot_precision@3 | 0.2444 |
| dot_precision@5 | 0.196 |
| dot_precision@10 | 0.1347 |
| dot_recall@1 | 0.2081 |
| dot_recall@3 | 0.3662 |
| dot_recall@5 | 0.4383 |
| dot_recall@10 | 0.496 |
| **dot_ndcg@10** | **0.4355** |
| dot_mrr@10 | 0.4629 |
| dot_map@100 | 0.3373 |
| query_active_dims | 7.84 |
| query_sparsity_ratio | 0.9997 |
| corpus_active_dims | 191.3343 |
| corpus_sparsity_ratio | 0.9937 |
<!--
## 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>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</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(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 0.03,
"lambda_query": 0
}
```
### 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: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</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(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 0.03,
"lambda_query": 0
}
```
### 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
- `router_mapping`: {'query': 'query', 'answer': 'document'}
- `learning_rate_mapping`: {'IDF\\.weight': 0.001}
#### 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
- `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
- `router_mapping`: {'query': 'query', 'answer': 'document'}
- `learning_rate_mapping`: {'IDF\\.weight': 0.001}
</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 | 1.8729 | - | - | - | - | - |
| 0.0259 | 40 | 4.3293 | - | - | - | - | - |
| 0.0388 | 60 | 7.3157 | - | - | - | - | - |
| 0.0517 | 80 | 7.3718 | - | - | - | - | - |
| 0.0646 | 100 | 5.171 | - | - | - | - | - |
| 0.0776 | 120 | 3.5119 | - | - | - | - | - |
| 0.0905 | 140 | 2.6883 | - | - | - | - | - |
| 0.1034 | 160 | 2.2642 | - | - | - | - | - |
| 0.1164 | 180 | 1.9244 | - | - | - | - | - |
| 0.1293 | 200 | 1.6712 | 1.2603 | 0.3854 | 0.3158 | 0.4024 | 0.3679 |
| 0.1422 | 220 | 1.4993 | - | - | - | - | - |
| 0.1551 | 240 | 1.3321 | - | - | - | - | - |
| 0.1681 | 260 | 1.2798 | - | - | - | - | - |
| 0.1810 | 280 | 1.1572 | - | - | - | - | - |
| 0.1939 | 300 | 1.0751 | - | - | - | - | - |
| 0.2069 | 320 | 1.0125 | - | - | - | - | - |
| 0.2198 | 340 | 0.9666 | - | - | - | - | - |
| 0.2327 | 360 | 0.935 | - | - | - | - | - |
| 0.2456 | 380 | 0.8799 | - | - | - | - | - |
| 0.2586 | 400 | 0.8102 | 0.7086 | 0.4184 | 0.3178 | 0.4684 | 0.4015 |
| 0.2715 | 420 | 0.7882 | - | - | - | - | - |
| 0.2844 | 440 | 0.8081 | - | - | - | - | - |
| 0.2973 | 460 | 0.7592 | - | - | - | - | - |
| 0.3103 | 480 | 0.7707 | - | - | - | - | - |
| 0.3232 | 500 | 0.7704 | - | - | - | - | - |
| 0.3361 | 520 | 0.7467 | - | - | - | - | - |
| 0.3491 | 540 | 0.7128 | - | - | - | - | - |
| 0.3620 | 560 | 0.7659 | - | - | - | - | - |
| 0.3749 | 580 | 0.6987 | - | - | - | - | - |
| 0.3878 | 600 | 0.7579 | 0.6132 | 0.4346 | 0.3186 | 0.4910 | 0.4147 |
| 0.4008 | 620 | 0.7029 | - | - | - | - | - |
| 0.4137 | 640 | 0.6148 | - | - | - | - | - |
| 0.4266 | 660 | 0.6393 | - | - | - | - | - |
| 0.4396 | 680 | 0.6764 | - | - | - | - | - |
| 0.4525 | 700 | 0.6586 | - | - | - | - | - |
| 0.4654 | 720 | 0.5964 | - | - | - | - | - |
| 0.4783 | 740 | 0.6263 | - | - | - | - | - |
| 0.4913 | 760 | 0.6045 | - | - | - | - | - |
| 0.5042 | 780 | 0.5662 | - | - | - | - | - |
| 0.5171 | 800 | 0.6092 | 0.5510 | 0.4367 | 0.3269 | 0.4902 | 0.4179 |
| 0.5301 | 820 | 0.6066 | - | - | - | - | - |
| 0.5430 | 840 | 0.5914 | - | - | - | - | - |
| 0.5559 | 860 | 0.608 | - | - | - | - | - |
| 0.5688 | 880 | 0.5745 | - | - | - | - | - |
| 0.5818 | 900 | 0.5733 | - | - | - | - | - |
| 0.5947 | 920 | 0.5631 | - | - | - | - | - |
| 0.6076 | 940 | 0.5444 | - | - | - | - | - |
| 0.6206 | 960 | 0.5588 | - | - | - | - | - |
| 0.6335 | 980 | 0.5975 | - | - | - | - | - |
| 0.6464 | 1000 | 0.5211 | 0.5213 | 0.4450 | 0.3315 | 0.4922 | 0.4229 |
| 0.6593 | 1020 | 0.5496 | - | - | - | - | - |
| 0.6723 | 1040 | 0.5321 | - | - | - | - | - |
| 0.6852 | 1060 | 0.5474 | - | - | - | - | - |
| 0.6981 | 1080 | 0.5752 | - | - | - | - | - |
| 0.7111 | 1100 | 0.5567 | - | - | - | - | - |
| 0.7240 | 1120 | 0.5332 | - | - | - | - | - |
| 0.7369 | 1140 | 0.5591 | - | - | - | - | - |
| 0.7498 | 1160 | 0.5345 | - | - | - | - | - |
| 0.7628 | 1180 | 0.5521 | - | - | - | - | - |
| 0.7757 | 1200 | 0.5581 | 0.5031 | 0.4640 | 0.3333 | 0.4904 | 0.4292 |
| 0.7886 | 1220 | 0.538 | - | - | - | - | - |
| 0.8016 | 1240 | 0.5487 | - | - | - | - | - |
| 0.8145 | 1260 | 0.5273 | - | - | - | - | - |
| 0.8274 | 1280 | 0.5431 | - | - | - | - | - |
| 0.8403 | 1300 | 0.5618 | - | - | - | - | - |
| 0.8533 | 1320 | 0.5379 | - | - | - | - | - |
| 0.8662 | 1340 | 0.5302 | - | - | - | - | - |
| 0.8791 | 1360 | 0.5268 | - | - | - | - | - |
| 0.8920 | 1380 | 0.5336 | - | - | - | - | - |
| 0.9050 | 1400 | 0.5189 | 0.4937 | 0.4716 | 0.3359 | 0.4971 | 0.4348 |
| 0.9179 | 1420 | 0.5221 | - | - | - | - | - |
| 0.9308 | 1440 | 0.4935 | - | - | - | - | - |
| 0.9438 | 1460 | 0.5454 | - | - | - | - | - |
| 0.9567 | 1480 | 0.5224 | - | - | - | - | - |
| 0.9696 | 1500 | 0.5315 | - | - | - | - | - |
| 0.9825 | 1520 | 0.5307 | - | - | - | - | - |
| 0.9955 | 1540 | 0.5303 | - | - | - | - | - |
| -1 | -1 | - | - | 0.4712 | 0.3364 | 0.4990 | 0.4355 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.031 kWh
- **Carbon Emitted**: 0.012 kg of CO2
- **Hours Used**: 0.09 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: 4.52.3
- 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},
}
```
#### SparseMultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### FlopsLoss
```bibtex
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}
```
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