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inference-free-splade-distilbert-base-nq-fresh-3e-2-lambda-corpus-1e-3-idf-lr-2e-5-lr

Feature Extraction
sentence-transformers
Safetensors
English
sparse-encoder
sparse
asymmetric
inference-free
splade
Generated from Trainer
dataset_size:99000
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results
Model card Files
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inference-free-splade-distilbert-base-nq-fresh-3e-2-lambda-corpus-1e-3-idf-lr-2e-5-lr / README.md
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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: document
- 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: 39.462120077254895
energy_consumed: 0.10152281201860267
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: 0.265
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: Inference-free SPLADE DistilBERT-base 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: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.132
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.34
name: Dot Recall@1
- type: dot_recall@3
value: 0.58
name: Dot Recall@3
- type: dot_recall@5
value: 0.66
name: Dot Recall@5
- type: dot_recall@10
value: 0.7
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5231308292979258
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4656904761904761
name: Dot Mrr@10
- type: dot_map@100
value: 0.4836285492285492
name: Dot Map@100
- type: query_active_dims
value: 7.21999979019165
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.999763449322122
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 168.1092529296875
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9944921940590498
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.58
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.62
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.3666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.32400000000000007
name: Dot Precision@5
- type: dot_precision@10
value: 0.266
name: Dot Precision@10
- type: dot_recall@1
value: 0.02400303009002023
name: Dot Recall@1
- type: dot_recall@3
value: 0.07539297398118948
name: Dot Recall@3
- type: dot_recall@5
value: 0.11279299258138195
name: Dot Recall@5
- type: dot_recall@10
value: 0.14025308960153313
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.32970875085690265
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5055238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.13622477789407875
name: Dot Map@100
- type: query_active_dims
value: 5.659999847412109
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9998145599945151
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 200.1533966064453
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.993442323681068
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.38
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.54
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.72
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38
name: Dot Precision@1
- type: dot_precision@3
value: 0.18
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: 0.35
name: Dot Recall@1
- type: dot_recall@3
value: 0.5
name: Dot Recall@3
- type: dot_recall@5
value: 0.6
name: Dot Recall@5
- type: dot_recall@10
value: 0.68
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5165892421271832
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.48800000000000004
name: Dot Mrr@10
- type: dot_map@100
value: 0.46127025074200567
name: Dot Map@100
- type: query_active_dims
value: 10.319999694824219
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9996618832417657
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 192.09732055664062
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9937062669367458
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: 0.3866666666666667
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6266666666666666
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3866666666666667
name: Dot Precision@1
- type: dot_precision@3
value: 0.24666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.19466666666666668
name: Dot Precision@5
- type: dot_precision@10
value: 0.1366666666666667
name: Dot Precision@10
- type: dot_recall@1
value: 0.23800101003000673
name: Dot Recall@1
- type: dot_recall@3
value: 0.38513099132706313
name: Dot Recall@3
- type: dot_recall@5
value: 0.457597664193794
name: Dot Recall@5
- type: dot_recall@10
value: 0.5067510298671777
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4564762740940038
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.48640476190476184
name: Dot Mrr@10
- type: dot_map@100
value: 0.3603745259548779
name: Dot Map@100
- type: query_active_dims
value: 7.733333110809326
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.999746630852801
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 184.6395481318343
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9939506078195455
name: Corpus Sparsity Ratio
---
# Inference-free SPLADE DistilBERT-base 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: DistilBertTokenizerFast)
(document_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM
(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-distilbert-base-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]',
'query',
]
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 | 0.34 | 0.44 | 0.38 |
| dot_accuracy@3 | 0.58 | 0.56 | 0.54 |
| dot_accuracy@5 | 0.66 | 0.58 | 0.64 |
| dot_accuracy@10 | 0.7 | 0.62 | 0.72 |
| dot_precision@1 | 0.34 | 0.44 | 0.38 |
| dot_precision@3 | 0.1933 | 0.3667 | 0.18 |
| dot_precision@5 | 0.132 | 0.324 | 0.128 |
| dot_precision@10 | 0.07 | 0.266 | 0.074 |
| dot_recall@1 | 0.34 | 0.024 | 0.35 |
| dot_recall@3 | 0.58 | 0.0754 | 0.5 |
| dot_recall@5 | 0.66 | 0.1128 | 0.6 |
| dot_recall@10 | 0.7 | 0.1403 | 0.68 |
| **dot_ndcg@10** | **0.5231** | **0.3297** | **0.5166** |
| dot_mrr@10 | 0.4657 | 0.5055 | 0.488 |
| dot_map@100 | 0.4836 | 0.1362 | 0.4613 |
| query_active_dims | 7.22 | 5.66 | 10.32 |
| query_sparsity_ratio | 0.9998 | 0.9998 | 0.9997 |
| corpus_active_dims | 168.1093 | 200.1534 | 192.0973 |
| corpus_sparsity_ratio | 0.9945 | 0.9934 | 0.9937 |
#### 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 | 0.3867 |
| dot_accuracy@3 | 0.56 |
| dot_accuracy@5 | 0.6267 |
| dot_accuracy@10 | 0.68 |
| dot_precision@1 | 0.3867 |
| dot_precision@3 | 0.2467 |
| dot_precision@5 | 0.1947 |
| dot_precision@10 | 0.1367 |
| dot_recall@1 | 0.238 |
| dot_recall@3 | 0.3851 |
| dot_recall@5 | 0.4576 |
| dot_recall@10 | 0.5068 |
| **dot_ndcg@10** | **0.4565** |
| dot_mrr@10 | 0.4864 |
| dot_map@100 | 0.3604 |
| query_active_dims | 7.7333 |
| query_sparsity_ratio | 0.9997 |
| corpus_active_dims | 184.6395 |
| corpus_sparsity_ratio | 0.994 |
<!--
## 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`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
- `router_mapping`: ['query', '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`: 16
- `per_device_eval_batch_size`: 16
- `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', '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 |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|
| 0.0323 | 200 | 0.4068 | - | - | - | - | - |
| 0.0646 | 400 | 0.1732 | 0.1685 | 0.5444 | 0.3099 | 0.4747 | 0.4430 |
| 0.0970 | 600 | 0.1482 | - | - | - | - | - |
| 0.1293 | 800 | 0.1582 | 0.1536 | 0.5562 | 0.3119 | 0.4981 | 0.4554 |
| 0.1616 | 1000 | 0.1549 | - | - | - | - | - |
| 0.1939 | 1200 | 0.1617 | 0.1819 | 0.5073 | 0.3230 | 0.5270 | 0.4524 |
| 0.2262 | 1400 | 0.1776 | - | - | - | - | - |
| 0.2586 | 1600 | 0.1944 | 0.2114 | 0.5079 | 0.3248 | 0.4943 | 0.4423 |
| 0.2909 | 1800 | 0.2176 | - | - | - | - | - |
| 0.3232 | 2000 | 0.2333 | 0.2477 | 0.5255 | 0.3259 | 0.5263 | 0.4592 |
| 0.3555 | 2200 | 0.2408 | - | - | - | - | - |
| 0.3878 | 2400 | 0.2397 | 0.2477 | 0.5242 | 0.3259 | 0.5156 | 0.4552 |
| 0.4202 | 2600 | 0.2324 | - | - | - | - | - |
| 0.4525 | 2800 | 0.2388 | 0.2254 | 0.4988 | 0.3292 | 0.4893 | 0.4391 |
| 0.4848 | 3000 | 0.2154 | - | - | - | - | - |
| 0.5171 | 3200 | 0.2238 | 0.2258 | 0.5165 | 0.3280 | 0.4890 | 0.4445 |
| 0.5495 | 3400 | 0.2277 | - | - | - | - | - |
| 0.5818 | 3600 | 0.2108 | 0.2248 | 0.5329 | 0.3250 | 0.5304 | 0.4628 |
| 0.6141 | 3800 | 0.2052 | - | - | - | - | - |
| 0.6464 | 4000 | 0.2057 | 0.2181 | 0.5170 | 0.3273 | 0.5117 | 0.4520 |
| 0.6787 | 4200 | 0.2078 | - | - | - | - | - |
| 0.7111 | 4400 | 0.2125 | 0.2165 | 0.5208 | 0.3257 | 0.5022 | 0.4496 |
| 0.7434 | 4600 | 0.2024 | - | - | - | - | - |
| 0.7757 | 4800 | 0.205 | 0.2088 | 0.5026 | 0.3278 | 0.4943 | 0.4416 |
| 0.8080 | 5000 | 0.2013 | - | - | - | - | - |
| 0.8403 | 5200 | 0.198 | 0.2075 | 0.5287 | 0.3234 | 0.4949 | 0.4490 |
| 0.8727 | 5400 | 0.2104 | - | - | - | - | - |
| 0.9050 | 5600 | 0.1965 | 0.2086 | 0.5247 | 0.3216 | 0.5094 | 0.4519 |
| 0.9373 | 5800 | 0.1955 | - | - | - | - | - |
| 0.9696 | 6000 | 0.1994 | 0.2056 | 0.5356 | 0.3272 | 0.5161 | 0.4596 |
| -1 | -1 | - | - | 0.5231 | 0.3297 | 0.5166 | 0.4565 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.102 kWh
- **Carbon Emitted**: 0.039 kg of CO2
- **Hours Used**: 0.265 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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