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splade-cocondenser-msmarco-margin-mse-small

Feature Extraction
sentence-transformers
Safetensors
English
bert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:90000
loss:SpladeLoss
loss:SparseMarginMSELoss
loss:FlopsLoss
Eval Results
text-embeddings-inference
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 ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:90000
- loss:SpladeLoss
- loss:SparseMarginMSELoss
- loss:FlopsLoss
base_model: Luyu/co-condenser-marco
widget:
- text: up to what age can a child get autism
- text: food temperature danger zone
- text: Small and medium size poly tanks are relatively inexpensive. They are also
easy to handle, so poly tanks are used in many smaller wineries. New and used
poly. drums are available in 20, 30, 40 and 55 gallon sizes, and they make excellent
wine storage containers. for home winemakers. Just like glass, wine storage containers
made of polyethylene advantages and disadvantages. They are lightweight, and polyethylene
drums can be handled and stored easily.
- text: what county is louin ms
- text: Map of the Old City of Shanghai. By the early 1400s, Shanghai had become important
enough for Ming dynasty engineers to begin dredging the Huangpu River (also known
as Shen). In 1553, a city wall was built around the Old Town (Nanshi) as a defense
against the depredations of the Wokou (Japanese pirates).
datasets:
- sentence-transformers/msmarco
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: 84.77861327949611
energy_consumed: 0.21810696440845714
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: 0.618
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: CoCondenser 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: 0.46
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.46
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333335
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.46
name: Dot Recall@1
- type: dot_recall@3
value: 0.64
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.82
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6288613269928542
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5688571428571428
name: Dot Mrr@10
- type: dot_map@100
value: 0.5779425698484522
name: Dot Map@100
- type: query_active_dims
value: 56.099998474121094
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981619815715183
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 192.40869140625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9936960654149056
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.38
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38
name: Dot Precision@1
- type: dot_precision@3
value: 0.36
name: Dot Precision@3
- type: dot_precision@5
value: 0.316
name: Dot Precision@5
- type: dot_precision@10
value: 0.26999999999999996
name: Dot Precision@10
- type: dot_recall@1
value: 0.039663209420347775
name: Dot Recall@1
- type: dot_recall@3
value: 0.07520387221675563
name: Dot Recall@3
- type: dot_recall@5
value: 0.09363263999248954
name: Dot Recall@5
- type: dot_recall@10
value: 0.14669853217549625
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3303519560816792
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.49576984126984125
name: Dot Mrr@10
- type: dot_map@100
value: 0.14778057031019226
name: Dot Map@100
- type: query_active_dims
value: 53.68000030517578
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982412685831473
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 367.5431823730469
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9879580898246167
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.5
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.76
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5
name: Dot Precision@1
- type: dot_precision@3
value: 0.25999999999999995
name: Dot Precision@3
- type: dot_precision@5
value: 0.16799999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.09599999999999997
name: Dot Precision@10
- type: dot_recall@1
value: 0.48
name: Dot Recall@1
- type: dot_recall@3
value: 0.71
name: Dot Recall@3
- type: dot_recall@5
value: 0.75
name: Dot Recall@5
- type: dot_recall@10
value: 0.85
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.677150216479017
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6328888888888887
name: Dot Mrr@10
- type: dot_map@100
value: 0.6167275355591967
name: Dot Map@100
- type: query_active_dims
value: 55.939998626708984
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981672236869567
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 228.83615112304688
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9925025833456834
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: 0.4466666666666667
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.66
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7133333333333333
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8133333333333334
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4466666666666667
name: Dot Precision@1
- type: dot_precision@3
value: 0.27777777777777773
name: Dot Precision@3
- type: dot_precision@5
value: 0.20933333333333334
name: Dot Precision@5
- type: dot_precision@10
value: 0.14933333333333332
name: Dot Precision@10
- type: dot_recall@1
value: 0.3265544031401159
name: Dot Recall@1
- type: dot_recall@3
value: 0.47506795740558516
name: Dot Recall@3
- type: dot_recall@5
value: 0.5212108799974965
name: Dot Recall@5
- type: dot_recall@10
value: 0.605566177391832
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5454544998511834
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5658386243386242
name: Dot Mrr@10
- type: dot_map@100
value: 0.44748355857261374
name: Dot Map@100
- type: query_active_dims
value: 55.23999913533529
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981901579472073
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 246.17159613336406
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9919346177795241
name: Corpus Sparsity Ratio
---
# CoCondenser trained on Natural-Questions tuples
This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) 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:** SPLADE Sparse Encoder
- **Base model:** [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) <!-- at revision e0cef0ab2410aae0f0994366ddefb5649a266709 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 30522 dimensions
- **Similarity Function:** Dot Product
- **Training Dataset:**
- [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco)
- **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("tomaarsen/splade-cocondenser-msmarco-margin-mse")
# Run inference
queries = [
"when did shanghai disneyland open",
]
documents = [
"Shanghai Disney officially opens: A peek inside. June 17, 2016, 6 p.m. After five years of construction, $5.5 billion in spending and a month of testing to work out the kinks, Shanghai Disney Resort opened to the public just before noon, Shanghai time, on Thursday, June 16 (which was 9 p.m. Wednesday in Anaheim, home of the original Disney park). Shanghai Disneyland features six themed areas, and the resort contains two hotels, a shopping district and 99 acres of gardens, lakes and parkland. We'll keep you updated throughout the week with new details and peeks inside the resort.",
'Map of the Old City of Shanghai. By the early 1400s, Shanghai had become important enough for Ming dynasty engineers to begin dredging the Huangpu River (also known as Shen). In 1553, a city wall was built around the Old Town (Nanshi) as a defense against the depredations of the Wokou (Japanese pirates).',
'The conflict is referred to in China as the War of Resistance against Japanese Aggression (1937-45) and the Anti-Fascist War. Japanâ\x80\x99s expansionist policy of the 1930s, driven by the military, was to set up what it called the Greater East Asia Co-Prosperity Sphere. Marco Polo Bridge, Beijing.A sphere.e are marking the anniversary of Germany and Japanâ\x80\x99s surrender in 1945, but it is legitimate to suggest that the incident that sparked the conflict that became WWII occurred not in Poland in 1939 but in China, near this eleven-arched bridge on the outskirts of Beijing, in July 1937. Letâ\x80\x99s look at the undisputed facts.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[31.8057, 19.5344, 12.4372]])
```
<!--
### 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.*
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## 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 | 0.46 | 0.38 | 0.5 |
| dot_accuracy@3 | 0.64 | 0.58 | 0.76 |
| dot_accuracy@5 | 0.72 | 0.62 | 0.8 |
| dot_accuracy@10 | 0.82 | 0.74 | 0.88 |
| dot_precision@1 | 0.46 | 0.38 | 0.5 |
| dot_precision@3 | 0.2133 | 0.36 | 0.26 |
| dot_precision@5 | 0.144 | 0.316 | 0.168 |
| dot_precision@10 | 0.082 | 0.27 | 0.096 |
| dot_recall@1 | 0.46 | 0.0397 | 0.48 |
| dot_recall@3 | 0.64 | 0.0752 | 0.71 |
| dot_recall@5 | 0.72 | 0.0936 | 0.75 |
| dot_recall@10 | 0.82 | 0.1467 | 0.85 |
| **dot_ndcg@10** | **0.6289** | **0.3304** | **0.6772** |
| dot_mrr@10 | 0.5689 | 0.4958 | 0.6329 |
| dot_map@100 | 0.5779 | 0.1478 | 0.6167 |
| query_active_dims | 56.1 | 53.68 | 55.94 |
| query_sparsity_ratio | 0.9982 | 0.9982 | 0.9982 |
| corpus_active_dims | 192.4087 | 367.5432 | 228.8362 |
| corpus_sparsity_ratio | 0.9937 | 0.988 | 0.9925 |
#### 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 | 0.4467 |
| dot_accuracy@3 | 0.66 |
| dot_accuracy@5 | 0.7133 |
| dot_accuracy@10 | 0.8133 |
| dot_precision@1 | 0.4467 |
| dot_precision@3 | 0.2778 |
| dot_precision@5 | 0.2093 |
| dot_precision@10 | 0.1493 |
| dot_recall@1 | 0.3266 |
| dot_recall@3 | 0.4751 |
| dot_recall@5 | 0.5212 |
| dot_recall@10 | 0.6056 |
| **dot_ndcg@10** | **0.5455** |
| dot_mrr@10 | 0.5658 |
| dot_map@100 | 0.4475 |
| query_active_dims | 55.24 |
| query_sparsity_ratio | 0.9982 |
| corpus_active_dims | 246.1716 |
| corpus_sparsity_ratio | 0.9919 |
<!--
## 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.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### msmarco
* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
* Size: 90,000 training samples
* Columns: <code>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | score | query | positive | negative |
|:--------|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | float | string | string | string |
| details | <ul><li>min: -2.22</li><li>mean: 13.59</li><li>max: 22.53</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.05 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 81.18 tokens</li><li>max: 203 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 77.08 tokens</li><li>max: 249 tokens</li></ul> |
* Samples:
| score | query | positive | negative |
|:-------------------------------|:-----------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>4.470368494590124</code> | <code>where does the bile duct carry its secretions</code> | <code>The function of the common bile duct is to carry bile from the liver and the gallbladder into the duodenum, the top of the small intestine directly after the stomach. The bile it carries interacts with ingested fats and fat-soluble vitamins to enable them to be absorbed by the intestine.</code> | <code>The gall bladder is a pouch-shaped organ that stores the bile produced by the liver. The gall bladder shares a vessel, called the common bile duct, with the liver. When bile is needed, it moves through the common bile duct into the first part of the small intestine, the duodenum. It is here that the bile breaks down fat.</code> |
| <code>9.550037781397503</code> | <code>definition of reverse auction</code> | <code>Reverse auction. A reverse auction is a type of auction in which the roles of buyer and seller are reversed. In an ordinary auction (also known as a 'forward auction'), buyers compete to obtain goods or services by offering increasingly higher prices. In a reverse auction, the sellers compete to obtain business from the buyer and prices will typically decrease as the sellers underbid each other.</code> | <code>No-reserve auction. A No-reserve auction (NR), also known as an absolute auction, is an auction in which the item for sale will be sold regardless of price. From the seller's perspective, advertising an auction as having no reserve price can be desirable because it potentially attracts a greater number of bidders due to the possibility of a bargain.</code> |
| <code>19.58259622255961</code> | <code>how do i prevent diverticulitis</code> | <code>Follow Following Unfollow Pending Disabled. A , Gastroenterology, answered. The suggestion to prevent diverticulitis is to eat a diet high in fiber, and that includes high-fiber whole grains, fruits, vegetables, nuts, and seeds. I’m aware that some gastroenterologists say to avoid all seeds and nuts, so some of you are nuts enough to wash tomato seeds from slices and pick free poppy seeds from buns.</code> | <code>The test is fast and easy especially with the newer CT scanners. But does it provide the information needed? CT KUBs are used to screen for a variety of intra-abdominal conditions, including appendicitis, kidney stones, diverticulitis, and others.</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMarginMSELoss",
"lambda_corpus": 0.08,
"lambda_query": 0.1
}
```
### Evaluation Dataset
#### msmarco
* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
* Size: 10,000 evaluation samples
* Columns: <code>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | score | query | positive | negative |
|:--------|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | float | string | string | string |
| details | <ul><li>min: -1.34</li><li>mean: 13.49</li><li>max: 22.2</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.85 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 80.48 tokens</li><li>max: 211 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 77.44 tokens</li><li>max: 209 tokens</li></ul> |
* Samples:
| score | query | positive | negative |
|:-------------------------------|:-----------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>15.64028427998225</code> | <code>what is a protected seedbed</code> | <code>A seedbed is a plot of garden set aside to grow vegetables seeds, which can later be transplanted. seedbed is a plot of garden set aside to grow vegetables seeds, which can later be transplanted.</code> | <code>Several articles within the Confederate States’ Constitution specifically protected slavery within the Confederacy, but some articles of the U.S. Constitution also protected slavery—the Emancipation Proclamation drew a clearer distinction between the two.</code> |
| <code>6.375148057937622</code> | <code>who founded ecuador</code> | <code>The first Spanish settlement in Ecuador was established in 1534 at Quito on the site of an important Incan town of the same name. Another settlement was established four years later near the river Guayas in Guayaquil.</code> | <code>Zuleta is a colonial working farm of 4,000 acres (2,000 hectares) that belongs to the family of Mr. Galo Plaza lasso, a former president of Ecuador, for more than 100 years. It was chosen as one of the world’s “Top Ten Finds” by Outside magazine and named as one of the best Ecuador Hotel by National Geographic Traveler.</code> |
| <code>8.436618288358051</code> | <code>what is aol problem</code> | <code>AOL problems. Lots of people are reporting ongoing (RTR:GE) messages from AOL today. This indicates the AOL mail servers are having problems and can’t accept mail. This has nothing to do with spam, filtering or malicious email. This is simply their servers aren’t functioning as well as they should be and so AOL can’t accept all the mail thrown at them. These types of blocks resolve themselves. Update Feb 8, 2016: AOL users are having problems logging in.</code> | <code>Executive Director. I have read these complaints of poor service and agree 110%. I'm a college professor and give extra credit to all AOL users and over the 100% highest grade. I thought I phoned AOL and get some chap in India who is a proven scam man and I'm the poor American SOB who gets whacked.</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMarginMSELoss",
"lambda_corpus": 0.08,
"lambda_query": 0.1
}
```
### 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
#### 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`: {}
- `learning_rate_mapping`: {}
</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 |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|
| 0.0178 | 100 | 805201.68 | - | - | - | - | - |
| 0.0356 | 200 | 11999.3975 | - | - | - | - | - |
| 0.0533 | 300 | 124.0031 | - | - | - | - | - |
| 0.0711 | 400 | 62.6813 | - | - | - | - | - |
| 0.0889 | 500 | 46.0329 | 49.7658 | 0.4890 | 0.2543 | 0.5131 | 0.4188 |
| 0.1067 | 600 | 41.2877 | - | - | - | - | - |
| 0.1244 | 700 | 35.3636 | - | - | - | - | - |
| 0.1422 | 800 | 33.3727 | - | - | - | - | - |
| 0.16 | 900 | 29.389 | - | - | - | - | - |
| 0.1778 | 1000 | 31.2482 | 28.1527 | 0.5652 | 0.2875 | 0.5423 | 0.4650 |
| 0.1956 | 1100 | 31.43 | - | - | - | - | - |
| 0.2133 | 1200 | 27.9919 | - | - | - | - | - |
| 0.2311 | 1300 | 26.9214 | - | - | - | - | - |
| 0.2489 | 1400 | 27.5533 | - | - | - | - | - |
| 0.2667 | 1500 | 25.7473 | 26.8466 | 0.5837 | 0.3265 | 0.6268 | 0.5123 |
| 0.2844 | 1600 | 26.7899 | - | - | - | - | - |
| 0.3022 | 1700 | 24.0652 | - | - | - | - | - |
| 0.32 | 1800 | 23.5837 | - | - | - | - | - |
| 0.3378 | 1900 | 24.1051 | - | - | - | - | - |
| 0.3556 | 2000 | 24.6901 | 22.0851 | 0.6018 | 0.3325 | 0.6359 | 0.5234 |
| 0.3733 | 2100 | 21.5136 | - | - | - | - | - |
| 0.3911 | 2200 | 22.066 | - | - | - | - | - |
| 0.4089 | 2300 | 20.8234 | - | - | - | - | - |
| 0.4267 | 2400 | 20.1988 | - | - | - | - | - |
| 0.4444 | 2500 | 20.0342 | 20.3437 | 0.5901 | 0.3222 | 0.6010 | 0.5044 |
| 0.4622 | 2600 | 18.8835 | - | - | - | - | - |
| 0.48 | 2700 | 19.4797 | - | - | - | - | - |
| 0.4978 | 2800 | 19.6199 | - | - | - | - | - |
| 0.5156 | 2900 | 16.6963 | - | - | - | - | - |
| 0.5333 | 3000 | 19.9204 | 18.0851 | 0.5915 | 0.3111 | 0.6323 | 0.5116 |
| 0.5511 | 3100 | 18.7849 | - | - | - | - | - |
| 0.5689 | 3200 | 18.3169 | - | - | - | - | - |
| 0.5867 | 3300 | 17.1938 | - | - | - | - | - |
| 0.6044 | 3400 | 18.0807 | - | - | - | - | - |
| 0.6222 | 3500 | 16.7721 | 20.1195 | 0.6012 | 0.3119 | 0.6337 | 0.5156 |
| 0.64 | 3600 | 16.7909 | - | - | - | - | - |
| 0.6578 | 3700 | 16.4954 | - | - | - | - | - |
| 0.6756 | 3800 | 16.3734 | - | - | - | - | - |
| 0.6933 | 3900 | 17.2231 | - | - | - | - | - |
| 0.7111 | 4000 | 16.8486 | 17.5785 | 0.6228 | 0.3423 | 0.6553 | 0.5401 |
| 0.7289 | 4100 | 18.2939 | - | - | - | - | - |
| 0.7467 | 4200 | 16.1108 | - | - | - | - | - |
| 0.7644 | 4300 | 16.878 | - | - | - | - | - |
| 0.7822 | 4400 | 15.6163 | - | - | - | - | - |
| 0.8 | 4500 | 15.8337 | 16.1847 | 0.6286 | 0.3376 | 0.6639 | 0.5434 |
| 0.8178 | 4600 | 15.5014 | - | - | - | - | - |
| 0.8356 | 4700 | 15.7579 | - | - | - | - | - |
| 0.8533 | 4800 | 15.9361 | - | - | - | - | - |
| 0.8711 | 4900 | 16.3308 | - | - | - | - | - |
| 0.8889 | 5000 | 14.8395 | 17.4054 | 0.6221 | 0.3280 | 0.6853 | 0.5451 |
| 0.9067 | 5100 | 14.8655 | - | - | - | - | - |
| 0.9244 | 5200 | 14.6498 | - | - | - | - | - |
| 0.9422 | 5300 | 15.5189 | - | - | - | - | - |
| 0.96 | 5400 | 14.608 | - | - | - | - | - |
| 0.9778 | 5500 | 15.6019 | 16.4883 | 0.6298 | 0.3317 | 0.6831 | 0.5482 |
| 0.9956 | 5600 | 14.6263 | - | - | - | - | - |
| -1 | -1 | - | - | 0.6289 | 0.3304 | 0.6772 | 0.5455 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.218 kWh
- **Carbon Emitted**: 0.085 kg of CO2
- **Hours Used**: 0.618 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: 4.52.4
- 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},
}
```
#### SparseMarginMSELoss
```bibtex
@misc{hofstätter2021improving,
title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
year={2021},
eprint={2010.02666},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
#### 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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