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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: weather in ljubljana, slovenia fahrenheit
- text: which type of shark is the largest?
- text: "Plan to have the farrier reset your horseâ\x80\x99s shoes approximately every\
\ six weeks. The shoes should be shaped to the horseâ\x80\x99s feet for a custom\
\ fit."
- text: what oscars was kudo nominated for
- text: "Answers from Ronald Petersen, M.D. Yes, Alzheimer's disease usually worsens\
\ slowly. But its speed of progression varies, depending on a person's genetic\
\ makeup, environmental factors, age at diagnosis and other medical conditions.\
\ Still, anyone diagnosed with Alzheimer's whose symptoms seem to be progressing\
\ quickly â\x80\x94 or who experiences a sudden decline â\x80\x94 should see his\
\ or her doctor."
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: 87.59304620021443
energy_consumed: 0.2253475572552095
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: 0.653
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: CoCondenser trained on MS MARCO
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.66
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.76
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.22
name: Dot Precision@3
- type: dot_precision@5
value: 0.15200000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
name: Dot Recall@1
- type: dot_recall@3
value: 0.66
name: Dot Recall@3
- type: dot_recall@5
value: 0.76
name: Dot Recall@5
- type: dot_recall@10
value: 0.84
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6312406680654746
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5636904761904762
name: Dot Mrr@10
- type: dot_map@100
value: 0.5721212783331427
name: Dot Map@100
- type: query_active_dims
value: 21.100000381469727
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9993086953547778
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 157.69065856933594
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9948335410992288
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: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.3933333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.336
name: Dot Precision@5
- type: dot_precision@10
value: 0.27
name: Dot Precision@10
- type: dot_recall@1
value: 0.04389819910134535
name: Dot Recall@1
- type: dot_recall@3
value: 0.0987021139802183
name: Dot Recall@3
- type: dot_recall@5
value: 0.11414854445866388
name: Dot Recall@5
- type: dot_recall@10
value: 0.14007230906638554
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.34454508141466533
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5322222222222223
name: Dot Mrr@10
- type: dot_map@100
value: 0.1566157643935124
name: Dot Map@100
- type: query_active_dims
value: 17.920000076293945
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.999412882508476
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 311.4259948730469
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9897966714214976
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.48
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.74
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: 0.48
name: Dot Precision@1
- type: dot_precision@3
value: 0.2533333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.16799999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.09399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.46
name: Dot Recall@1
- type: dot_recall@3
value: 0.7
name: Dot Recall@3
- type: dot_recall@5
value: 0.76
name: Dot Recall@5
- type: dot_recall@10
value: 0.84
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6640066557351431
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6205238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.604249902859187
name: Dot Map@100
- type: query_active_dims
value: 25.100000381469727
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.999177642343835
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 194.18609619140625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9936378318527159
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: 0.4466666666666666
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.68
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7333333333333334
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7999999999999999
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4466666666666666
name: Dot Precision@1
- type: dot_precision@3
value: 0.28888888888888886
name: Dot Precision@3
- type: dot_precision@5
value: 0.21866666666666668
name: Dot Precision@5
- type: dot_precision@10
value: 0.14933333333333332
name: Dot Precision@10
- type: dot_recall@1
value: 0.3079660663671151
name: Dot Recall@1
- type: dot_recall@3
value: 0.4862340379934061
name: Dot Recall@3
- type: dot_recall@5
value: 0.5447161814862213
name: Dot Recall@5
- type: dot_recall@10
value: 0.6066907696887952
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5465974684050944
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5721455026455027
name: Dot Mrr@10
- type: dot_map@100
value: 0.44432898186194736
name: Dot Map@100
- type: query_active_dims
value: 21.3733336130778
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9992997400690297
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 206.63049254462427
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9932301129498518
name: Corpus Sparsity Ratio
---
# CoCondenser trained on MS MARCO
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) 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:** Unknown -->
- **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-minilm")
# Run inference
queries = [
"what causes aging fast",
]
documents = [
'UV-A light, specifically, is what mainly causes tanning, skin aging, and cataracts, UV-B causes sunburn, skin aging and skin cancer, and UV-C is the strongest, and therefore most effective at killing microorganisms. Again â\x80\x93 single words and multiple bullets.',
"Answers from Ronald Petersen, M.D. Yes, Alzheimer's disease usually worsens slowly. But its speed of progression varies, depending on a person's genetic makeup, environmental factors, age at diagnosis and other medical conditions. Still, anyone diagnosed with Alzheimer's whose symptoms seem to be progressing quickly â\x80\x94 or who experiences a sudden decline â\x80\x94 should see his or her doctor.",
"Bell's palsy and Extreme tiredness and Extreme fatigue (2 causes) Bell's palsy and Extreme tiredness and Hepatitis (2 causes) Bell's palsy and Extreme tiredness and Liver pain (2 causes) Bell's palsy and Extreme tiredness and Lymph node swelling in children (2 causes)",
]
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([[11.2444, 10.6804, 4.3465]])
```
<!--
### 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 | 0.42 | 0.44 | 0.48 |
| dot_accuracy@3 | 0.66 | 0.64 | 0.74 |
| dot_accuracy@5 | 0.76 | 0.64 | 0.8 |
| dot_accuracy@10 | 0.84 | 0.68 | 0.88 |
| dot_precision@1 | 0.42 | 0.44 | 0.48 |
| dot_precision@3 | 0.22 | 0.3933 | 0.2533 |
| dot_precision@5 | 0.152 | 0.336 | 0.168 |
| dot_precision@10 | 0.084 | 0.27 | 0.094 |
| dot_recall@1 | 0.42 | 0.0439 | 0.46 |
| dot_recall@3 | 0.66 | 0.0987 | 0.7 |
| dot_recall@5 | 0.76 | 0.1141 | 0.76 |
| dot_recall@10 | 0.84 | 0.1401 | 0.84 |
| **dot_ndcg@10** | **0.6312** | **0.3445** | **0.664** |
| dot_mrr@10 | 0.5637 | 0.5322 | 0.6205 |
| dot_map@100 | 0.5721 | 0.1566 | 0.6042 |
| query_active_dims | 21.1 | 17.92 | 25.1 |
| query_sparsity_ratio | 0.9993 | 0.9994 | 0.9992 |
| corpus_active_dims | 157.6907 | 311.426 | 194.1861 |
| corpus_sparsity_ratio | 0.9948 | 0.9898 | 0.9936 |
#### 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 | 0.68 |
| dot_accuracy@5 | 0.7333 |
| dot_accuracy@10 | 0.8 |
| dot_precision@1 | 0.4467 |
| dot_precision@3 | 0.2889 |
| dot_precision@5 | 0.2187 |
| dot_precision@10 | 0.1493 |
| dot_recall@1 | 0.308 |
| dot_recall@3 | 0.4862 |
| dot_recall@5 | 0.5447 |
| dot_recall@10 | 0.6067 |
| **dot_ndcg@10** | **0.5466** |
| dot_mrr@10 | 0.5721 |
| dot_map@100 | 0.4443 |
| query_active_dims | 21.3733 |
| query_sparsity_ratio | 0.9993 |
| corpus_active_dims | 206.6305 |
| corpus_sparsity_ratio | 0.9932 |
<!--
## 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
#### Unnamed Dataset
* Size: 90,000 training samples
* Columns: <code>query</code>, <code>positive</code>, <code>negative</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative | score |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------|
| type | string | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.22 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 79.27 tokens</li><li>max: 247 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 81.15 tokens</li><li>max: 201 tokens</li></ul> | <ul><li>min: -14.32</li><li>mean: 4.62</li><li>max: 21.72</li></ul> |
* Samples:
| query | positive | negative | score |
|:---------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>most powerful army in the world</code> | <code>U.S. Army Reserve Command You may be asking yourself, âWhat is the Army Reserve?â The Army is the most powerful and sophisticated military force in the world.</code> | <code>The British Royal Navy was the most powerful sea-going force by the time of World War 1 (1914-1918) and this was well-underst...</code> | <code>2.919867515563965</code> |
| <code>define vasomotor</code> | <code>Define peripheral neuropathy: a disease or degenerative state of the peripheral nerves in which motor, sensory, or vasomotor nerve fibers may be⦠a disease or degenerative state of the peripheral nerves in which motor, sensory, or vasomotor nerve fibers may be affected and which is markedâ¦</code> | <code>VairÄgya (Devanagari: वà¥à¤°à¤¾à¤à¥à¤¯, also spelt Vairagya) is a Sanskrit term used in Hindu philosophy that roughly translates as dispassion, detachment, or renunciation, in particular renunciation from the pains and pleasures in the material world (Maya).</code> | <code>3.0037026405334473</code> |
| <code>nitrates definition biology</code> | <code>In Botany or Plant Biology. By Photosynthesis, the palisade cells make glucose which has many uses including: storage as starch, to make fat, to make cellulose and to make protein. Glucose is converted wâ¦ith mineral slat nitrates to make the protein. Nitrates provide the essential nitrogen to make protein. The Ribosome, an organelle of the plant cell, manufactures most of the cell's protein.</code> | <code>Almost all inorganic nitrate salts are soluble in water at standard temperature and pressure. A common example of an inorganic nitrate salt is potassium nitrate (saltpeter). A rich source of inorganic nitrate in the human body comes from diets rich in leafy green foods, such as spinach and arugula.It is now believed that dietary nitrate in the form of plant-based foods is converted in the body to nitrite.itrate is a polyatomic ion with the molecular formula NO 3 â and a molecular mass of 62.0049 g/mol.</code> | <code>-1.6804794073104858</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
#### Unnamed Dataset
* Size: 10,000 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, <code>negative</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative | score |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.01 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 79.8 tokens</li><li>max: 336 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 81.3 tokens</li><li>max: 273 tokens</li></ul> | <ul><li>min: -15.9</li><li>mean: 4.91</li><li>max: 21.67</li></ul> |
* Samples:
| query | positive | negative | score |
|:----------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>femoral artery definition</code> | <code>medical Definition of circumflex artery : any of several paired curving arteries: as a: either of two arteries that branch from the deep femoral artery or from the femoral artery itself:</code> | <code>Femoral vein. The femoral vein is located in the upper thigh and pelvic region of the human body. It travels in close proximity to the femoral artery. This vein is one of the larger vessels in the venous system. Instead of draining deoxygenated blood from specific parts of the body, it receives blood from several significant branches. These include popliteal, the profunda femoris, and the great sapheneous veins.</code> | <code>-0.1968388557434082</code> |
| <code>what causes mastitis and how do you treat it</code> | <code>Mastitis is an infection of the tissue of the breast that occurs most frequently during the time of breastfeeding. This infection causes pain, swelling, redness, and increased temperature of the breast. It can occur when bacteria, often from the infant's mouth, enter a milk duct through a crack in the nipple. This causes an infection and painful inflammation of the breast.</code> | <code>Common causes of mastitis include bacteria from the babyâs mouth, bacteria entering via breast injuries (bruising, fissures, cracks in the nipple), milk stasis (milk pooling in the breast), and bacteria from the hands of the mother or health care provider.</code> | <code>-0.8143405914306641</code> |
| <code>what is a buck moth</code> | <code>Buck moth caterpillars that have a light background color can be confused with both the Nevada buck moth, Hemileuca nevadensis Stretch, and the New England buck moth, Hemileuca lucina Henry Edwards. The larvae of these three species can best be distinguished based on the preferred host plants (Wagner 2005).hey rely on resources that are acquired by the caterpillars (larvae). The caterpillars are robust and can exceed four inches (10 cm) in North America. Figure 4. Adult cecropia moth, Hyalophora cecropia (Linnaeus). Photograph by Pennsylvania Department of Conservation and Natural Resources-Forestry Archive, Bugwood.org.</code> | <code>bucktail that gets talked about quietly in the . privacy of remote cabins. The âMusky-Teerâ is a big fish bait that anglers treasure in their collection. You wonât find these at your local bait shop but weâve been stocking these highly prized baits in all colors for years.</code> | <code>11.004357814788818</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 | 501776.8 | - | - | - | - | - |
| 0.0356 | 200 | 9740.8356 | - | - | - | - | - |
| 0.0533 | 300 | 61.9771 | - | - | - | - | - |
| 0.0711 | 400 | 37.6145 | - | - | - | - | - |
| 0.0889 | 500 | 28.8887 | 24.4953 | 0.4878 | 0.3047 | 0.5425 | 0.4450 |
| 0.1067 | 600 | 24.7991 | - | - | - | - | - |
| 0.1244 | 700 | 22.1517 | - | - | - | - | - |
| 0.1422 | 800 | 22.0889 | - | - | - | - | - |
| 0.16 | 900 | 20.7825 | - | - | - | - | - |
| 0.1778 | 1000 | 20.0856 | 18.6383 | 0.5751 | 0.3303 | 0.6100 | 0.5051 |
| 0.1956 | 1100 | 18.6968 | - | - | - | - | - |
| 0.2133 | 1200 | 20.5069 | - | - | - | - | - |
| 0.2311 | 1300 | 19.8162 | - | - | - | - | - |
| 0.2489 | 1400 | 19.1892 | - | - | - | - | - |
| 0.2667 | 1500 | 17.5024 | 18.0698 | 0.5750 | 0.3281 | 0.6222 | 0.5084 |
| 0.2844 | 1600 | 17.7801 | - | - | - | - | - |
| 0.3022 | 1700 | 17.9045 | - | - | - | - | - |
| 0.32 | 1800 | 16.3731 | - | - | - | - | - |
| 0.3378 | 1900 | 16.293 | - | - | - | - | - |
| 0.3556 | 2000 | 16.1167 | 14.5428 | 0.5696 | 0.3422 | 0.6232 | 0.5116 |
| 0.3733 | 2100 | 16.561 | - | - | - | - | - |
| 0.3911 | 2200 | 16.5533 | - | - | - | - | - |
| 0.4089 | 2300 | 14.9371 | - | - | - | - | - |
| 0.4267 | 2400 | 15.565 | - | - | - | - | - |
| 0.4444 | 2500 | 14.2143 | 15.2027 | 0.6071 | 0.3376 | 0.6600 | 0.5349 |
| 0.4622 | 2600 | 13.7188 | - | - | - | - | - |
| 0.48 | 2700 | 14.8554 | - | - | - | - | - |
| 0.4978 | 2800 | 15.1021 | - | - | - | - | - |
| 0.5156 | 2900 | 13.3032 | - | - | - | - | - |
| 0.5333 | 3000 | 13.8999 | 12.9609 | 0.5874 | 0.3423 | 0.6562 | 0.5286 |
| 0.5511 | 3100 | 12.7418 | - | - | - | - | - |
| 0.5689 | 3200 | 12.9422 | - | - | - | - | - |
| 0.5867 | 3300 | 13.6937 | - | - | - | - | - |
| 0.6044 | 3400 | 13.1183 | - | - | - | - | - |
| 0.6222 | 3500 | 12.7998 | 12.2024 | 0.6262 | 0.3424 | 0.6771 | 0.5486 |
| 0.64 | 3600 | 12.7799 | - | - | - | - | - |
| 0.6578 | 3700 | 12.2294 | - | - | - | - | - |
| 0.6756 | 3800 | 13.6836 | - | - | - | - | - |
| 0.6933 | 3900 | 13.579 | - | - | - | - | - |
| 0.7111 | 4000 | 12.6337 | 13.9878 | 0.6156 | 0.3435 | 0.6526 | 0.5372 |
| 0.7289 | 4100 | 12.682 | - | - | - | - | - |
| 0.7467 | 4200 | 12.2157 | - | - | - | - | - |
| 0.7644 | 4300 | 12.3127 | - | - | - | - | - |
| 0.7822 | 4400 | 11.7435 | - | - | - | - | - |
| 0.8 | 4500 | 12.086 | 12.3685 | 0.6262 | 0.3386 | 0.6782 | 0.5477 |
| 0.8178 | 4600 | 12.5455 | - | - | - | - | - |
| 0.8356 | 4700 | 11.7477 | - | - | - | - | - |
| 0.8533 | 4800 | 11.9948 | - | - | - | - | - |
| 0.8711 | 4900 | 11.8997 | - | - | - | - | - |
| 0.8889 | 5000 | 12.1624 | 12.8277 | 0.6241 | 0.3515 | 0.6740 | 0.5499 |
| 0.9067 | 5100 | 11.4352 | - | - | - | - | - |
| 0.9244 | 5200 | 10.9171 | - | - | - | - | - |
| 0.9422 | 5300 | 11.3242 | - | - | - | - | - |
| 0.96 | 5400 | 11.437 | - | - | - | - | - |
| 0.9778 | 5500 | 11.3141 | 11.6410 | 0.6366 | 0.3441 | 0.6605 | 0.5471 |
| 0.9956 | 5600 | 11.8683 | - | - | - | - | - |
| -1 | -1 | - | - | 0.6312 | 0.3445 | 0.6640 | 0.5466 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.225 kWh
- **Carbon Emitted**: 0.088 kg of CO2
- **Hours Used**: 0.653 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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