---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- csr
- generated_from_trainer
- dataset_size:3011496
- loss:CSRLoss
- loss:SparseMultipleNegativesRankingLoss
base_model: mixedbread-ai/mxbai-embed-large-v1
widget:
- source_sentence: how much is a car title transfer in minnesota?
sentences:
- This complex is a larger molecule than the original crystal violet stain and iodine
and is insoluble in water. ... Conversely, the the outer membrane of Gram negative
bacteria is degraded and the thinner peptidoglycan layer of Gram negative cells
is unable to retain the crystal violet-iodine complex and the color is lost.
- Get insurance on the car and provide proof. Bring this information (including
the title) to the Minnesota DVS office, as well as $10 for the filing fee and
$7.25 for the titling fee. There is also a $10 transfer tax, as well as a 6.5%
sales tax on the purchase price.
- 'One of the risks of DNP is that it accelerates the metabolism to a dangerously
fast level. Our metabolic system operates at the rate it does for a reason – it
is safe. Speeding up the metabolism may help burn off fat, but it can also trigger
a number of potentially dangerous side effects, such as: fever.'
- source_sentence: what is the difference between 18 and 20 inch tires?
sentences:
- The only real difference is a 20" rim would be more likely to be damaged, as you
pointed out. Beyond looks, there is zero benefit for the 20" rim. Also, just the
availability of tires will likely be much more limited for the larger rim. ...
Tire selection is better for 18" wheels than 20" wheels.
- '[''Open your Outlook app on your mobile device and click on the Settings gear
icon.'', ''Under Settings, click on the Signature option.'', ''Enter either a
generic signature that could be used for all email accounts tied to your Outlook
app, or a specific signature, Per Account Signature, for each email account.'']'
- The average normal body temperature is around 98.6 degrees Fahrenheit, or 37 degrees
Celsius. If your body temperature drops to just a few degrees lower than this,
your blood vessels in your hands, feet, arms, and legs start to get narrower.
- source_sentence: whom the bell tolls meaning?
sentences:
- 'Answer: Humans are depicted in Hindu art often in sensuous and erotic postures.'
- The phrase "For whom the bell tolls" refers to the church bells that are rung
when a person dies. Hence, the author is suggesting that we should not be curious
as to for whom the church bell is tolling for. It is for all of us.
- '[''Automatically.'', ''When connected to car Bluetooth and,'', ''Manually.'']'
- source_sentence: how long before chlamydia symptoms appear?
sentences:
- Most people who have chlamydia don't notice any symptoms. If you do get symptoms,
these usually appear between 1 and 3 weeks after having unprotected sex with an
infected person. For some people they don't develop until many months later. Sometimes
the symptoms can disappear after a few days.
- '[''Open the My Verizon app . ... '', ''Tap the Menu icon. ... '', ''Tap Manage
device for the appropriate mobile number. ... '', ''Tap Transfer content between
phones. ... '', ''Tap Start Transfer.'']'
- 'Psychiatrist vs Psychologist A psychiatrist is classed as a medical doctor, they
include a physical examination of symptoms in their assessment and are able to
prescribe medicine: a psychologist is also a doctor by virtue of their PHD level
qualification, but is not medically trained and cannot prescribe.'
- source_sentence: are you human korean novela?
sentences:
- Many cysts heal on their own, which means that conservative treatments like rest
and anti-inflammatory painkillers can often be enough to get rid of them. However,
in some cases, routine drainage of the sac may be necessary to reduce symptoms.
- A relative of European pear varieties like Bartlett and Anjou, the Asian pear
is great used in recipes or simply eaten out of hand. It retains a crispness that
works well in slaws and salads, and it holds its shape better than European pears
when baked and cooked.
- 'Are You Human? (Korean: 너도 인간이니; RR: Neodo Inganini; lit. Are You Human Too?)
is a 2018 South Korean television series starring Seo Kang-jun and Gong Seung-yeon.
It aired on KBS2''s Mondays and Tuesdays at 22:00 (KST) time slot, from June 4
to August 7, 2018.'
datasets:
- sentence-transformers/gooaq
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
- row_non_zero_mean_query
- row_sparsity_mean_query
- row_non_zero_mean_corpus
- row_sparsity_mean_corpus
co2_eq_emissions:
emissions: 467.36155743833086
energy_consumed: 1.2023646840981803
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: 3.125
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: Sparse CSR model trained on Natural Questions
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 128
type: NanoMSMARCO_128
metrics:
- type: dot_accuracy@1
value: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.68
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.136
name: Dot Precision@5
- type: dot_precision@10
value: 0.08
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
name: Dot Recall@1
- type: dot_recall@3
value: 0.64
name: Dot Recall@3
- type: dot_recall@5
value: 0.68
name: Dot Recall@5
- type: dot_recall@10
value: 0.8
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6079185617079585
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5469047619047619
name: Dot Mrr@10
- type: dot_map@100
value: 0.5546949863343481
name: Dot Map@100
- type: row_non_zero_mean_query
value: 128.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.96875
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 128.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.96875
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus 128
type: NanoNFCorpus_128
metrics:
- type: dot_accuracy@1
value: 0.28
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.46
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.58
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.66
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.28
name: Dot Precision@1
- type: dot_precision@3
value: 0.2866666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.28
name: Dot Precision@5
- type: dot_precision@10
value: 0.24600000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.010077778443246685
name: Dot Recall@1
- type: dot_recall@3
value: 0.04965300165842144
name: Dot Recall@3
- type: dot_recall@5
value: 0.07680443441830657
name: Dot Recall@5
- type: dot_recall@10
value: 0.10785346110615711
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.27112973349418856
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3951904761904761
name: Dot Mrr@10
- type: dot_map@100
value: 0.10882673834779542
name: Dot Map@100
- type: row_non_zero_mean_query
value: 128.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.96875
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 128.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.96875
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ 128
type: NanoNQ_128
metrics:
- type: dot_accuracy@1
value: 0.46
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.62
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7
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: 0.20666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.14
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.44
name: Dot Recall@1
- type: dot_recall@3
value: 0.58
name: Dot Recall@3
- type: dot_recall@5
value: 0.65
name: Dot Recall@5
- type: dot_recall@10
value: 0.76
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5976862103963738
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5692222222222223
name: Dot Mrr@10
- type: dot_map@100
value: 0.5513454286143362
name: Dot Map@100
- type: row_non_zero_mean_query
value: 128.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.96875
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 128.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.96875
name: Row Sparsity Mean Corpus
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 128
type: NanoBEIR_mean_128
metrics:
- type: dot_accuracy@1
value: 0.38666666666666666
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5733333333333334
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6533333333333333
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7599999999999999
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38666666666666666
name: Dot Precision@1
- type: dot_precision@3
value: 0.23555555555555555
name: Dot Precision@3
- type: dot_precision@5
value: 0.18533333333333335
name: Dot Precision@5
- type: dot_precision@10
value: 0.136
name: Dot Precision@10
- type: dot_recall@1
value: 0.2900259261477489
name: Dot Recall@1
- type: dot_recall@3
value: 0.4232176672194738
name: Dot Recall@3
- type: dot_recall@5
value: 0.4689348114727689
name: Dot Recall@5
- type: dot_recall@10
value: 0.5559511537020524
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.49224483519950696
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5037724867724868
name: Dot Mrr@10
- type: dot_map@100
value: 0.4049557177654933
name: Dot Map@100
- type: row_non_zero_mean_query
value: 128.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.96875
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 128.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.96875
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 256
type: NanoMSMARCO_256
metrics:
- type: dot_accuracy@1
value: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7
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: 0.2333333333333333
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: 0.7
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: 0.6326016391887893
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.566111111111111
name: Dot Mrr@10
- type: dot_map@100
value: 0.5727341193854673
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus 256
type: NanoNFCorpus_256
metrics:
- type: dot_accuracy@1
value: 0.32
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.32
name: Dot Precision@1
- type: dot_precision@3
value: 0.31999999999999995
name: Dot Precision@3
- type: dot_precision@5
value: 0.316
name: Dot Precision@5
- type: dot_precision@10
value: 0.262
name: Dot Precision@10
- type: dot_recall@1
value: 0.030392237560226815
name: Dot Recall@1
- type: dot_recall@3
value: 0.0717373009745601
name: Dot Recall@3
- type: dot_recall@5
value: 0.09312218308574575
name: Dot Recall@5
- type: dot_recall@10
value: 0.133341363492939
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.30709320262394824
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.45252380952380944
name: Dot Mrr@10
- type: dot_map@100
value: 0.14302697817666413
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ 256
type: NanoNQ_256
metrics:
- type: dot_accuracy@1
value: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.68
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: 0.14
name: Dot Precision@5
- type: dot_precision@10
value: 0.088
name: Dot Precision@10
- type: dot_recall@1
value: 0.4
name: Dot Recall@1
- type: dot_recall@3
value: 0.6
name: Dot Recall@3
- type: dot_recall@5
value: 0.63
name: Dot Recall@5
- type: dot_recall@10
value: 0.79
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.594269599796927
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5505952380952379
name: Dot Mrr@10
- type: dot_map@100
value: 0.5330295920949546
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 256
type: NanoBEIR_mean_256
metrics:
- type: dot_accuracy@1
value: 0.38666666666666666
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6333333333333333
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6866666666666666
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7933333333333333
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38666666666666666
name: Dot Precision@1
- type: dot_precision@3
value: 0.2577777777777777
name: Dot Precision@3
- type: dot_precision@5
value: 0.2026666666666667
name: Dot Precision@5
- type: dot_precision@10
value: 0.14466666666666664
name: Dot Precision@10
- type: dot_recall@1
value: 0.28346407918674227
name: Dot Recall@1
- type: dot_recall@3
value: 0.45724576699152
name: Dot Recall@3
- type: dot_recall@5
value: 0.4943740610285819
name: Dot Recall@5
- type: dot_recall@10
value: 0.5877804544976463
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5113214805365548
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5230767195767194
name: Dot Mrr@10
- type: dot_map@100
value: 0.41626356321902863
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
---
# Sparse CSR model trained on Natural Questions
This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** CSR Sparse Encoder
- **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 4096 dimensions
- **Similarity Function:** Dot Product
- **Training Dataset:**
- [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- **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): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30})
)
```
## 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/csr-mxbai-embed-large-v1-gooaq-2e-4")
# Run inference
sentences = [
'are you human korean novela?',
"Are You Human? (Korean: 너도 인간이니; RR: Neodo Inganini; lit. Are You Human Too?) is a 2018 South Korean television series starring Seo Kang-jun and Gong Seung-yeon. It aired on KBS2's Mondays and Tuesdays at 22:00 (KST) time slot, from June 4 to August 7, 2018.",
'A relative of European pear varieties like Bartlett and Anjou, the Asian pear is great used in recipes or simply eaten out of hand. It retains a crispness that works well in slaws and salads, and it holds its shape better than European pears when baked and cooked.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# (3, 4096)
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO_128`, `NanoNFCorpus_128` and `NanoNQ_128`
* Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 128
}
```
| Metric | NanoMSMARCO_128 | NanoNFCorpus_128 | NanoNQ_128 |
|:-------------------------|:----------------|:-----------------|:-----------|
| dot_accuracy@1 | 0.42 | 0.28 | 0.46 |
| dot_accuracy@3 | 0.64 | 0.46 | 0.62 |
| dot_accuracy@5 | 0.68 | 0.58 | 0.7 |
| dot_accuracy@10 | 0.8 | 0.66 | 0.82 |
| dot_precision@1 | 0.42 | 0.28 | 0.46 |
| dot_precision@3 | 0.2133 | 0.2867 | 0.2067 |
| dot_precision@5 | 0.136 | 0.28 | 0.14 |
| dot_precision@10 | 0.08 | 0.246 | 0.082 |
| dot_recall@1 | 0.42 | 0.0101 | 0.44 |
| dot_recall@3 | 0.64 | 0.0497 | 0.58 |
| dot_recall@5 | 0.68 | 0.0768 | 0.65 |
| dot_recall@10 | 0.8 | 0.1079 | 0.76 |
| **dot_ndcg@10** | **0.6079** | **0.2711** | **0.5977** |
| dot_mrr@10 | 0.5469 | 0.3952 | 0.5692 |
| dot_map@100 | 0.5547 | 0.1088 | 0.5513 |
| row_non_zero_mean_query | 128.0 | 128.0 | 128.0 |
| row_sparsity_mean_query | 0.9688 | 0.9688 | 0.9688 |
| row_non_zero_mean_corpus | 128.0 | 128.0 | 128.0 |
| row_sparsity_mean_corpus | 0.9688 | 0.9688 | 0.9688 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_128`
* Evaluated with [SparseNanoBEIREvaluator](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"
],
"max_active_dims": 128
}
```
| Metric | Value |
|:-------------------------|:-----------|
| dot_accuracy@1 | 0.3867 |
| dot_accuracy@3 | 0.5733 |
| dot_accuracy@5 | 0.6533 |
| dot_accuracy@10 | 0.76 |
| dot_precision@1 | 0.3867 |
| dot_precision@3 | 0.2356 |
| dot_precision@5 | 0.1853 |
| dot_precision@10 | 0.136 |
| dot_recall@1 | 0.29 |
| dot_recall@3 | 0.4232 |
| dot_recall@5 | 0.4689 |
| dot_recall@10 | 0.556 |
| **dot_ndcg@10** | **0.4922** |
| dot_mrr@10 | 0.5038 |
| dot_map@100 | 0.405 |
| row_non_zero_mean_query | 128.0 |
| row_sparsity_mean_query | 0.9688 |
| row_non_zero_mean_corpus | 128.0 |
| row_sparsity_mean_corpus | 0.9688 |
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO_256`, `NanoNFCorpus_256` and `NanoNQ_256`
* Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 256
}
```
| Metric | NanoMSMARCO_256 | NanoNFCorpus_256 | NanoNQ_256 |
|:-------------------------|:----------------|:-----------------|:-----------|
| dot_accuracy@1 | 0.42 | 0.32 | 0.42 |
| dot_accuracy@3 | 0.7 | 0.56 | 0.64 |
| dot_accuracy@5 | 0.76 | 0.62 | 0.68 |
| dot_accuracy@10 | 0.84 | 0.7 | 0.84 |
| dot_precision@1 | 0.42 | 0.32 | 0.42 |
| dot_precision@3 | 0.2333 | 0.32 | 0.22 |
| dot_precision@5 | 0.152 | 0.316 | 0.14 |
| dot_precision@10 | 0.084 | 0.262 | 0.088 |
| dot_recall@1 | 0.42 | 0.0304 | 0.4 |
| dot_recall@3 | 0.7 | 0.0717 | 0.6 |
| dot_recall@5 | 0.76 | 0.0931 | 0.63 |
| dot_recall@10 | 0.84 | 0.1333 | 0.79 |
| **dot_ndcg@10** | **0.6326** | **0.3071** | **0.5943** |
| dot_mrr@10 | 0.5661 | 0.4525 | 0.5506 |
| dot_map@100 | 0.5727 | 0.143 | 0.533 |
| row_non_zero_mean_query | 256.0 | 256.0 | 256.0 |
| row_sparsity_mean_query | 0.9375 | 0.9375 | 0.9375 |
| row_non_zero_mean_corpus | 256.0 | 256.0 | 256.0 |
| row_sparsity_mean_corpus | 0.9375 | 0.9375 | 0.9375 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_256`
* Evaluated with [SparseNanoBEIREvaluator](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"
],
"max_active_dims": 256
}
```
| Metric | Value |
|:-------------------------|:-----------|
| dot_accuracy@1 | 0.3867 |
| dot_accuracy@3 | 0.6333 |
| dot_accuracy@5 | 0.6867 |
| dot_accuracy@10 | 0.7933 |
| dot_precision@1 | 0.3867 |
| dot_precision@3 | 0.2578 |
| dot_precision@5 | 0.2027 |
| dot_precision@10 | 0.1447 |
| dot_recall@1 | 0.2835 |
| dot_recall@3 | 0.4572 |
| dot_recall@5 | 0.4944 |
| dot_recall@10 | 0.5878 |
| **dot_ndcg@10** | **0.5113** |
| dot_mrr@10 | 0.5231 |
| dot_map@100 | 0.4163 |
| row_non_zero_mean_query | 256.0 |
| row_sparsity_mean_query | 0.9375 |
| row_non_zero_mean_corpus | 256.0 |
| row_sparsity_mean_corpus | 0.9375 |
## Training Details
### Training Dataset
#### gooaq
* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,011,496 training samples
* Columns: question and answer
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 8 tokens
- mean: 11.87 tokens
- max: 23 tokens
| - min: 14 tokens
- mean: 60.09 tokens
- max: 201 tokens
|
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| what is the difference between clay and mud mask? | The main difference between the two is that mud is a skin-healing agent, while clay is a cosmetic, drying agent. Clay masks are most useful for someone who has oily skin and is prone to breakouts of acne and blemishes. |
| myki how much on card? | A full fare myki card costs $6 and a concession, seniors or child myki costs $3. For more information about how to use your myki, visit ptv.vic.gov.au or call 1800 800 007. |
| how to find out if someone blocked your phone number on iphone? | If you get a notification like "Message Not Delivered" or you get no notification at all, that's a sign of a potential block. Next, you could try calling the person. If the call goes right to voicemail or rings once (or a half ring) then goes to voicemail, that's further evidence you may have been blocked. |
* Loss: [CSRLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')"
}
```
### Evaluation Dataset
#### gooaq
* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 1,000 evaluation samples
* Columns: question and answer
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 8 tokens
- mean: 11.88 tokens
- max: 22 tokens
| - min: 14 tokens
- mean: 61.03 tokens
- max: 127 tokens
|
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| how do i program my directv remote with my tv? | ['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.'] |
| are rodrigues fruit bats nocturnal? | Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night. |
| why does your heart rate increase during exercise bbc bitesize? | During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it. |
* Loss: [CSRLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 0.0002
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0002
- `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`: True
- `fp16`: False
- `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`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_128_dot_ndcg@10 | NanoNFCorpus_128_dot_ndcg@10 | NanoNQ_128_dot_ndcg@10 | NanoBEIR_mean_128_dot_ndcg@10 | NanoMSMARCO_256_dot_ndcg@10 | NanoNFCorpus_256_dot_ndcg@10 | NanoNQ_256_dot_ndcg@10 | NanoBEIR_mean_256_dot_ndcg@10 |
|:----------:|:---------:|:-------------:|:---------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|
| -1 | -1 | - | - | 0.6175 | 0.2875 | 0.5432 | 0.4827 | 0.6158 | 0.3234 | 0.5929 | 0.5107 |
| 0.0064 | 300 | 0.3621 | - | - | - | - | - | - | - | - | - |
| 0.0128 | 600 | 0.3319 | - | - | - | - | - | - | - | - | - |
| 0.0191 | 900 | 0.3212 | - | - | - | - | - | - | - | - | - |
| 0.0255 | 1200 | 0.3154 | - | - | - | - | - | - | - | - | - |
| 0.0319 | 1500 | 0.3129 | - | - | - | - | - | - | - | - | - |
| 0.0383 | 1800 | 0.309 | - | - | - | - | - | - | - | - | - |
| 0.0446 | 2100 | 0.317 | - | - | - | - | - | - | - | - | - |
| 0.0510 | 2400 | 0.2997 | - | - | - | - | - | - | - | - | - |
| 0.0574 | 2700 | 0.3409 | - | - | - | - | - | - | - | - | - |
| 0.0638 | 3000 | 0.3251 | 0.3136 | 0.6049 | 0.2393 | 0.5583 | 0.4675 | 0.5950 | 0.2559 | 0.5555 | 0.4688 |
| 0.0701 | 3300 | 0.3291 | - | - | - | - | - | - | - | - | - |
| 0.0765 | 3600 | 0.3366 | - | - | - | - | - | - | - | - | - |
| 0.0829 | 3900 | 0.3286 | - | - | - | - | - | - | - | - | - |
| 0.0893 | 4200 | 0.3264 | - | - | - | - | - | - | - | - | - |
| 0.0956 | 4500 | 0.3413 | - | - | - | - | - | - | - | - | - |
| 0.1020 | 4800 | 0.3352 | - | - | - | - | - | - | - | - | - |
| 0.1084 | 5100 | 0.3323 | - | - | - | - | - | - | - | - | - |
| 0.1148 | 5400 | 0.3308 | - | - | - | - | - | - | - | - | - |
| 0.1211 | 5700 | 0.3127 | - | - | - | - | - | - | - | - | - |
| 0.1275 | 6000 | 0.3224 | 0.2949 | 0.5445 | 0.2155 | 0.5394 | 0.4331 | 0.5911 | 0.2340 | 0.5365 | 0.4539 |
| 0.1339 | 6300 | 0.3216 | - | - | - | - | - | - | - | - | - |
| 0.1403 | 6600 | 0.3202 | - | - | - | - | - | - | - | - | - |
| 0.1466 | 6900 | 0.3296 | - | - | - | - | - | - | - | - | - |
| 0.1530 | 7200 | 0.3171 | - | - | - | - | - | - | - | - | - |
| 0.1594 | 7500 | 0.3141 | - | - | - | - | - | - | - | - | - |
| 0.1658 | 7800 | 0.3202 | - | - | - | - | - | - | - | - | - |
| 0.1721 | 8100 | 0.3088 | - | - | - | - | - | - | - | - | - |
| 0.1785 | 8400 | 0.304 | - | - | - | - | - | - | - | - | - |
| 0.1849 | 8700 | 0.3105 | - | - | - | - | - | - | - | - | - |
| 0.1913 | 9000 | 0.307 | 0.2849 | 0.6038 | 0.2258 | 0.5471 | 0.4589 | 0.6241 | 0.2449 | 0.5498 | 0.4730 |
| 0.1976 | 9300 | 0.3043 | - | - | - | - | - | - | - | - | - |
| 0.2040 | 9600 | 0.3035 | - | - | - | - | - | - | - | - | - |
| 0.2104 | 9900 | 0.3069 | - | - | - | - | - | - | - | - | - |
| 0.2168 | 10200 | 0.3174 | - | - | - | - | - | - | - | - | - |
| 0.2231 | 10500 | 0.3111 | - | - | - | - | - | - | - | - | - |
| 0.2295 | 10800 | 0.295 | - | - | - | - | - | - | - | - | - |
| 0.2359 | 11100 | 0.2892 | - | - | - | - | - | - | - | - | - |
| 0.2423 | 11400 | 0.3012 | - | - | - | - | - | - | - | - | - |
| 0.2486 | 11700 | 0.3061 | - | - | - | - | - | - | - | - | - |
| 0.2550 | 12000 | 0.2863 | 0.2631 | 0.6190 | 0.2720 | 0.5379 | 0.4763 | 0.6056 | 0.2898 | 0.5419 | 0.4791 |
| 0.2614 | 12300 | 0.3008 | - | - | - | - | - | - | - | - | - |
| 0.2678 | 12600 | 0.2849 | - | - | - | - | - | - | - | - | - |
| 0.2741 | 12900 | 0.2876 | - | - | - | - | - | - | - | - | - |
| 0.2805 | 13200 | 0.2963 | - | - | - | - | - | - | - | - | - |
| 0.2869 | 13500 | 0.2926 | - | - | - | - | - | - | - | - | - |
| 0.2933 | 13800 | 0.2855 | - | - | - | - | - | - | - | - | - |
| 0.2996 | 14100 | 0.2868 | - | - | - | - | - | - | - | - | - |
| 0.3060 | 14400 | 0.294 | - | - | - | - | - | - | - | - | - |
| 0.3124 | 14700 | 0.3008 | - | - | - | - | - | - | - | - | - |
| 0.3188 | 15000 | 0.293 | 0.2745 | 0.5538 | 0.2847 | 0.5422 | 0.4602 | 0.5615 | 0.2976 | 0.5588 | 0.4726 |
| 0.3252 | 15300 | 0.2776 | - | - | - | - | - | - | - | - | - |
| 0.3315 | 15600 | 0.2906 | - | - | - | - | - | - | - | - | - |
| 0.3379 | 15900 | 0.2874 | - | - | - | - | - | - | - | - | - |
| 0.3443 | 16200 | 0.2834 | - | - | - | - | - | - | - | - | - |
| 0.3507 | 16500 | 0.2718 | - | - | - | - | - | - | - | - | - |
| 0.3570 | 16800 | 0.2834 | - | - | - | - | - | - | - | - | - |
| 0.3634 | 17100 | 0.2833 | - | - | - | - | - | - | - | - | - |
| 0.3698 | 17400 | 0.281 | - | - | - | - | - | - | - | - | - |
| 0.3762 | 17700 | 0.2922 | - | - | - | - | - | - | - | - | - |
| 0.3825 | 18000 | 0.279 | 0.2623 | 0.5851 | 0.2696 | 0.5097 | 0.4548 | 0.5849 | 0.2776 | 0.5570 | 0.4732 |
| 0.3889 | 18300 | 0.2894 | - | - | - | - | - | - | - | - | - |
| 0.3953 | 18600 | 0.283 | - | - | - | - | - | - | - | - | - |
| 0.4017 | 18900 | 0.2824 | - | - | - | - | - | - | - | - | - |
| 0.4080 | 19200 | 0.2758 | - | - | - | - | - | - | - | - | - |
| 0.4144 | 19500 | 0.2893 | - | - | - | - | - | - | - | - | - |
| 0.4208 | 19800 | 0.278 | - | - | - | - | - | - | - | - | - |
| 0.4272 | 20100 | 0.2814 | - | - | - | - | - | - | - | - | - |
| 0.4335 | 20400 | 0.278 | - | - | - | - | - | - | - | - | - |
| 0.4399 | 20700 | 0.2783 | - | - | - | - | - | - | - | - | - |
| 0.4463 | 21000 | 0.2803 | 0.2510 | 0.5880 | 0.2664 | 0.5664 | 0.4736 | 0.6115 | 0.2734 | 0.5465 | 0.4772 |
| 0.4527 | 21300 | 0.2668 | - | - | - | - | - | - | - | - | - |
| 0.4590 | 21600 | 0.2828 | - | - | - | - | - | - | - | - | - |
| 0.4654 | 21900 | 0.2815 | - | - | - | - | - | - | - | - | - |
| 0.4718 | 22200 | 0.2778 | - | - | - | - | - | - | - | - | - |
| 0.4782 | 22500 | 0.271 | - | - | - | - | - | - | - | - | - |
| 0.4845 | 22800 | 0.2696 | - | - | - | - | - | - | - | - | - |
| 0.4909 | 23100 | 0.2698 | - | - | - | - | - | - | - | - | - |
| 0.4973 | 23400 | 0.2768 | - | - | - | - | - | - | - | - | - |
| 0.5037 | 23700 | 0.2626 | - | - | - | - | - | - | - | - | - |
| 0.5100 | 24000 | 0.2611 | 0.2414 | 0.6078 | 0.2635 | 0.5668 | 0.4794 | 0.6231 | 0.2942 | 0.5944 | 0.5039 |
| 0.5164 | 24300 | 0.2736 | - | - | - | - | - | - | - | - | - |
| 0.5228 | 24600 | 0.2695 | - | - | - | - | - | - | - | - | - |
| 0.5292 | 24900 | 0.2673 | - | - | - | - | - | - | - | - | - |
| 0.5355 | 25200 | 0.2746 | - | - | - | - | - | - | - | - | - |
| 0.5419 | 25500 | 0.2681 | - | - | - | - | - | - | - | - | - |
| 0.5483 | 25800 | 0.2676 | - | - | - | - | - | - | - | - | - |
| 0.5547 | 26100 | 0.2686 | - | - | - | - | - | - | - | - | - |
| 0.5610 | 26400 | 0.2652 | - | - | - | - | - | - | - | - | - |
| 0.5674 | 26700 | 0.2596 | - | - | - | - | - | - | - | - | - |
| 0.5738 | 27000 | 0.2677 | 0.2494 | 0.6018 | 0.2460 | 0.5280 | 0.4586 | 0.6238 | 0.2775 | 0.5673 | 0.4895 |
| 0.5802 | 27300 | 0.2621 | - | - | - | - | - | - | - | - | - |
| 0.5865 | 27600 | 0.2558 | - | - | - | - | - | - | - | - | - |
| 0.5929 | 27900 | 0.251 | - | - | - | - | - | - | - | - | - |
| 0.5993 | 28200 | 0.2601 | - | - | - | - | - | - | - | - | - |
| 0.6057 | 28500 | 0.2612 | - | - | - | - | - | - | - | - | - |
| 0.6120 | 28800 | 0.2695 | - | - | - | - | - | - | - | - | - |
| 0.6184 | 29100 | 0.2662 | - | - | - | - | - | - | - | - | - |
| 0.6248 | 29400 | 0.2589 | - | - | - | - | - | - | - | - | - |
| 0.6312 | 29700 | 0.2602 | - | - | - | - | - | - | - | - | - |
| 0.6376 | 30000 | 0.2698 | 0.2507 | 0.5892 | 0.2996 | 0.5386 | 0.4758 | 0.6102 | 0.2941 | 0.5535 | 0.4860 |
| 0.6439 | 30300 | 0.2625 | - | - | - | - | - | - | - | - | - |
| 0.6503 | 30600 | 0.2598 | - | - | - | - | - | - | - | - | - |
| 0.6567 | 30900 | 0.2594 | - | - | - | - | - | - | - | - | - |
| 0.6631 | 31200 | 0.2618 | - | - | - | - | - | - | - | - | - |
| 0.6694 | 31500 | 0.2556 | - | - | - | - | - | - | - | - | - |
| 0.6758 | 31800 | 0.2591 | - | - | - | - | - | - | - | - | - |
| 0.6822 | 32100 | 0.2544 | - | - | - | - | - | - | - | - | - |
| 0.6886 | 32400 | 0.2589 | - | - | - | - | - | - | - | - | - |
| 0.6949 | 32700 | 0.2522 | - | - | - | - | - | - | - | - | - |
| 0.7013 | 33000 | 0.2521 | 0.2535 | 0.6053 | 0.2650 | 0.5329 | 0.4677 | 0.6115 | 0.2925 | 0.6057 | 0.5032 |
| 0.7077 | 33300 | 0.2576 | - | - | - | - | - | - | - | - | - |
| 0.7141 | 33600 | 0.2582 | - | - | - | - | - | - | - | - | - |
| 0.7204 | 33900 | 0.2567 | - | - | - | - | - | - | - | - | - |
| 0.7268 | 34200 | 0.2577 | - | - | - | - | - | - | - | - | - |
| 0.7332 | 34500 | 0.2568 | - | - | - | - | - | - | - | - | - |
| 0.7396 | 34800 | 0.254 | - | - | - | - | - | - | - | - | - |
| 0.7459 | 35100 | 0.2489 | - | - | - | - | - | - | - | - | - |
| 0.7523 | 35400 | 0.2545 | - | - | - | - | - | - | - | - | - |
| 0.7587 | 35700 | 0.2476 | - | - | - | - | - | - | - | - | - |
| 0.7651 | 36000 | 0.2637 | 0.2397 | 0.6138 | 0.2726 | 0.5627 | 0.4831 | 0.6056 | 0.2889 | 0.5745 | 0.4897 |
| 0.7714 | 36300 | 0.2508 | - | - | - | - | - | - | - | - | - |
| 0.7778 | 36600 | 0.2569 | - | - | - | - | - | - | - | - | - |
| 0.7842 | 36900 | 0.2419 | - | - | - | - | - | - | - | - | - |
| 0.7906 | 37200 | 0.2453 | - | - | - | - | - | - | - | - | - |
| 0.7969 | 37500 | 0.2456 | - | - | - | - | - | - | - | - | - |
| 0.8033 | 37800 | 0.2497 | - | - | - | - | - | - | - | - | - |
| 0.8097 | 38100 | 0.2556 | - | - | - | - | - | - | - | - | - |
| 0.8161 | 38400 | 0.252 | - | - | - | - | - | - | - | - | - |
| 0.8224 | 38700 | 0.2423 | - | - | - | - | - | - | - | - | - |
| 0.8288 | 39000 | 0.2545 | 0.2301 | 0.5927 | 0.2895 | 0.5553 | 0.4792 | 0.5979 | 0.2987 | 0.5587 | 0.4851 |
| 0.8352 | 39300 | 0.2482 | - | - | - | - | - | - | - | - | - |
| 0.8416 | 39600 | 0.2429 | - | - | - | - | - | - | - | - | - |
| 0.8479 | 39900 | 0.2463 | - | - | - | - | - | - | - | - | - |
| 0.8543 | 40200 | 0.2354 | - | - | - | - | - | - | - | - | - |
| 0.8607 | 40500 | 0.2466 | - | - | - | - | - | - | - | - | - |
| 0.8671 | 40800 | 0.2484 | - | - | - | - | - | - | - | - | - |
| 0.8734 | 41100 | 0.2448 | - | - | - | - | - | - | - | - | - |
| 0.8798 | 41400 | 0.2448 | - | - | - | - | - | - | - | - | - |
| 0.8862 | 41700 | 0.2515 | - | - | - | - | - | - | - | - | - |
| 0.8926 | 42000 | 0.2428 | 0.2392 | 0.6001 | 0.2826 | 0.5857 | 0.4895 | 0.6208 | 0.3019 | 0.6010 | 0.5079 |
| 0.8989 | 42300 | 0.2497 | - | - | - | - | - | - | - | - | - |
| 0.9053 | 42600 | 0.2415 | - | - | - | - | - | - | - | - | - |
| 0.9117 | 42900 | 0.2408 | - | - | - | - | - | - | - | - | - |
| 0.9181 | 43200 | 0.242 | - | - | - | - | - | - | - | - | - |
| 0.9245 | 43500 | 0.2412 | - | - | - | - | - | - | - | - | - |
| 0.9308 | 43800 | 0.2472 | - | - | - | - | - | - | - | - | - |
| 0.9372 | 44100 | 0.2408 | - | - | - | - | - | - | - | - | - |
| 0.9436 | 44400 | 0.2374 | - | - | - | - | - | - | - | - | - |
| 0.9500 | 44700 | 0.2312 | - | - | - | - | - | - | - | - | - |
| **0.9563** | **45000** | **0.2412** | **0.2379** | **0.6079** | **0.2711** | **0.5977** | **0.4922** | **0.6326** | **0.3071** | **0.5943** | **0.5113** |
| 0.9627 | 45300 | 0.2381 | - | - | - | - | - | - | - | - | - |
| 0.9691 | 45600 | 0.2456 | - | - | - | - | - | - | - | - | - |
| 0.9755 | 45900 | 0.2418 | - | - | - | - | - | - | - | - | - |
| 0.9818 | 46200 | 0.2355 | - | - | - | - | - | - | - | - | - |
| 0.9882 | 46500 | 0.2424 | - | - | - | - | - | - | - | - | - |
| 0.9946 | 46800 | 0.2389 | - | - | - | - | - | - | - | - | - |
* The bold row denotes the saved checkpoint.