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---
base_model: actualdata/bilingual-embedding-large
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4885
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: ' Le CO2, le CH4, le N2O, le SF6, le NF3 ainsi que les groupes
de gaz HFC et PFC.'
sentences:
- ' Qui a initié l''élaboration du guide sectoriel de réalisation d''un bilan des
émissions de gaz à effet de serre pour la filière cosmétique ?'
- ' Quel est l''objectif premier du Guide sectoriel de réalisation d''un bilan des
émissions de gaz à effet de serre pour la filière des sites de loisirs et culturels
?'
- ' Quel est le gaz contribuant à l''augmentation de l''effet de serre qui doit
être pris en compte dans la réalisation des bilans ?'
- source_sentence: ' Il est conseillé d''implémenter d''abord les leviers déjà matures
et « sans regret » (efficacité énergétique, efficacité matières, décarbonation
du mix énergétique) avant d''envisager des technologies moins matures.'
sentences:
- ' Quel est le recommandé ordre d''implémentation des leviers de décarbonation
?'
- ' Quels sont les types de connexions utilisés pour relier un utilisateur à une
ressource distante dans un réseau de communication ?'
- ' Comment peut-on utiliser le Bilan Carbone pour tenir compte de processus de
valorisation mis en œuvre par les entreprises du secteur agricole et agro-alimentaire
?'
- source_sentence: ' Les échanges ont permis de décrire des exemples par poste d''émissions.'
sentences:
- ' Quel était l''objectif des échanges sur les bonnes pratiques utilisées dans
le secteur ?'
- Existe-t-il une méthode rigoureuse pour déterminer l'incertitude de ces facteurs
d'émissions monétaires?
- ' Quels sont les modes de transport pris en compte dans cette fiche ?'
- source_sentence: ' La variation du périmètre organisationnel par la vente d''une
usine, la variation du périmètre opérationnel par l''achat d''une nouvelle ligne
de production, le changement de valeur de facteurs d''émission, le changement
du mix des produits des usines et la dégradation des outils de production.'
sentences:
- ' Quel type de repas a un total de quantité (g) de 83229,6 ? '
- Quel est l'objectif principal de la collecte des données pour la réalisation d'un
bilan GES ?
- ' Quels sont les facteurs qui ont influencé l''évolution des émissions de GES
de l''entreprise ?'
- source_sentence: ' Le PCS intègre l''énergie libérée par la condensation de l''eau
après la combustion, tandis que le PCI ne l''intègre pas.'
sentences:
- ' La proportion d''énergie utilisée dans l''eau chaude sanitaire pour les résidences
principales (métropole uniquement) est-elle supérieure à 1 % ?'
- ' Qu''est-ce qui distingue le Pouvoir Calorifique Supérieur (PCS) du Pouvoir Calorifique
Inférieur (PCI) ?'
- ' Quelle méthode de mesure directe par suivi de la consommation des véhicules
de transport sera privilégiée si le matériel de transport est contrôlé ?'
model-index:
- name: test qwen2 Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 1024
type: dim_1024
metrics:
- type: cosine_accuracy@1
value: 0.31675874769797424
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.425414364640884
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.47697974217311234
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5561694290976059
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.31675874769797424
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.141804788213628
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09539594843462246
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05561694290976059
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.31675874769797424
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.425414364640884
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.47697974217311234
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5561694290976059
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.42756869844177203
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.38761729369464176
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.399364505533715
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 896
type: dim_896
metrics:
- type: cosine_accuracy@1
value: 0.32228360957642727
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42357274401473294
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4732965009208103
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5488029465930019
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32228360957642727
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.14119091467157763
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09465930018416206
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05488029465930018
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.32228360957642727
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.42357274401473294
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.4732965009208103
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5488029465930019
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4272124343988002
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3893734105060072
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.40183454050045436
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.3314917127071823
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42357274401473294
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.47513812154696133
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5488029465930019
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3314917127071823
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.14119091467157763
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09502762430939225
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05488029465930018
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3314917127071823
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.42357274401473294
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.47513812154696133
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5488029465930019
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.43088591845526986
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.39430705369931895
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4065191633235482
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.30755064456721914
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4125230202578269
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4677716390423573
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5395948434622467
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.30755064456721914
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1375076734192756
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09355432780847145
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.053959484346224676
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.30755064456721914
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4125230202578269
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.4677716390423573
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5395948434622467
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.41562425407928066
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3769351632611302
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3895577962122803
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.2965009208103131
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.40515653775322286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.44751381215469616
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5395948434622467
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2965009208103131
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13505217925107427
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08950276243093921
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.053959484346224676
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.2965009208103131
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.40515653775322286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.44751381215469616
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5395948434622467
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.40786326501955955
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.367228653278377
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3789438619494699
name: Cosine Map@100
---
# test qwen2 Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [actualdata/bilingual-embedding-large](https://huggingface.co/actualdata/bilingual-embedding-large). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [actualdata/bilingual-embedding-large](https://huggingface.co/actualdata/bilingual-embedding-large) <!-- at revision b595d8ed97b05e847230c8bd2432ea248c2afe2d -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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): Normalize()
)
```
## 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 SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sylvain471/bl_ademe_large")
# Run inference
sentences = [
" Le PCS intègre l'énergie libérée par la condensation de l'eau après la combustion, tandis que le PCI ne l'intègre pas.",
" Qu'est-ce qui distingue le Pouvoir Calorifique Supérieur (PCS) du Pouvoir Calorifique Inférieur (PCI) ?",
" La proportion d'énergie utilisée dans l'eau chaude sanitaire pour les résidences principales (métropole uniquement) est-elle supérieure à 1 % ?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_1024`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3168 |
| cosine_accuracy@3 | 0.4254 |
| cosine_accuracy@5 | 0.477 |
| cosine_accuracy@10 | 0.5562 |
| cosine_precision@1 | 0.3168 |
| cosine_precision@3 | 0.1418 |
| cosine_precision@5 | 0.0954 |
| cosine_precision@10 | 0.0556 |
| cosine_recall@1 | 0.3168 |
| cosine_recall@3 | 0.4254 |
| cosine_recall@5 | 0.477 |
| cosine_recall@10 | 0.5562 |
| cosine_ndcg@10 | 0.4276 |
| cosine_mrr@10 | 0.3876 |
| **cosine_map@100** | **0.3994** |
#### Information Retrieval
* Dataset: `dim_896`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3223 |
| cosine_accuracy@3 | 0.4236 |
| cosine_accuracy@5 | 0.4733 |
| cosine_accuracy@10 | 0.5488 |
| cosine_precision@1 | 0.3223 |
| cosine_precision@3 | 0.1412 |
| cosine_precision@5 | 0.0947 |
| cosine_precision@10 | 0.0549 |
| cosine_recall@1 | 0.3223 |
| cosine_recall@3 | 0.4236 |
| cosine_recall@5 | 0.4733 |
| cosine_recall@10 | 0.5488 |
| cosine_ndcg@10 | 0.4272 |
| cosine_mrr@10 | 0.3894 |
| **cosine_map@100** | **0.4018** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3315 |
| cosine_accuracy@3 | 0.4236 |
| cosine_accuracy@5 | 0.4751 |
| cosine_accuracy@10 | 0.5488 |
| cosine_precision@1 | 0.3315 |
| cosine_precision@3 | 0.1412 |
| cosine_precision@5 | 0.095 |
| cosine_precision@10 | 0.0549 |
| cosine_recall@1 | 0.3315 |
| cosine_recall@3 | 0.4236 |
| cosine_recall@5 | 0.4751 |
| cosine_recall@10 | 0.5488 |
| cosine_ndcg@10 | 0.4309 |
| cosine_mrr@10 | 0.3943 |
| **cosine_map@100** | **0.4065** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3076 |
| cosine_accuracy@3 | 0.4125 |
| cosine_accuracy@5 | 0.4678 |
| cosine_accuracy@10 | 0.5396 |
| cosine_precision@1 | 0.3076 |
| cosine_precision@3 | 0.1375 |
| cosine_precision@5 | 0.0936 |
| cosine_precision@10 | 0.054 |
| cosine_recall@1 | 0.3076 |
| cosine_recall@3 | 0.4125 |
| cosine_recall@5 | 0.4678 |
| cosine_recall@10 | 0.5396 |
| cosine_ndcg@10 | 0.4156 |
| cosine_mrr@10 | 0.3769 |
| **cosine_map@100** | **0.3896** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2965 |
| cosine_accuracy@3 | 0.4052 |
| cosine_accuracy@5 | 0.4475 |
| cosine_accuracy@10 | 0.5396 |
| cosine_precision@1 | 0.2965 |
| cosine_precision@3 | 0.1351 |
| cosine_precision@5 | 0.0895 |
| cosine_precision@10 | 0.054 |
| cosine_recall@1 | 0.2965 |
| cosine_recall@3 | 0.4052 |
| cosine_recall@5 | 0.4475 |
| cosine_recall@10 | 0.5396 |
| cosine_ndcg@10 | 0.4079 |
| cosine_mrr@10 | 0.3672 |
| **cosine_map@100** | **0.3789** |
<!--
## 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
#### Unnamed Dataset
* Size: 4,885 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 32.82 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 26.77 tokens</li><li>max: 71 tokens</li></ul> |
* Samples:
| positive | anchor |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
| <code> Lorsque le traitement spécifique par catégorie de déchets produits par la Personne Morale est inconnu, le taux moyen local ou sectoriel de traitement en fin de vie (incinération, mise en décharge, recyclage, compostage, etc.) est utilisé. Le transport est également un paramètre à intégrer au calcul.</code> | <code> Quels sont les paramètres clés par type de traitement à prendre en compte pour réaliser un bilan d'émissions de gaz à effet de serre ?</code> |
| <code> Une analyse de cycle de vie fournit un moyen efficace et systémique pour évaluer les impacts environnementaux d’un produit, d’un service, d’une entreprise ou d’un procédé.</code> | <code> Qu'est-ce qu'une évaluation de cycle de vie (ACV) ?</code> |
| <code> 1 469,2 t CO2e.</code> | <code> Quel est le total des émissions annuelles de l'entreprise GAMMA ?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
896,
512,
256,
128
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 20
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `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`: 20
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `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_fused
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_896_cosine_map@100 |
|:-----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|
| 0.2614 | 10 | 5.4141 | - | - | - | - | - |
| 0.5229 | 20 | 4.2823 | - | - | - | - | - |
| 0.7843 | 30 | 3.0162 | - | - | - | - | - |
| 0.9935 | 38 | - | 0.3636 | 0.3170 | 0.3407 | 0.3566 | 0.3668 |
| 1.0458 | 40 | 2.5846 | - | - | - | - | - |
| 1.3072 | 50 | 2.2069 | - | - | - | - | - |
| 1.5686 | 60 | 1.7585 | - | - | - | - | - |
| 1.8301 | 70 | 1.3099 | - | - | - | - | - |
| 1.9869 | 76 | - | 0.3979 | 0.3353 | 0.3726 | 0.3895 | 0.3983 |
| 2.0915 | 80 | 1.1449 | - | - | - | - | - |
| 2.3529 | 90 | 1.0137 | - | - | - | - | - |
| 2.6144 | 100 | 0.6402 | - | - | - | - | - |
| 2.8758 | 110 | 0.4931 | - | - | - | - | - |
| 2.9804 | 114 | - | 0.4026 | 0.3568 | 0.3808 | 0.3882 | 0.3992 |
| 3.1373 | 120 | 0.4662 | - | - | - | - | - |
| 3.3987 | 130 | 0.3782 | - | - | - | - | - |
| 3.6601 | 140 | 0.2696 | - | - | - | - | - |
| 3.9216 | 150 | 0.2478 | - | - | - | - | - |
| 4.0 | 153 | - | 0.3805 | 0.3460 | 0.3613 | 0.3680 | 0.3850 |
| 4.1830 | 160 | 0.2655 | - | - | - | - | - |
| 4.4444 | 170 | 0.1952 | - | - | - | - | - |
| 4.7059 | 180 | 0.1494 | - | - | - | - | - |
| 4.9673 | 190 | 0.1482 | - | - | - | - | - |
| 4.9935 | 191 | - | 0.3806 | 0.3619 | 0.3702 | 0.3799 | 0.3814 |
| 5.2288 | 200 | 0.161 | - | - | - | - | - |
| 5.4902 | 210 | 0.1282 | - | - | - | - | - |
| 5.7516 | 220 | 0.0888 | - | - | - | - | - |
| 5.9869 | 229 | - | 0.3936 | 0.3685 | 0.3758 | 0.3870 | 0.3916 |
| 6.0131 | 230 | 0.1042 | - | - | - | - | - |
| 6.2745 | 240 | 0.126 | - | - | - | - | - |
| 6.5359 | 250 | 0.103 | - | - | - | - | - |
| 6.7974 | 260 | 0.0467 | - | - | - | - | - |
| 6.9804 | 267 | - | 0.4022 | 0.3689 | 0.3897 | 0.3950 | 0.4022 |
| 7.0588 | 270 | 0.0581 | - | - | - | - | - |
| 7.3203 | 280 | 0.0728 | - | - | - | - | - |
| 7.5817 | 290 | 0.064 | - | - | - | - | - |
| 7.8431 | 300 | 0.0271 | - | - | - | - | - |
| 8.0 | 306 | - | 0.4010 | 0.3756 | 0.3872 | 0.3988 | 0.4021 |
| 8.1046 | 310 | 0.0452 | - | - | - | - | - |
| 8.3660 | 320 | 0.0613 | - | - | - | - | - |
| 8.6275 | 330 | 0.0294 | - | - | - | - | - |
| 8.8889 | 340 | 0.0396 | - | - | - | - | - |
| 8.9935 | 344 | - | 0.3914 | 0.3722 | 0.3801 | 0.3916 | 0.3939 |
| 9.1503 | 350 | 0.024 | - | - | - | - | - |
| 9.4118 | 360 | 0.0253 | - | - | - | - | - |
| 9.6732 | 370 | 0.017 | - | - | - | - | - |
| 9.9346 | 380 | 0.0163 | - | - | - | - | - |
| 9.9869 | 382 | - | 0.3901 | 0.3660 | 0.3796 | 0.3892 | 0.3904 |
| 10.1961 | 390 | 0.0191 | - | - | - | - | - |
| 10.4575 | 400 | 0.017 | - | - | - | - | - |
| 10.7190 | 410 | 0.0108 | - | - | - | - | - |
| **10.9804** | **420** | **0.0118** | **0.3994** | **0.3789** | **0.3896** | **0.4065** | **0.4018** |
| 11.2418 | 430 | 0.0111 | - | - | - | - | - |
| 11.5033 | 440 | 0.011 | - | - | - | - | - |
| 11.7647 | 450 | 0.0052 | - | - | - | - | - |
| 12.0 | 459 | - | 0.4030 | 0.3772 | 0.3986 | 0.4034 | 0.3999 |
| 12.0261 | 460 | 0.0144 | - | - | - | - | - |
| 12.2876 | 470 | 0.0068 | - | - | - | - | - |
| 12.5490 | 480 | 0.0061 | - | - | - | - | - |
| 12.8105 | 490 | 0.0039 | - | - | - | - | - |
| 12.9935 | 497 | - | 0.4022 | 0.3733 | 0.3869 | 0.3995 | 0.3983 |
| 13.0719 | 500 | 0.0074 | - | - | - | - | - |
| 13.3333 | 510 | 0.005 | - | - | - | - | - |
| 13.5948 | 520 | 0.0045 | - | - | - | - | - |
| 13.8562 | 530 | 0.0035 | - | - | - | - | - |
| 13.9869 | 535 | - | 0.4027 | 0.3779 | 0.3891 | 0.4015 | 0.3999 |
| 14.1176 | 540 | 0.0047 | - | - | - | - | - |
| 14.3791 | 550 | 0.0043 | - | - | - | - | - |
| 14.6405 | 560 | 0.0038 | - | - | - | - | - |
| 14.9020 | 570 | 0.0034 | - | - | - | - | - |
| 14.9804 | 573 | - | 0.3954 | 0.3734 | 0.3875 | 0.3982 | 0.3962 |
| 15.1634 | 580 | 0.0037 | - | - | - | - | - |
| 15.4248 | 590 | 0.0039 | - | - | - | - | - |
| 15.6863 | 600 | 0.0034 | - | - | - | - | - |
| 15.9477 | 610 | 0.0033 | - | - | - | - | - |
| 16.0 | 612 | - | 0.3966 | 0.3720 | 0.3852 | 0.3948 | 0.3936 |
| 16.2092 | 620 | 0.0038 | - | - | - | - | - |
| 16.4706 | 630 | 0.0034 | - | - | - | - | - |
| 16.7320 | 640 | 0.0029 | - | - | - | - | - |
| 16.9935 | 650 | 0.0033 | 0.3968 | 0.3723 | 0.3844 | 0.3977 | 0.3966 |
| 17.2549 | 660 | 0.0034 | - | - | - | - | - |
| 17.5163 | 670 | 0.0033 | - | - | - | - | - |
| 17.7778 | 680 | 0.0028 | - | - | - | - | - |
| 17.9869 | 688 | - | 0.3965 | 0.3695 | 0.3861 | 0.3960 | 0.3969 |
| 18.0392 | 690 | 0.0033 | - | - | - | - | - |
| 18.3007 | 700 | 0.0033 | - | - | - | - | - |
| 18.5621 | 710 | 0.0036 | - | - | - | - | - |
| 18.8235 | 720 | 0.0026 | - | - | - | - | - |
| 18.9804 | 726 | - | 0.3962 | 0.3701 | 0.3819 | 0.3951 | 0.3964 |
| 19.0850 | 730 | 0.003 | - | - | - | - | - |
| 19.3464 | 740 | 0.0036 | - | - | - | - | - |
| 19.6078 | 750 | 0.0033 | - | - | - | - | - |
| 19.8693 | 760 | 0.0031 | 0.3994 | 0.3789 | 0.3896 | 0.4065 | 0.4018 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 2.21.0
- Tokenizers: 0.19.1
## 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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