README.md

metadata

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 model finetuned from 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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 tokens
• Similarity Function: Cosine Similarity
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Evaluation

Metrics

Information Retrieval

• Dataset: dim_1024
• Evaluated with 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 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 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 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 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

Training Details

Training Dataset

Unnamed Dataset

• Size: 4,885 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 3 tokens mean: 32.82 tokens max: 185 tokens	min: 2 tokens mean: 26.77 tokens max: 71 tokens

• Samples:

  positive	anchor
  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.	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 ?
  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.	Qu'est-ce qu'une évaluation de cycle de vie (ACV) ?
  1 469,2 t CO2e.	Quel est le total des émissions annuelles de l'entreprise GAMMA ?

• Loss: MatryoshkaLoss with these parameters:

  {
      "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

Click to expand

• 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

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

@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

@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

@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}
}