metadata

base_model: BAAI/bge-m3
library_name: sentence-transformers
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:3814
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      Sol·licitud de l'informe d'integració social per a la renovació o
      modificació de la residència.
    sentences:
      - Quin és el propòsit de la renovació o modificació de la residència?
      - >-
        Quin és el paper de l'Administració en la Declaració responsable
        d'obertura?
      - Quin és el lloc on es pot realitzar l'ocupació de la via pública?
  - source_sentence: >-
      Aquest tràmit permet obtenir la llicència d'ocupació de la via pública per
      a la instal·lació de grues desmuntables.
    sentences:
      - Quin és el propòsit de la consulta del Cens Electoral?
      - >-
        Quin és el tràmit necessari per a la instal·lació de grues desmuntables
        en una via pública?
      - Quines reclamacions es consideren en aquest tràmit?
  - source_sentence: >-
      Bonificacions: Persones amb discapacitat: bonificació 50%. Laboratori
      d'art: Preu: 15€/mes
    sentences:
      - >-
        Quin és el preu del curs de Laboratori d'art per a persones amb
        discapacitat?
      - >-
        Quin és el paper de les oficines municipals d'atenció ciutadana en la
        renovació de la inscripció padronal?
      - >-
        Quin és el període en què les entitats i associacions registrades han de
        notificar les modificacions produïdes en les dades registrals?
  - source_sentence: >-
      Es tracta de la sol·licitud d'elaboració del certificat que justifica
      l'antiguitat i legalitat d'un immoble, document necessari en el moment de
      la venda, per poder-lo inscriure al Registre de la Propietat si no es va
      fer en finalitzar l'obra.
    sentences:
      - >-
        Què ha de fer el responsable en relació amb els destinataris quan es
        limita el tractament de dades personals?
      - >-
        Quin és el motiu pel qual es sol·licita el certificat d'antiguitat i
        legalitat urbanística en la venda d'un immoble?
      - >-
        Qui és el destinatari de la comunicació de canvi de titularitat
        d'activitats?
  - source_sentence: >-
      Laboratori d'art: D'octubre 2024 a maig de 2025. Horari: Dilluns de 17.30h
      a 19.00h
    sentences:
      - Quin és el dia i hora del curs de Laboratori d'art?
      - >-
        Quin és el paper dels dipòsits o fiances en la garantia d'abocament
        controlat de runes?
      - On es pot sol·licitar la reserva especial d'estacionament?
model-index:
  - name: SentenceTransformer based on BAAI/bge-m3
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 1024
          type: dim_1024
        metrics:
          - type: cosine_accuracy@1
            value: 0.10384615384615385
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.2153846153846154
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.27692307692307694
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.48846153846153845
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.10384615384615385
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.07179487179487179
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.055384615384615386
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.048846153846153845
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.10384615384615385
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.2153846153846154
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.27692307692307694
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.48846153846153845
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2612154031642473
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.193324175824176
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.21923866500444808
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.11923076923076924
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.23076923076923078
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.31153846153846154
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5307692307692308
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.11923076923076924
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.07692307692307693
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.06230769230769231
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05307692307692307
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.11923076923076924
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.23076923076923078
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.31153846153846154
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5307692307692308
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2878219714456531
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.21504578754578765
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.23782490878695842
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.12692307692307692
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.23846153846153847
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.3269230769230769
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5269230769230769
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.12692307692307692
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.07948717948717948
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.06538461538461539
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05269230769230769
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.12692307692307692
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.23846153846153847
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.3269230769230769
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5269230769230769
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2920408684487264
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.22163461538461554
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.24439125474069504
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.10384615384615385
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.2076923076923077
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.3076923076923077
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.49615384615384617
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.10384615384615385
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.06923076923076923
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.06153846153846154
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.04961538461538462
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.10384615384615385
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.2076923076923077
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.3076923076923077
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.49615384615384617
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.26493374179245505
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.195289987789988
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.22019396693132914
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.13076923076923078
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.23076923076923078
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.3423076923076923
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.55
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.13076923076923078
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.07692307692307693
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.06846153846153846
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05499999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.13076923076923078
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.23076923076923078
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.3423076923076923
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.55
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.30010874813387883
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2253495115995117
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2488774864299421
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.10384615384615385
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.2230769230769231
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.2846153846153846
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.49230769230769234
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.10384615384615385
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.07435897435897434
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.05692307692307692
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.04923076923076923
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.10384615384615385
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.2230769230769231
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.2846153846153846
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.49230769230769234
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2636327280635836
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.19504273504273517
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.21974930573072288
            name: Cosine Map@100

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3 on the json dataset. 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: BAAI/bge-m3
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- json

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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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): 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("adriansanz/ST-tramits-MONT-001-5ep")
# Run inference
sentences = [
    "Laboratori d'art: D'octubre 2024 a maig de 2025. Horari: Dilluns de 17.30h a 19.00h",
    "Quin és el dia i hora del curs de Laboratori d'art?",
    "On es pot sol·licitar la reserva especial d'estacionament?",
]
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	0.1038
cosine_accuracy@3	0.2154
cosine_accuracy@5	0.2769
cosine_accuracy@10	0.4885
cosine_precision@1	0.1038
cosine_precision@3	0.0718
cosine_precision@5	0.0554
cosine_precision@10	0.0488
cosine_recall@1	0.1038
cosine_recall@3	0.2154
cosine_recall@5	0.2769
cosine_recall@10	0.4885
cosine_ndcg@10	0.2612
cosine_mrr@10	0.1933
cosine_map@100	0.2192

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1192
cosine_accuracy@3	0.2308
cosine_accuracy@5	0.3115
cosine_accuracy@10	0.5308
cosine_precision@1	0.1192
cosine_precision@3	0.0769
cosine_precision@5	0.0623
cosine_precision@10	0.0531
cosine_recall@1	0.1192
cosine_recall@3	0.2308
cosine_recall@5	0.3115
cosine_recall@10	0.5308
cosine_ndcg@10	0.2878
cosine_mrr@10	0.215
cosine_map@100	0.2378

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1269
cosine_accuracy@3	0.2385
cosine_accuracy@5	0.3269
cosine_accuracy@10	0.5269
cosine_precision@1	0.1269
cosine_precision@3	0.0795
cosine_precision@5	0.0654
cosine_precision@10	0.0527
cosine_recall@1	0.1269
cosine_recall@3	0.2385
cosine_recall@5	0.3269
cosine_recall@10	0.5269
cosine_ndcg@10	0.292
cosine_mrr@10	0.2216
cosine_map@100	0.2444

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1038
cosine_accuracy@3	0.2077
cosine_accuracy@5	0.3077
cosine_accuracy@10	0.4962
cosine_precision@1	0.1038
cosine_precision@3	0.0692
cosine_precision@5	0.0615
cosine_precision@10	0.0496
cosine_recall@1	0.1038
cosine_recall@3	0.2077
cosine_recall@5	0.3077
cosine_recall@10	0.4962
cosine_ndcg@10	0.2649
cosine_mrr@10	0.1953
cosine_map@100	0.2202

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1308
cosine_accuracy@3	0.2308
cosine_accuracy@5	0.3423
cosine_accuracy@10	0.55
cosine_precision@1	0.1308
cosine_precision@3	0.0769
cosine_precision@5	0.0685
cosine_precision@10	0.055
cosine_recall@1	0.1308
cosine_recall@3	0.2308
cosine_recall@5	0.3423
cosine_recall@10	0.55
cosine_ndcg@10	0.3001
cosine_mrr@10	0.2253
cosine_map@100	0.2489

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1038
cosine_accuracy@3	0.2231
cosine_accuracy@5	0.2846
cosine_accuracy@10	0.4923
cosine_precision@1	0.1038
cosine_precision@3	0.0744
cosine_precision@5	0.0569
cosine_precision@10	0.0492
cosine_recall@1	0.1038
cosine_recall@3	0.2231
cosine_recall@5	0.2846
cosine_recall@10	0.4923
cosine_ndcg@10	0.2636
cosine_mrr@10	0.195
cosine_map@100	0.2197

Training Details

Training Dataset

json

Dataset: json
Size: 3,814 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 3 tokens
mean: 39.27 tokens
max: 165 tokens

min: 9 tokens
mean: 20.67 tokens
max: 50 tokens

	positive	anchor
type	string	string
details	min: 3 tokens mean: 39.27 tokens max: 165 tokens	min: 9 tokens mean: 20.67 tokens max: 50 tokens

Samples:

positive	anchor
`Aquest tràmit permet obtenir la llicència per a ocupació de la via pública per quioscs, casetes o parades (xurreries, gelats,...).`	`Quins són els requisits per obtenir la llicència d'ocupació de la via pública per a gelats?`
`Aquest tràmit permet obtenir la llicència d'ocupació de la via pública per a la instal·lació de grues desmuntables.`	`Quin és el lloc on es pot obtenir la llicència d'ocupació de la via pública per a la instal·lació de grues desmuntables en una via pública?`
`L’Espai Jove de Montgat disposa de dues sales, una aula, i una sala chill-out així com jardins i serveis adreçats als joves del municipi.`	`Quin és el propòsit de l'aula de l'Espai Jove de Montgat?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 5
lr_scheduler_type: cosine
warmup_ratio: 0.2
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: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
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: 5
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.2
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_768_cosine_map@100	dim_512_cosine_map@100	dim_256_cosine_map@100	dim_128_cosine_map@100	dim_64_cosine_map@100
0.6695	10	3.4242	-	-	-	-	-	-
0.9372	14	-	0.2075	0.2165	0.2078	0.1957	0.2050	0.1949
1.3389	20	1.666	-	-	-	-	-	-
1.9414	29	-	0.2145	0.2184	0.2248	0.2144	0.2244	0.2112
2.0084	30	0.7666	-	-	-	-	-	-
2.6778	40	0.4859	-	-	-	-	-	-
2.9456	44	-	0.2263	0.2408	0.2234	0.2274	0.252	0.2313
3.3473	50	0.277	-	-	-	-	-	-
3.9498	59	-	0.2107	0.2359	0.2386	0.2275	0.2382	0.2246
4.0167	60	0.2423	-	-	-	-	-	-
4.6862	70	0.2281	0.2192	0.2378	0.2444	0.2202	0.2489	0.2197

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.2.0
Transformers: 4.44.2
PyTorch: 2.4.1+cu121
Accelerate: 1.1.0.dev0
Datasets: 3.0.1
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}
}