metadata

base_model: nomic-ai/modernbert-embed-base
language:
  - fr
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_ndcg@15
  - cosine_ndcg@20
  - cosine_mrr@10
  - cosine_map@100
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:47560
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      Pourquoi l'enfant de Jéroboam sera-t-il le seul de sa maison à être
      enterré?
    sentences:
      - Nathan le prophète.
      - >-
        Parce qu'il est le seul de la maison de Jéroboam en qui se soit trouvé
        quelque chose de bon devant l'Éternel, le Dieu d'Israël.
      - Deux ans.
  - source_sentence: >-
      Que dit le texte sur la foi capable de transporter des montagnes sans
      charité?
    sentences:
      - Urie était un Héthien.
      - >-
        Il dit que même avec une foi capable de transporter des montagnes, sans
        la charité, cela ne vaut rien.
      - >-
        David est allé se présenter devant l'Éternel et a exprimé son humilité
        et sa gratitude envers Dieu.
  - source_sentence: Quels sont les noms des fils de Schobal?
    sentences:
      - Reaja, Jachath, Achumaï et Lahad.
      - Le côté du midi échut à Obed-Édom, et la maison des magasins à ses fils.
      - Meschélémia avait dix-huit fils et frères vaillants.
  - source_sentence: Qui a succédé au roi Asa après sa mort?
    sentences:
      - 'L''un dit: Moi, je suis de Paul! Et un autre: Moi, d''Apollos!'
      - >-
        Neuf fils: Zemira, Joasch, Éliézer, Éljoénaï, Omri, Jerémoth, Abija,
        Anathoth et Alameth, enregistrés au nombre de vingt mille deux cents.
      - Josaphat, son fils.
  - source_sentence: >-
      Quelles tâches les Lévites devaient-ils accomplir dans le service de la
      maison de l'Éternel?
    sentences:
      - >-
        Ils devaient prendre soin des parvis et des chambres, purifier toutes
        les choses saintes, s'occuper des pains de proposition, de la fleur de
        farine pour les offrandes, des galettes sans levain, des gâteaux cuits
        sur la plaque et des gâteaux frits, et de toutes les mesures de capacité
        et de longueur.
      - >-
        Les chefs des maisons paternelles, les chefs des tribus d'Israël, les
        chefs de milliers et de centaines, et les intendants du roi.
      - Les enfants sont considérés comme saints.
co2_eq_emissions:
  emissions: 11.494424944753328
  energy_consumed: 0.20511474053343792
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
  ram_total_size: 7.6847381591796875
  hours_used: 6.806
  hardware_used: 1 x NVIDIA GeForce GTX 1660 Ti
model-index:
  - name: modernbert-embed-base-bible
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.17498667614141056
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.24835672410730147
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.2762480014212116
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.320305560490318
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.17498667614141056
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.08278557470243382
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.05524960028424231
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0320305560490318
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.17498667614141056
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.24835672410730147
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.2762480014212116
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.320305560490318
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.24430049048684818
            name: Cosine Ndcg@10
          - type: cosine_ndcg@15
            value: 0.2525347835304927
            name: Cosine Ndcg@15
          - type: cosine_ndcg@20
            value: 0.2574496509992833
            name: Cosine Ndcg@20
          - type: cosine_mrr@10
            value: 0.2204687601338871
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.22764969395073778
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.17161129863208385
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.24018475750577367
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.2719843666725884
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.31621957718955407
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.17161129863208385
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.08006158583525788
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.05439687333451768
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.03162195771895541
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.17161129863208385
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.24018475750577367
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.2719843666725884
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.31621957718955407
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.23947113373513576
            name: Cosine Ndcg@10
          - type: cosine_ndcg@15
            value: 0.24636222462199156
            name: Cosine Ndcg@15
          - type: cosine_ndcg@20
            value: 0.2517242130957284
            name: Cosine Ndcg@20
          - type: cosine_mrr@10
            value: 0.2154852845384024
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2225725360678114
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.16024160596908865
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.22757150470776336
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.2602593711138746
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.3075146562444484
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.16024160596908865
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.07585716823592112
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.052051874222774915
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.030751465624444838
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.16024160596908865
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.22757150470776336
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.2602593711138746
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.3075146562444484
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.22844579790475078
            name: Cosine Ndcg@10
          - type: cosine_ndcg@15
            value: 0.2357050364715922
            name: Cosine Ndcg@15
          - type: cosine_ndcg@20
            value: 0.24051535612507915
            name: Cosine Ndcg@20
          - type: cosine_mrr@10
            value: 0.20381231547513284
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.21077486383464478
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.14372002131817374
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.20465446793391368
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.23307869959140168
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.279445727482679
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.14372002131817374
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.06821815597797122
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.04661573991828033
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0279445727482679
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.14372002131817374
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.20465446793391368
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.23307869959140168
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.279445727482679
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.20572968417646773
            name: Cosine Ndcg@10
          - type: cosine_ndcg@15
            value: 0.21411686675503838
            name: Cosine Ndcg@15
          - type: cosine_ndcg@20
            value: 0.21935674398662894
            name: Cosine Ndcg@20
          - type: cosine_mrr@10
            value: 0.1828928000406064
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.19012440317942259
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.11067685201634393
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.15953100017765146
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.18617871735654645
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.22721620181204477
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.11067685201634393
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.05317700005921715
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.03723574347130929
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.022721620181204476
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.11067685201634393
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.15953100017765146
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.18617871735654645
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.22721620181204477
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.16327341570689552
            name: Cosine Ndcg@10
          - type: cosine_ndcg@15
            value: 0.1699977455983759
            name: Cosine Ndcg@15
          - type: cosine_ndcg@20
            value: 0.17462327712912765
            name: Cosine Ndcg@20
          - type: cosine_mrr@10
            value: 0.1435284115422685
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.1500325081763102
            name: Cosine Map@100

modernbert-embed-base-bible

This is a sentence-transformers model finetuned from nomic-ai/modernbert-embed-base on the json dataset. It maps sentences & paragraphs to a 768-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: nomic-ai/modernbert-embed-base
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
Language: fr
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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("Steve77/modernbert-embed-base-bible")
# Run inference
sentences = [
    "Quelles tâches les Lévites devaient-ils accomplir dans le service de la maison de l'Éternel?",
    "Ils devaient prendre soin des parvis et des chambres, purifier toutes les choses saintes, s'occuper des pains de proposition, de la fleur de farine pour les offrandes, des galettes sans levain, des gâteaux cuits sur la plaque et des gâteaux frits, et de toutes les mesures de capacité et de longueur.",
    "Les chefs des maisons paternelles, les chefs des tribus d'Israël, les chefs de milliers et de centaines, et les intendants du roi.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Datasets: dim_768, dim_512, dim_256, dim_128 and dim_64
Evaluated with InformationRetrievalEvaluator

Metric	dim_768	dim_512	dim_256	dim_128	dim_64
cosine_accuracy@1	0.175	0.1716	0.1602	0.1437	0.1107
cosine_accuracy@3	0.2484	0.2402	0.2276	0.2047	0.1595
cosine_accuracy@5	0.2762	0.272	0.2603	0.2331	0.1862
cosine_accuracy@10	0.3203	0.3162	0.3075	0.2794	0.2272
cosine_precision@1	0.175	0.1716	0.1602	0.1437	0.1107
cosine_precision@3	0.0828	0.0801	0.0759	0.0682	0.0532
cosine_precision@5	0.0552	0.0544	0.0521	0.0466	0.0372
cosine_precision@10	0.032	0.0316	0.0308	0.0279	0.0227
cosine_recall@1	0.175	0.1716	0.1602	0.1437	0.1107
cosine_recall@3	0.2484	0.2402	0.2276	0.2047	0.1595
cosine_recall@5	0.2762	0.272	0.2603	0.2331	0.1862
cosine_recall@10	0.3203	0.3162	0.3075	0.2794	0.2272
cosine_ndcg@10	0.2443	0.2395	0.2284	0.2057	0.1633
cosine_ndcg@15	0.2525	0.2464	0.2357	0.2141	0.17
cosine_ndcg@20	0.2574	0.2517	0.2405	0.2194	0.1746
cosine_mrr@10	0.2205	0.2155	0.2038	0.1829	0.1435
cosine_map@100	0.2276	0.2226	0.2108	0.1901	0.15

Training Details

Training Dataset

json

Dataset: json
Size: 47,560 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 8 tokens
mean: 21.11 tokens
max: 45 tokens

min: 3 tokens
mean: 24.84 tokens
max: 108 tokens
Samples:

anchor positive

Quels sont les noms des fils de Schobal? Aljan, Manahath, Ébal, Schephi et Onam

Quels sont les noms des fils de Tsibeon? Ajja et Ana

Qui est le fils d'Ana? Dischon

	anchor	positive
type	string	string
details	min: 8 tokens mean: 21.11 tokens max: 45 tokens	min: 3 tokens mean: 24.84 tokens max: 108 tokens

anchor	positive
`Quels sont les noms des fils de Schobal?`	`Aljan, Manahath, Ébal, Schephi et Onam`
`Quels sont les noms des fils de Tsibeon?`	`Ajja et Ana`
`Qui est le fils d'Ana?`	`Dischon`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "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
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: 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: 4
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: 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_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: 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

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@20	dim_512_cosine_ndcg@20	dim_256_cosine_ndcg@20	dim_128_cosine_ndcg@20	dim_64_cosine_ndcg@20
0.0538	10	12.274	-	-	-	-	-
0.1076	20	11.5084	-	-	-	-	-
0.1615	30	10.5276	-	-	-	-	-
0.2153	40	9.0432	-	-	-	-	-
0.2691	50	7.572	-	-	-	-	-
0.3229	60	7.7696	-	-	-	-	-
0.3767	70	6.5673	-	-	-	-	-
0.4305	80	6.6586	-	-	-	-	-
0.4844	90	5.5276	-	-	-	-	-
0.5382	100	5.9891	-	-	-	-	-
0.5920	110	5.2983	-	-	-	-	-
0.6458	120	5.6242	-	-	-	-	-
0.6996	130	5.498	-	-	-	-	-
0.7534	140	4.4201	-	-	-	-	-
0.8073	150	4.3818	-	-	-	-	-
0.8611	160	4.2175	-	-	-	-	-
0.9149	170	4.2341	-	-	-	-	-
0.9687	180	4.3349	-	-	-	-	-
0.9956	185	-	0.2664	0.2607	0.2508	0.2263	0.1796
1.0269	190	4.6803	-	-	-	-	-
1.0807	200	3.877	-	-	-	-	-
1.1345	210	4.0309	-	-	-	-	-
1.1884	220	4.0755	-	-	-	-	-
1.2422	230	3.9068	-	-	-	-	-
1.2960	240	4.188	-	-	-	-	-
1.3498	250	4.3417	-	-	-	-	-
1.4036	260	4.0526	-	-	-	-	-
1.4575	270	3.3933	-	-	-	-	-
1.5113	280	3.8309	-	-	-	-	-
1.5651	290	3.5633	-	-	-	-	-
1.6189	300	3.8179	-	-	-	-	-
1.6727	310	4.0671	-	-	-	-	-
1.7265	320	3.3919	-	-	-	-	-
1.7804	330	2.6578	-	-	-	-	-
1.8342	340	2.6953	-	-	-	-	-
1.8880	350	2.8858	-	-	-	-	-
1.9418	360	2.8933	-	-	-	-	-
1.9956	370	2.9603	0.2775	0.2737	0.2637	0.2402	0.1916
2.0538	380	3.3361	-	-	-	-	-
2.1076	390	2.7904	-	-	-	-	-
2.1615	400	3.0108	-	-	-	-	-
2.2153	410	2.8917	-	-	-	-	-
2.2691	420	3.0295	-	-	-	-	-
2.3229	430	3.5609	-	-	-	-	-
2.3767	440	2.7722	-	-	-	-	-
2.4305	450	3.2115	-	-	-	-	-
2.4844	460	2.6333	-	-	-	-	-
2.5382	470	3.2503	-	-	-	-	-
2.5920	480	2.7708	-	-	-	-	-
2.6458	490	3.167	-	-	-	-	-
2.6996	500	3.1447	-	-	-	-	-
2.7534	510	2.0428	-	-	-	-	-
2.8073	520	2.0001	-	-	-	-	-
2.8611	530	2.0826	-	-	-	-	-
2.9149	540	2.0853	-	-	-	-	-
2.9687	550	2.2365	-	-	-	-	-
2.9956	555	-	0.2660	0.2604	0.2509	0.2266	0.1810
3.0269	560	2.762	-	-	-	-	-
3.0807	570	2.1219	-	-	-	-	-
3.1345	580	2.2908	-	-	-	-	-
3.1884	590	2.6195	-	-	-	-	-
3.2422	600	2.3468	-	-	-	-	-
3.2960	610	2.7504	-	-	-	-	-
3.3498	620	2.9486	-	-	-	-	-
3.4036	630	2.7281	-	-	-	-	-
3.4575	640	2.188	-	-	-	-	-
3.5113	650	2.5494	-	-	-	-	-
3.5651	660	2.426	-	-	-	-	-
3.6189	670	2.6478	-	-	-	-	-
3.6727	680	2.9209	-	-	-	-	-
3.7265	690	2.3512	-	-	-	-	-
3.7804	700	1.6746	-	-	-	-	-
3.8342	710	1.739	-	-	-	-	-
3.8880	720	1.951	-	-	-	-	-
3.9418	730	1.9886	-	-	-	-	-
3.9956	740	2.1022	0.2574	0.2517	0.2405	0.2194	0.1746

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.205 kWh
Carbon Emitted: 0.011 kg of CO2
Hours Used: 6.806 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce GTX 1660 Ti
CPU Model: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
RAM Size: 7.68 GB

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.3.1
Transformers: 4.48.0.dev0
PyTorch: 2.5.1
Accelerate: 1.2.1
Datasets: 2.19.1
Tokenizers: 0.21.0

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}
}