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
anddim_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
andpositive
- 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
- 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
: epochper_device_train_batch_size
: 16per_device_eval_batch_size
: 16gradient_accumulation_steps
: 16learning_rate
: 2e-05num_train_epochs
: 4lr_scheduler_type
: cosinewarmup_ratio
: 0.1bf16
: Trueload_best_model_at_end
: Trueoptim
: adamw_torch_fusedbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 16eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 4max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torch_fusedoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: no_duplicatesmulti_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}
}
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Model tree for Steve77/modernbert-embed-base-bible
Base model
answerdotai/ModernBERT-base
Quantized
nomic-ai/modernbert-embed-base
Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.175
- Cosine Accuracy@3 on dim 768self-reported0.248
- Cosine Accuracy@5 on dim 768self-reported0.276
- Cosine Accuracy@10 on dim 768self-reported0.320
- Cosine Precision@1 on dim 768self-reported0.175
- Cosine Precision@3 on dim 768self-reported0.083
- Cosine Precision@5 on dim 768self-reported0.055
- Cosine Precision@10 on dim 768self-reported0.032
- Cosine Recall@1 on dim 768self-reported0.175
- Cosine Recall@3 on dim 768self-reported0.248