You can deploy this model and start earning money today!

Views

No views yet

modernbert-embed-base-bible

Name: modernbert-embed-base-bible
Availability: InStock

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.

python

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
Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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

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}
}

	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`