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Parallia/
Fairly-Multilingual-ModernBERT-Embed-BE

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FMMB-BE: The Fairly Multilingual ModernBERT Embedding Model (Belgian Edition)

🇧🇪 The Fairly Multilingual ModernBERT Embedding Model (Belgian Edition) is the perfect model for embedding at the speed of light texts of up to 8192 tokens written in French, Dutch, German or English. It produces embeddings very similar across languages

🔀 For each input text, the FMMB model autodetects the most efficient tokenizer (English, French, Dutch, or German) and routes the input text to that tokenizer. Each tokenizer uses its own language-specific token embeddings, reducing the risk of language interference. Because all the other weights are shared, the FMMB models can mix and match different languages in the same batch without requiring to load 4 different models in memory. That said: if you know the tokenizer you want to use in advance, you can use the monolingual variants for French, Dutch, German or English for a faster tokenization and lower memory footprint.

🆘 This sentence-transformers model was trained on a small parallel corpus containing English-French, English-Dutch, and English-German sentence pairs. 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. The input texts can be used as-is, no need to use prefixes.

🪄 Thanks to the magic of Trans-Tokenization, monoligual English models such as ModernBERT-Embed from Nomic AI can be turned into embedding models for another language. And this, with almost no GPU compute involved! 🤯

⚖️ Each of the 5 FMMB-BE models are actually copies of the exact same model, paired with different tokenizers and embedding tables. Indeed, as all trans-tokenized models operate on embeddings in the same latent space, aligning them cross-lingually is a breeze: after creating a "super" model which can speak in all of the 4 tokenizers, this model can be finetuned to produce similar embeddings for sentences which are translation of each other.

⚡ ModernBERT, developped last month by Answer Ai and LightOn, is about 3x to 6x faster at inference time than regular BERT/RoBERTa models, while providing us with superior results. This makes it a wonderful choice for many use cases.

⚠️ This model is cross-lingually aligned, but trained in an unsupervised manner. It is recommended to finetune this model on your use case before using it.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: ModernBERT-Embed-Base
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- parallel-sentences
Languages: fr,nl,de,en
License: apache-2.0

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})
)

Usage

IMPORTANT: While waiting for the next stable release of the transformers library, please install the latest git release to use modernbert models:

pip install --upgrade git+https://github.com/huggingface/transformers.git

The easiest way to use this model is to 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("Parallia/Fairly-Multilingual-ModernBERT-Embed-BE", trust_remote_code=True)
# Run inference
sentences = [
    'These three mysterious men came to our help.',
    'Three strange guys helped us then.',
    'These three black birds came in our garden.',
    'Some people are helpful.',
    'One, two, three... Who can guess the next digits?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [5, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [5, 5]

Training Details

Training Dataset

parallel-sentences

Dataset: parallel dataset
Size: 8,066,634 training samples
Columns: sent1 and sent2
Approximate statistics based on the first 1000 samples:
sent1 sent2
type string string
details
min: 6 tokens
mean: 17.86 tokens
max: 46 tokens
min: 6 tokens
mean: 18.87 tokens
max: 52 tokens

Samples:

sent1	sent2
`The faces may change, but the essential views that have characterised Israel’s government for decades will remain the same after 9 April`	`Les visages peuvent changer, mais les opinions fondamentales qui caractérisent le gouvernement israélien depuis des décennies resteront les mêmes après le 9 avril`
`- Yeah. My husband never talked about business.`	`M'n man had het nooit over z'n zaken.`
`Or do they think that We hear not their secrets and their private counsels?`	`Oder meinen sie, daß Wir ihre Geheimnisse und heimlichen Beratungen nicht hören?`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
bf16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
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: 1
max_steps: -1
lr_scheduler_type: linear
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: False
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
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: batch_sampler
multi_dataset_batch_sampler: proportional

Framework Versions

Python: 3.11.7
Sentence Transformers: 3.3.1
Transformers: 4.48.0.dev0
PyTorch: 2.2.0+cu121
Accelerate: 1.0.1
Datasets: 3.2.0
Tokenizers: 0.21.0

Citation

If you use or finetune this model, please consider citing this paper and the sentence-transformers library:

BibTeX

This model

bibtex

@misc{remy-2025-fmmb-be,
    title={The Fairly Multilingual ModernBERT Embbeding Model -- Belgian Edition},
    author={Francois Remy},
    year={2025},
    eprint={2501.99999},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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",
}

	sent1	sent2
type	string	string
details	min: 6 tokens mean: 17.86 tokens max: 46 tokens	min: 6 tokens mean: 18.87 tokens max: 52 tokens