aloizidis/make-multilingual-en-lb-checkpoint-5000

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2 on the lb-en 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: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • lb-en

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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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

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("aloizidis/make-multilingual-en-lb-2025-02-28_01-09-55")
# Run inference
sentences = [
    'The cross-border workers should also receive more wages.',
    "D'grenzarbechetr missten och me' lo'n kre'en.",
    "De Néckel: Firun! Dât ass jo ailes, wèll 't get dach neischt un der Bréck gemâcht!",
]
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

Knowledge Distillation

• Dataset: lb-en
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-0.4761

Translation

• Dataset: lb-en
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.9861
trg2src_accuracy	0.9861
mean_accuracy	0.9861

Training Details

Training Dataset

lb-en

• Dataset: lb-en
• Size: 40,000 training samples
• Columns: english, non_english, and label
• Approximate statistics based on the first 1000 samples:

  	english	non_english	label
  type	string	string	list
  details	min: 4 tokens mean: 25.32 tokens max: 128 tokens	min: 5 tokens mean: 36.91 tokens max: 128 tokens	size: 768 elements

• Samples:

  english	non_english	label
  A lesson for the next year	Eng le’er fir dat anert joer	[0.08891881257295609, 0.20895496010780334, -0.10672671347856522, -0.03302554786205292, 0.049002278596162796, ...]
  On Easter, the Maquisards' northern section organizes their big spring ball in Willy Pintsch's hall at the station.	Op O'schteren organisieren d'Maquisard'eiii section Nord, hire gro'sse fre'joersbal am sali Willy Pintsch op der gare.	[-0.08668982982635498, -0.06969941407442093, -0.0036096556577831507, 0.1605304628610611, -0.041704729199409485, ...]
  The happiness, the peace is long gone now,	V ergângen ass nu läng dat gléck, de' fréd,	[0.07229219377040863, 0.3288629353046417, -0.012548360042273998, 0.06720984727144241, -0.02617395855486393, ...]

• Loss: MSELoss

Evaluation Dataset

lb-en

• Dataset: lb-en
• Size: 504 evaluation samples
• Columns: english, non_english, and label
• Approximate statistics based on the first 504 samples:

  	english	non_english	label
  type	string	string	list
  details	min: 5 tokens mean: 25.23 tokens max: 85 tokens	min: 5 tokens mean: 36.48 tokens max: 128 tokens	size: 768 elements

• Samples:

  english	non_english	label
  But he was not the instigator of the mass murders of the Jews, his lawyer explained, and he bore no more responsibility than the others.	Mé hié wir net den ustêfter vun de massemuerden un de judden, erklärt sein affekot, an hicn hätt net me' verantwortong ze droen we' de' aner.	[0.021159790456295013, 0.11144042760133743, 0.00869293138384819, 0.004551620222628117, -0.09236127883195877, ...]
  The Romanian automotive industry * For the first time in its history, Romania has started car production.	D’rumänesch autoindustrie * Fir d'c'schte ke'er an senger geschieht huet Rumänien d'fabrikalio'n vun'den autoen opgeholl.	[-0.16835248470306396, 0.14826826751232147, 0.01772368885576725, -0.027855699881911278, 0.04770198464393616, ...]
  The drugs were confiscated along with the dealer's car, mobile phones and cash.	D'Drogen, den Auto, d'Boergeld an d'Handye si saiséiert ginn.	[-0.05122023820877075, 0.01204440463334322, -0.025424882769584656, 0.1286350041627884, 0.034633491188287735, ...]

• Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• learning_rate: 2e-05
• num_train_epochs: 5
• 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: 32
• per_device_eval_batch_size: 32
• 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: 5
• 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

Training Logs

Epoch	Step	Training Loss	lb-en loss	lb-en_negative_mse	lb-en_mean_accuracy
0.08	100	0.0056	0.0048	-0.7796	0.7887
0.16	200	0.0051	0.0046	-0.7330	0.8373
0.24	300	0.0049	0.0044	-0.6992	0.8740
0.32	400	0.0047	0.0043	-0.6763	0.8889
0.4	500	0.0046	0.0042	-0.6584	0.8988
0.48	600	0.0045	0.0041	-0.6377	0.9067
0.56	700	0.0044	0.0040	-0.6209	0.9206
0.64	800	0.0043	0.0040	-0.6087	0.9266
0.72	900	0.0043	0.0039	-0.5984	0.9395
0.8	1000	0.0042	0.0038	-0.5887	0.9385
0.88	1100	0.0042	0.0038	-0.5799	0.9425
0.96	1200	0.0041	0.0038	-0.5725	0.9474
1.04	1300	0.004	0.0037	-0.5690	0.9524
1.12	1400	0.0039	0.0037	-0.5602	0.9554
1.2	1500	0.0038	0.0037	-0.5545	0.9603
1.28	1600	0.0038	0.0036	-0.5501	0.9673
1.3600	1700	0.0038	0.0036	-0.5459	0.9643
1.44	1800	0.0037	0.0036	-0.5411	0.9702
1.52	1900	0.0038	0.0036	-0.5360	0.9722
1.6	2000	0.0037	0.0035	-0.5326	0.9683
1.6800	2100	0.0037	0.0035	-0.5310	0.9732
1.76	2200	0.0036	0.0035	-0.5264	0.9752
1.8400	2300	0.0037	0.0035	-0.5224	0.9792
1.92	2400	0.0036	0.0035	-0.5205	0.9792
2.0	2500	0.0036	0.0034	-0.5166	0.9782
2.08	2600	0.0033	0.0034	-0.5137	0.9782
2.16	2700	0.0034	0.0034	-0.5121	0.9812
2.24	2800	0.0033	0.0034	-0.5093	0.9802
2.32	2900	0.0034	0.0034	-0.5063	0.9821
2.4	3000	0.0034	0.0034	-0.5051	0.9802
2.48	3100	0.0034	0.0034	-0.5030	0.9812
2.56	3200	0.0033	0.0033	-0.5002	0.9851
2.64	3300	0.0034	0.0033	-0.4962	0.9831
2.7200	3400	0.0034	0.0033	-0.4936	0.9831
2.8	3500	0.0033	0.0033	-0.4916	0.9841
2.88	3600	0.0033	0.0033	-0.4892	0.9841
2.96	3700	0.0033	0.0033	-0.4871	0.9841
3.04	3800	0.0032	0.0033	-0.4863	0.9861
3.12	3900	0.0031	0.0033	-0.4864	0.9841
3.2	4000	0.0031	0.0033	-0.4859	0.9841
3.2800	4100	0.0031	0.0033	-0.4848	0.9871
3.36	4200	0.0031	0.0033	-0.4838	0.9881
3.44	4300	0.0031	0.0032	-0.4837	0.9861
3.52	4400	0.0031	0.0032	-0.4817	0.9851
3.6	4500	0.0031	0.0032	-0.4812	0.9841
3.68	4600	0.0031	0.0032	-0.4792	0.9861
3.76	4700	0.0031	0.0032	-0.4793	0.9851
3.84	4800	0.0031	0.0032	-0.4779	0.9871
3.92	4900	0.0031	0.0032	-0.4771	0.9861
4.0	5000	0.0031	0.0032	-0.4761	0.9861

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.49.0
• PyTorch: 2.6.0
• Accelerate: 1.4.0
• Datasets: 3.3.2
• 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",
}

MSELoss

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}