panalexeu/xlm-roberta-ua-distilled

SentenceTransformer based on FacebookAI/xlm-roberta-base

This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base. 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.

👉 Check out the model on GitHub.

Model Details

Model Description

• —Model Type: Sentence Transformer
• —Base model: FacebookAI/xlm-roberta-base <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity
• —Training Dataset: parallel-sentences-talks, parallel-sentences-wikimatrix, parallel-sentences-tatoeba
• —Language: Ukrainian, English
• —License: MIT

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': 512, '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("panalexeu/xlm-roberta-ua-distilled")
# Run inference
sentences = [
    "You'd better consult the doctor.",
    'Краще проконсультуйся у лікаря.',
    'Їх позначають як Aufklärungsfahrzeug 93 та Aufklärungsfahrzeug 97 відповідно.',
]
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]

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Knowledge Distillation

• —Dataset: mse-en-ua
• —Evaluated with <code>MSEEvaluator</code>

Semantic Similarity

• —Datasets: sts17-en-en, sts17-en-ua and sts17-ua-ua
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code>

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Training Details

Training Dataset

• —Dataset: parallel-sentences-talks, parallel-sentences-wikimatrix, parallel-sentences-tatoeba
• —Size: 523,982 training samples
• —Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | english | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | string | list | | details | <ul><li>min: 5 tokens</li><li>mean: 21.11 tokens</li><li>max: 254 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 23.15 tokens</li><li>max: 293 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
• —Samples: | english | non_english | label | |:----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------| | <code>Her real name is Lydia (リディア, Ridia), but she was mistaken for a boy and called Ricard.</code> | <code>Справжнє ім'я — Лідія, але її помилково сприйняли за хлопчика і назвали Рікард.</code> | <code>[0.15217968821525574, -0.17830222845077515, -0.12677159905433655, 0.22082313895225525, 0.40085524320602417, ...]</code> | | <code>(Applause) So he didn't just learn water.</code> | <code>(Аплодисменти) Він не тільки вивчив слово "вода".</code> | <code>[-0.1058148592710495, -0.08846072107553482, -0.2684604823589325, -0.105219267308712, 0.3050258755683899, ...]</code> | | <code>It is tightly integrated with SAM, the Storage and Archive Manager, and hence is often referred to as SAM-QFS.</code> | <code>Вона тісно інтегрована з SAM (Storage and Archive Manager), тому часто називається SAM-QFS.</code> | <code>[0.03270340710878372, -0.45798248052597046, -0.20090211927890778, 0.006579531356692314, -0.03178019821643829, ...]</code> |
• —Loss: <code>MSELoss</code>

Evaluation Dataset

• —Dataset: parallel-sentences-talks, parallel-sentences-wikimatrix, parallel-sentences-tatoeba
• —Size: 3,838 evaluation samples
• —Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | english | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | string | list | | details | <ul><li>min: 5 tokens</li><li>mean: 15.64 tokens</li><li>max: 143 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.98 tokens</li><li>max: 148 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
• —Samples: | english | non_english | label | |:---------------------------------------------------------|:-----------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------| | <code>I have lost my wallet.</code> | <code>Я загубив гаманець.</code> | <code>[-0.11186987161636353, -0.03419225662946701, -0.31304317712783813, 0.0838347002863884, 0.108644500374794, ...]</code> | | <code>It's a pharmaceutical product.</code> | <code>Це фармацевтичний продукт.</code> | <code>[0.04133488982915878, -0.4182000756263733, -0.30786487460136414, -0.09351564198732376, -0.023946482688188553, ...]</code> | | <code>We've all heard of the Casual Friday thing.</code> | <code>Всі ми чули про «джинсову п’ятницю» (вільна форма одягу).</code> | <code>[-0.10697802156209946, 0.21002227067947388, -0.2513434886932373, -0.3718843460083008, 0.06871984899044037, ...]</code> |
• —Loss: <code>MSELoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_train_batch_size: 16
• —per_device_eval_batch_size: 16
• —gradient_accumulation_steps: 3
• —num_train_epochs: 4
• —warmup_ratio: 0.1

All Hyperparameters

<details><summary>Click to expand</summary>

• —overwrite_output_dir: False
• —do_predict: False
• —eval_strategy: steps
• —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: 3
• —eval_accumulation_steps: None
• —torch_empty_cache_steps: None
• —learning_rate: 5e-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: 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: False
• —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —tp_size: 0
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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
• —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

</details>

Training Logs

Framework Versions

• —Python: 3.11.11
• —Sentence Transformers: 3.4.1
• —Transformers: 4.51.1
• —PyTorch: 2.5.1+cu124
• —Accelerate: 1.3.0
• —Datasets: 3.5.0
• —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",
}

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