saikasyap/xlm-roberta-base-multilingual-en-sa

SentenceTransformer based on FacebookAI/xlm-roberta-base

This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base on the en-sa 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: FacebookAI/xlm-roberta-base <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 -->
• —Maximum Sequence Length: 128 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity
• —Training Dataset:
• —en-sa <!-- - Language: Unknown --> <!-- - License: Unknown -->

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("saikasyap/xlm-roberta-base-multilingual-en-sa")
# Run inference
sentences = [
    'Magazines and Periodicals   that are published periodically.',
    'पत्रिकाणां (Magazines) तथा नियतकालिकानां च (Periodicals) ग्राहकत्वस्य निर्वहणार्थम् उपयुज्यन्ते ।',
    '"अस्योपरि नुदामश्चेत्, इदं पेन्-ड्रैव् मध्ये, विद्यमानानि सर्वाणि फैल्स् फोल्डर्स् च दर्शयति ।"',
]
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: en-sa
• —Evaluated with <code>MSEEvaluator</code>

Translation

• —Dataset: en-sa
• —Evaluated with <code>TranslationEvaluator</code>

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Training Details

Training Dataset

en-sa

• —Dataset: en-sa
• —Size: 257,886 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: 12 tokens</li><li>mean: 34.23 tokens</li><li>max: 113 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 49.72 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
• —Samples: | english | non_english | label | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | <code>There was no Mughal tradition of primogeniture, the systematic passing of rule, upon an emperor's death, to his eldest son.<br></code> | <code>चक्रवर्तिनः मृत्योः अनन्तरं तस्य शासनस्य व्यवस्थितरूपेण सङ्क्रमणस्य, मुघलपरम्परायाः ज्येष्ठपुत्राधिकारपद्धतिः नासीत्।<br></code> | <code>[-0.5880301594734192, -0.20026817917823792, 0.372330904006958, -0.9807565808296204, -0.35607191920280457, ...]</code> | | <code>The four sons of Shah Jahan all held governorships during their father's reign.<br></code> | <code>शाह्-जहाँ-नामकस्य चत्वारः पुत्राः, सर्वे पितुः शासनकाले शासकपदम् अधारयन्।<br></code> | <code>[-0.5090229511260986, 0.33517003059387207, 0.27507224678993225, -0.05707915127277374, -0.5126022100448608, ...]</code> | | <code>In this regard he discusses the correlation between social opportunities of education and health and how both of these complement economic and political freedoms as a healthy and well-educated person is better suited to make informed economic decisions and be involved in fruitful political demonstrations etc.<br></code> | <code>अस्मिन् विषये सः शिक्षणस्य स्वास्थ्यस्य च सामाजिकावकाशानाम् अन्योन्य-सम्बन्धस्य, तथा च एतद्द्वयम् अपि आर्थिक-राजनैतिक-स्वातन्त्र्ययोः कथं पूरकं भवतः इति च चर्चां करोति, यतोहि स्वस्था सुशिक्षिता च व्यक्तिः ज्ञानपूर्वम् आर्थिकविषयान् निर्णेतुं तथा फलप्रदेषु राजनैतिकेषु प्रतिपादनादिषु संलग्नः भवितुं च अधिकारी भवति इति।<br></code> | <code>[0.16507332026958466, -0.1722974181175232, 0.02585001103579998, 0.36087149381637573, -0.6401643753051758, ...]</code> |
• —Loss: <code>MSELoss</code>

Evaluation Dataset

en-sa

• —Dataset: en-sa
• —Size: 1,000 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: 4 tokens</li><li>mean: 21.38 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 27.89 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
• —Samples: | english | non_english | label | |:-------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------| | <code>"""So they cast him out of the vineyard, and killed him. What therefore shall the lord of the vineyard do unto them?"""</code> | <code>ततस्ते तं क्षेत्राद् बहि र्निपात्य जघ्नुस्तस्मात् स क्षेत्रपतिस्तान् प्रति किं करिष्यति?</code> | <code>[-0.06878167390823364, -0.5150429606437683, -0.09011576324701309, -0.7458725571632385, 0.050420328974723816, ...]</code> | | <code>Avogadro application window opens.</code> | <code>Avogadro एप्लिकेशन् विण्डो उद्घट्यते ।</code> | <code>[0.9054689407348633, -0.2203768789768219, -0.19827595353126526, 0.23870715498924255, -0.3162331283092499, ...]</code> | | <code>Svangah: One whose limbs are beautiful.</code> | <code>स्वंग:यस्य अङ्गानि सुन्दराणि सन्ति</code> | <code>[0.6443825960159302, 0.4850354492664337, -0.4563218355178833, -0.4771449863910675, 0.6588209867477417, ...]</code> |
• —Loss: <code>MSELoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —learning_rate: 2e-05
• —num_train_epochs: 10
• —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: 8
• —per_device_eval_batch_size: 8
• —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: 10
• —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}
• —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: False
• —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

</details>

Training Logs

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

</details>

Framework Versions

• —Python: 3.10.17
• —Sentence Transformers: 4.1.0
• —Transformers: 4.46.3
• —PyTorch: 2.2.0+cu121
• —Accelerate: 1.1.1
• —Datasets: 2.18.0
• —Tokenizers: 0.20.3

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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