miswag-category-mapper

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. 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 <!-- at revision 4328cf26390c98c5e3c738b4460a05b95f4911f5 -->
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **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, 'architecture': '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'أطفال | مندالان',
    'Kids | أطفال | مندالان',
    'Kids Footwear | أحذية الأطفال | أحذية الأطفال | Clothes, Shoes & Bags > Shoes > Kids Footwear | ملابس، أحذية وحقائب > أحذية > أحذية الأطفال',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9686, 0.3404],
#         [0.9686, 1.0000, 0.4502],
#         [0.3404, 0.4502, 1.0000]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Information Retrieval

• Dataset: val
• Evaluated with <code>InformationRetrievalEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• Size: 41,454 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, <code>sentence_2</code>, <code>sentence_3</code>, <code>sentence_4</code>, <code>sentence_5</code>, and <code>sentence_6</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	sentence_2	sentence_3	sentence_4	sentence_5	sentence_6
  type	string	string	string	string	string	string	string
  details	<ul><li>min: 5 tokens</li><li>mean: 21.23 tokens</li><li>max: 75 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 56.88 tokens</li><li>max: 99 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 57.2 tokens</li><li>max: 95 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 56.71 tokens</li><li>max: 94 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 55.42 tokens</li><li>max: 111 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 56.14 tokens</li><li>max: 92 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 56.79 tokens</li><li>max: 111 tokens</li></ul>

• Samples:

  sentence_0	sentence_1	sentence_2	sentence_3	sentence_4	sentence_5	sentence_6
  <code>فرش تنظيف اللسان | فرش تنظيف اللسان</code>	<code>Tongue Cleaning Brushes | فرش تنظيف اللسان | فرش تنظيف اللسان | Health & Personal Care > Oral Hygiene > Teeth Cleaning & Care > Tongue Cleaning Brushes | الصحة والعناية الشخصية > العناية بالفم > مستلزمات التنظيف والعناية بالأسنان > فرش تنظيف اللسان</code>	<code>Lip Care | العناية بالشفاه | العناية بالشفاه | Beauty > Skincare > Lip Care | الجمال والعناية > عناية بالبشرة > العناية بالشفاه</code>	<code>Oral Hygiene | العناية بالفم | العناية بالفم | Health & Personal Care > Oral Hygiene | الصحة والعناية الشخصية > العناية بالفم</code>	<code>Lip Liners | تحديد الشفاه | تحديد الشفاه | Beauty > Makeup > Lips Makeup > Lip Liners | الجمال والعناية > مكياج > مكياج الشفاه > تحديد الشفاه</code>	<code>General Oral Care Products | منتجات متنوعة | منتجات متنوعة | Health & Personal Care > Oral Hygiene > Teeth Cleaning & Care > General Oral Care Products | الصحة والعناية الشخصية > العناية بالفم > مستلزمات التنظيف والعناية بالأسنان > منتجات متنوعة</code>	<code>Electric Tooth Brushes | فرش الأسنان الكهربائية | فرش الأسنان الكهربائية | Health & Personal Care > Oral Hygiene > Tooth Brushes > Electric Tooth Brushes | الصحة والعناية الشخصية > العناية بالفم > فرش الأسنان > فرش الأسنان الكهربائية</code>
  <code>بنطلون منسوج بأرجل واسعة وكسرات أمامية Pleated Wide Leg Woven Trousers</code>	<code>Pants | بناطيل | Women Fashion > Pants | ملابس نسائية > بناطيل</code>	<code>Women Jeans | جينز نسائي | Women Fashion > Women Jeans | ملابس نسائية > جينز نسائي</code>	<code>Pants & Skirts | تنورة وبنطلون نسائي | تنورة وبنطلون نسائي | Clothes, Shoes & Bags > Women Clothing > Pants & Skirts | ملابس، أحذية وحقائب > ملابس نسائية > تنورة وبنطلون نسائي</code>	<code>Panties | لباس داخلي | لباس داخلي | Clothes, Shoes & Bags > Women Clothing > Women Underwear > Panties | ملابس، أحذية وحقائب > ملابس نسائية > ملابس داخلية نسائية > لباس داخلي</code>	<code>Casual Pants & Sweatpants | بنطلون قماش | بنطلون قماش | Women Fashion > Pants > Casual Pants & Sweatpants | ملابس نسائية > بناطيل > بنطلون قماش</code>	<code>Night Gown | دشداشة | دشداشة | Women Fashion > Women Home Wear > Night Gown | ملابس نسائية > ملابس بيت نسائية > دشداشة</code>
  <code>صابون جليسرين الاصلي سيت Original Glycerin Soap Set</code>	<code>Facial Cleansers | غسولات وصابون الوجه | غسولات وصابون الوجه | Beauty > Skincare > Facial Skincare > Facial Cleansers | الجمال والعناية > عناية بالبشرة > العناية ببشرة الوجه > غسولات وصابون الوجه</code>	<code>Shower Gel | غسولات الجسم | غسولات الجسم | Beauty > Bathing > Bathing & Showering > Shower Gel | الجمال والعناية > الحمام والإستحمام > الإستحمام > غسولات الجسم</code>	<code>Soap | صابون | صابون | Beauty > Bathing > Bathing & Showering > Soap | الجمال والعناية > الحمام والإستحمام > الإستحمام > صابون</code>	<code>Skincare Tools | أدوات العناية ببشرة الوجه | أدوات العناية ببشرة الوجه | Beauty > Skincare > Facial Skincare > Skincare Tools | الجمال والعناية > عناية بالبشرة > العناية ببشرة الوجه > أدوات العناية ببشرة الوجه</code>	<code>Lip Balm | مرطب | مرطب | Beauty > Skincare > Lip Care > Lip Balm | الجمال والعناية > عناية بالبشرة > العناية بالشفاه > مرطب</code>	<code>Eye Care | العناية بالعين | العناية بالعين | Beauty > Skincare > Eye Care | الجمال والعناية > عناية بالبشرة > العناية بالعين</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 32
• num_train_epochs: 5
• per_device_eval_batch_size: 32
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

• per_device_train_batch_size: 32
• num_train_epochs: 5
• max_steps: -1
• learning_rate: 5e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 1
• average_tokens_across_devices: True
• max_grad_norm: 1
• label_smoothing_factor: 0.0
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: trackio
• eval_strategy: no
• per_device_eval_batch_size: 32
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: False
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• Python: 3.14.3
• Sentence Transformers: 5.3.0
• Transformers: 5.5.0
• PyTorch: 2.11.0+cu130
• Accelerate: 1.13.0
• Datasets: 4.8.4
• Tokenizers: 0.22.2

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",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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