paraphrase-multilingual-MiniLM-L12-hu-v2

paraphrase-multilingual-MiniLM-L12-hu-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the train dataset. It maps sentences & paragraphs to a 384-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-MiniLM-L12-v2 <!-- at revision ae06c001a2546bef168b9bf8f570ccb1a16aaa27 -->
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 tokens
• Similarity Function: Cosine Similarity
• Training Dataset:
  • train
• Language: hu
• License: apache-2.0

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: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("karsar/paraphrase-multilingual-MiniLM-L12-hu-v2")
# Run inference
sentences = [
    'Az emberek alszanak.',
    'Egy apa és a fia ölelgeti alvás közben.',
    'Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős elmosódás tesz kivehetetlenné.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

<!-- ### 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

Triplet

• Dataset: all-nli-dev
• Evaluated with <code>TripletEvaluator</code>

Triplet

• Dataset: all-nli-test
• Evaluated with <code>TripletEvaluator</code>

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

train

• Dataset: train
• Size: 1,044,013 training samples
• Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 7 tokens</li><li>mean: 11.73 tokens</li><li>max: 56 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 15.24 tokens</li><li>max: 47 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 16.07 tokens</li><li>max: 53 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett.</code>	<code>Egy ember a szabadban, lóháton.</code>	<code>Egy ember egy étteremben van, és omlettet rendel.</code>
  <code>Gyerekek mosolyogva és integetett a kamera</code>	<code>Gyermekek vannak jelen</code>	<code>A gyerekek homlokot rántanak</code>
  <code>Egy fiú ugrál a gördeszkát a közepén egy piros híd.</code>	<code>A fiú gördeszkás trükköt csinál.</code>	<code>A fiú korcsolyázik a járdán.</code>

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

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

train

• Dataset: train
• Size: 5,000 evaluation samples
• Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 7 tokens</li><li>mean: 11.73 tokens</li><li>max: 56 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 15.24 tokens</li><li>max: 47 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 16.07 tokens</li><li>max: 53 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett.</code>	<code>Egy ember a szabadban, lóháton.</code>	<code>Egy ember egy étteremben van, és omlettet rendel.</code>
  <code>Gyerekek mosolyogva és integetett a kamera</code>	<code>Gyermekek vannak jelen</code>	<code>A gyerekek homlokot rántanak</code>
  <code>Egy fiú ugrál a gördeszkát a közepén egy piros híd.</code>	<code>A fiú gördeszkás trükköt csinál.</code>	<code>A fiú korcsolyázik a járdán.</code>

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

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• num_train_epochs: 1
• warmup_ratio: 0.1
• bf16: True
• batch_sampler: no_duplicates

Framework Versions

• Python: 3.11.8
• Sentence Transformers: 3.1.1
• Transformers: 4.44.0
• PyTorch: 2.3.0.post101
• Accelerate: 0.33.0
• Datasets: 3.0.2
• Tokenizers: 0.19.0

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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

<!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->