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karsar/
paraphrase-multilingual-MiniLM-L12-hu-v2

Name: paraphrase-multilingual-MiniLM-L12-hu-v2
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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
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.

python

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]

Evaluation

Metrics

Triplet

Dataset: all-nli-dev
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9918
dot_accuracy	0.0102
manhattan_accuracy	0.99
euclidean_accuracy	0.99
max_accuracy	0.9918

Triplet

Dataset: all-nli-test
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9938
dot_accuracy	0.008
manhattan_accuracy	0.9929
euclidean_accuracy	0.9924
max_accuracy	0.9938

Training Details

Training Dataset

train

Dataset: train
Size: 1,044,013 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 7 tokens
mean: 11.73 tokens
max: 56 tokens
min: 6 tokens
mean: 15.24 tokens
max: 47 tokens
min: 7 tokens
mean: 16.07 tokens
max: 53 tokens

Samples:

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

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

train

Dataset: train
Size: 5,000 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 7 tokens
mean: 11.73 tokens
max: 56 tokens
min: 6 tokens
mean: 15.24 tokens
max: 47 tokens
min: 7 tokens
mean: 16.07 tokens
max: 53 tokens

Samples:

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

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

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

bibtex

@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

bibtex

@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}
}

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 11.73 tokens max: 56 tokens	min: 6 tokens mean: 15.24 tokens max: 47 tokens	min: 7 tokens mean: 16.07 tokens max: 53 tokens