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---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
language:
- hu
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1044013
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Emberek várnak a lámpánál kerékpárral.
sentences:
- Az emberek piros lámpánál haladnak.
- Az emberek a kerékpárjukon vannak.
- Egy fekete kutya úszik a vízben egy teniszlabdával a szájában
- source_sentence: A kutya a vízben van.
sentences:
- Két férfi takarítja a havat a tetőről, az egyik egy emelőben ül, a másik pedig
a tetőn.
- A macska a vízben van, és dühös.
- Egy kutya van a vízben, a szájában egy faág.
- source_sentence: A nő feketét visel.
sentences:
- Egy barna kutya fröcsköl, ahogy úszik a vízben.
- Egy tetoválással rendelkező nő, aki fekete tank tetején néz a földre.
- 'Egy kékbe öltözött nő intenzív arckifejezéssel üti a teniszlabdát. A képen:'
- source_sentence: Az emberek alszanak.
sentences:
- Három ember beszélget egy városi utcán.
- A nő fehéret visel.
- Egy apa és a fia ölelgeti alvás közben.
- source_sentence: Az emberek alszanak.
sentences:
- Egy feketébe öltözött nő cigarettát és bevásárlótáskát tart a kezében, miközben
egy idősebb nő átmegy az utcán.
- 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.
- Egy apa és a fia ölelgeti alvás közben.
model-index:
- name: paraphrase-multilingual-MiniLM-L12-v2-hu
results:
- task:
type: triplet
name: Triplet
dataset:
name: all nli dev
type: all-nli-dev
metrics:
- type: cosine_accuracy
value: 0.9918
name: Cosine Accuracy
- type: dot_accuracy
value: 0.0102
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.99
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.99
name: Euclidean Accuracy
- type: max_accuracy
value: 0.9918
name: Max Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: all nli test
type: all-nli-test
metrics:
- type: cosine_accuracy
value: 0.9937878787878788
name: Cosine Accuracy
- type: dot_accuracy
value: 0.00803030303030303
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.9928787878787879
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.9924242424242424
name: Euclidean Accuracy
- type: max_accuracy
value: 0.9937878787878788
name: Max Accuracy
---
# paraphrase-multilingual-MiniLM-L12-v2-hu
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/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](https://huggingface.co/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](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
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]
```
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## Evaluation
### Metrics
#### Triplet
* Dataset: `all-nli-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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 [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| cosine_accuracy | 0.9938 |
| dot_accuracy | 0.008 |
| manhattan_accuracy | 0.9929 |
| euclidean_accuracy | 0.9924 |
| **max_accuracy** | **0.9938** |
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## 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>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"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>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"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
#### 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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 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`: 1
- `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`: True
- `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`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': 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
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | train loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
|:------:|:----:|:-------------:|:----------:|:------------------------:|:-------------------------:|
| 0 | 0 | - | - | 0.7574 | - |
| 0.0123 | 100 | 2.5472 | - | - | - |
| 0.0245 | 200 | 2.0478 | - | - | - |
| 0.0368 | 300 | 1.8426 | - | - | - |
| 0.0490 | 400 | 1.698 | - | - | - |
| 0.0613 | 500 | 1.5715 | - | - | - |
| 0.0736 | 600 | 1.4616 | - | - | - |
| 0.0858 | 700 | 1.6106 | - | - | - |
| 0.0981 | 800 | 1.5849 | - | - | - |
| 0.1103 | 900 | 1.5374 | - | - | - |
| 0.1226 | 1000 | 1.3653 | - | - | - |
| 0.1349 | 1100 | 1.274 | - | - | - |
| 0.1471 | 1200 | 1.1907 | - | - | - |
| 0.1594 | 1300 | 1.2155 | - | - | - |
| 0.1716 | 1400 | 1.2786 | - | - | - |
| 0.1839 | 1500 | 1.1062 | - | - | - |
| 0.1962 | 1600 | 1.0289 | - | - | - |
| 0.2084 | 1700 | 1.0013 | - | - | - |
| 0.2207 | 1800 | 0.9209 | - | - | - |
| 0.2329 | 1900 | 0.8095 | - | - | - |
| 0.2452 | 2000 | 0.9753 | 0.1916 | 0.9558 | - |
| 0.2574 | 2100 | 0.8728 | - | - | - |
| 0.2697 | 2200 | 0.8343 | - | - | - |
| 0.2820 | 2300 | 0.7203 | - | - | - |
| 0.2942 | 2400 | 0.6901 | - | - | - |
| 0.3065 | 2500 | 0.6606 | - | - | - |
| 0.3187 | 2600 | 0.7205 | - | - | - |
| 0.3310 | 2700 | 0.7479 | - | - | - |
| 0.3433 | 2800 | 0.6677 | - | - | - |
| 0.3555 | 2900 | 1.2531 | - | - | - |
| 0.3678 | 3000 | 1.3619 | - | - | - |
| 0.3800 | 3100 | 1.3923 | - | - | - |
| 0.3923 | 3200 | 1.412 | - | - | - |
| 0.4046 | 3300 | 1.3904 | - | - | - |
| 0.4168 | 3400 | 1.3782 | - | - | - |
| 0.4291 | 3500 | 1.3601 | - | - | - |
| 0.4413 | 3600 | 1.3582 | - | - | - |
| 0.4536 | 3700 | 1.3402 | - | - | - |
| 0.4659 | 3800 | 1.32 | - | - | - |
| 0.4781 | 3900 | 1.3277 | - | - | - |
| 0.4904 | 4000 | 1.3112 | 0.0699 | 0.987 | - |
| 0.5026 | 4100 | 1.2992 | - | - | - |
| 0.5149 | 4200 | 1.3005 | - | - | - |
| 0.5272 | 4300 | 1.2978 | - | - | - |
| 0.5394 | 4400 | 1.272 | - | - | - |
| 0.5517 | 4500 | 1.2864 | - | - | - |
| 0.5639 | 4600 | 1.2519 | - | - | - |
| 0.5762 | 4700 | 1.1924 | - | - | - |
| 0.5885 | 4800 | 1.1778 | - | - | - |
| 0.6007 | 4900 | 1.1801 | - | - | - |
| 0.6130 | 5000 | 1.1666 | - | - | - |
| 0.6252 | 5100 | 1.1682 | - | - | - |
| 0.6375 | 5200 | 1.1518 | - | - | - |
| 0.6497 | 5300 | 1.1606 | - | - | - |
| 0.6620 | 5400 | 1.1534 | - | - | - |
| 0.6743 | 5500 | 1.1473 | - | - | - |
| 0.6865 | 5600 | 1.1596 | - | - | - |
| 0.6988 | 5700 | 1.1536 | - | - | - |
| 0.7110 | 5800 | 1.1517 | - | - | - |
| 0.7233 | 5900 | 1.1517 | - | - | - |
| 0.7356 | 6000 | 1.153 | 0.0359 | 0.9896 | - |
| 0.7478 | 6100 | 1.142 | - | - | - |
| 0.7601 | 6200 | 1.093 | - | - | - |
| 0.7723 | 6300 | 1.1764 | - | - | - |
| 0.7846 | 6400 | 1.1868 | - | - | - |
| 0.7969 | 6500 | 1.0308 | - | - | - |
| 0.8091 | 6600 | 1.0122 | - | - | - |
| 0.8214 | 6700 | 1.0084 | - | - | - |
| 0.8336 | 6800 | 1.0151 | - | - | - |
| 0.8459 | 6900 | 1.0121 | - | - | - |
| 0.8582 | 7000 | 1.0071 | - | - | - |
| 0.8704 | 7100 | 1.1543 | - | - | - |
| 0.8827 | 7200 | 1.1915 | - | - | - |
| 0.8949 | 7300 | 1.2224 | - | - | - |
| 0.9072 | 7400 | 1.1463 | - | - | - |
| 0.9195 | 7500 | 1.0254 | - | - | - |
| 0.9317 | 7600 | 1.2396 | - | - | - |
| 0.9440 | 7700 | 1.1225 | - | - | - |
| 0.9562 | 7800 | 0.7177 | - | - | - |
| 0.9685 | 7900 | 0.0681 | - | - | - |
| 0.9808 | 8000 | 0.0264 | 0.0317 | 0.9918 | - |
| 0.9930 | 8100 | 0.078 | - | - | - |
| 1.0 | 8157 | - | - | - | 0.9938 |
### 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}
}
```
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