sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:457856 loss:MultipleNegativesRankingLoss hu arxiv:1908.10084 arxiv:1705.00652 base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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:457856
- 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: e5-base_hun
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli dev
      type: all-nli-dev
    metrics:
    - type: cosine_accuracy
      value: 0.9746
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.0284
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9676
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9658
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9746
      name: Max Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli test
      type: all-nli-test
    metrics:
    - type: cosine_accuracy
      value: 0.9921212121212121
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.008636363636363636
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9896969696969697
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9895454545454545
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9921212121212121
      name: Max Accuracy
---

# e5-base_hun

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")
# 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    | 0.9746     |
| dot_accuracy       | 0.0284     |
| manhattan_accuracy | 0.9676     |
| euclidean_accuracy | 0.9658     |
| **max_accuracy**   | **0.9746** |

#### 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    | 0.9921     |
| dot_accuracy       | 0.0086     |
| manhattan_accuracy | 0.9897     |
| euclidean_accuracy | 0.9895     |
| **max_accuracy**   | **0.9921** |

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## Training Details

### Training Dataset

#### train

* Dataset: train
* Size: 457,856 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                   | -                         |
| 0.0280 | 100  | 2.3495        | -          | -                        | -                         |
| 0.0559 | 200  | 1.8588        | -          | -                        | -                         |
| 0.0839 | 300  | 1.7156        | -          | -                        | -                         |
| 0.1118 | 400  | 1.609         | -          | -                        | -                         |
| 0.1398 | 500  | 1.5286        | -          | -                        | -                         |
| 0.1677 | 600  | 1.4425        | -          | -                        | -                         |
| 0.1957 | 700  | 1.6016        | -          | -                        | -                         |
| 0.2237 | 800  | 1.5278        | -          | -                        | -                         |
| 0.2516 | 900  | 1.4255        | -          | -                        | -                         |
| 0.2796 | 1000 | 1.2084        | -          | -                        | -                         |
| 0.3075 | 1100 | 1.1248        | -          | -                        | -                         |
| 0.3355 | 1200 | 1.0773        | -          | -                        | -                         |
| 0.3634 | 1300 | 1.1373        | -          | -                        | -                         |
| 0.3914 | 1400 | 1.222         | -          | -                        | -                         |
| 0.4193 | 1500 | 1.048         | -          | -                        | -                         |
| 0.4473 | 1600 | 0.9319        | -          | -                        | -                         |
| 0.4753 | 1700 | 0.8837        | -          | -                        | -                         |
| 0.5032 | 1800 | 0.8402        | -          | -                        | -                         |
| 0.5312 | 1900 | 0.7515        | -          | -                        | -                         |
| 0.5591 | 2000 | 0.9405        | 0.1310     | 0.9746                   | -                         |
| 0.5871 | 2100 | 0.8526        | -          | -                        | -                         |
| 0.6150 | 2200 | 0.7886        | -          | -                        | -                         |
| 0.6430 | 2300 | 0.6704        | -          | -                        | -                         |
| 0.6710 | 2400 | 0.6488        | -          | -                        | -                         |
| 0.6989 | 2500 | 0.635         | -          | -                        | -                         |
| 0.7269 | 2600 | 0.7242        | -          | -                        | -                         |
| 0.7548 | 2700 | 0.7593        | -          | -                        | -                         |
| 0.7828 | 2800 | 0.62          | -          | -                        | -                         |
| 0.8107 | 2900 | 0.4302        | -          | -                        | -                         |
| 0.8387 | 3000 | 0.2952        | -          | -                        | -                         |
| 0.8666 | 3100 | 0.3354        | -          | -                        | -                         |
| 0.8946 | 3200 | 0.3221        | -          | -                        | -                         |
| 0.9226 | 3300 | 0.4317        | -          | -                        | -                         |
| 0.9505 | 3400 | 0.3185        | -          | -                        | -                         |
| 0.9785 | 3500 | 0.433         | -          | -                        | -                         |
| 1.0    | 3577 | -             | -          | -                        | 0.9921                    |


### 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: 2.18.0
- 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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