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karsar/
paraphrase-multilingual-MiniLM-L12-hu_v1

Name: paraphrase-multilingual-MiniLM-L12-hu_v1
Availability: InStock

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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:857856
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-hu-v1 results:
- task: type: triplet name: Triplet dataset: name: all nli dev type: all-nli-dev metrics:
  - type: cosine_accuracy value: 0.992 name: Cosine Accuracy
  - type: dot_accuracy value: 0.0108 name: Dot Accuracy
  - type: manhattan_accuracy value: 0.9908 name: Manhattan Accuracy
  - type: euclidean_accuracy value: 0.9908 name: Euclidean Accuracy
  - type: max_accuracy value: 0.992 name: Max Accuracy
- task: type: triplet name: Triplet dataset: name: all nli test type: all-nli-test metrics:
  - type: cosine_accuracy value: 0.9913636363636363 name: Cosine Accuracy
  - type: dot_accuracy value: 0.013939393939393939 name: Dot Accuracy
  - type: manhattan_accuracy value: 0.990909090909091 name: Manhattan Accuracy
  - type: euclidean_accuracy value: 0.9910606060606061 name: Euclidean Accuracy
  - type: max_accuracy value: 0.9913636363636363 name: Max Accuracy

paraphrase-multilingual-MiniLM-L12-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_v1")
# 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	0.992
dot_accuracy	0.0108
manhattan_accuracy	0.9908
euclidean_accuracy	0.9908
max_accuracy	0.992

Triplet

Dataset: all-nli-test
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9914
dot_accuracy	0.0139
manhattan_accuracy	0.9909
euclidean_accuracy	0.9911
max_accuracy	0.9914

Training Details

Training Dataset

train

Dataset: train
Size: 857,856 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

All Hyperparameters

Click to expand

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

Training Logs

Epoch	Step	Training Loss	train loss	all-nli-dev_max_accuracy	all-nli-test_max_accuracy
0	0	-	-	0.7574	-
0.0149	100	2.5002	-	-	-
0.0298	200	1.9984	-	-	-
0.0448	300	1.8094	-	-	-
0.0597	400	1.6704	-	-	-
0.0746	500	1.5518	-	-	-
0.0895	600	1.449	-	-	-
0.1044	700	1.5998	-	-	-
0.1194	800	1.5725	-	-	-
0.1343	900	1.5341	-	-	-
0.1492	1000	1.3423	-	-	-
0.1641	1100	1.2485	-	-	-
0.1791	1200	1.1527	-	-	-
0.1940	1300	1.1672	-	-	-
0.2089	1400	1.2426	-	-	-
0.2238	1500	1.0948	-	-	-
0.2387	1600	1.0069	-	-	-
0.2537	1700	0.976	-	-	-
0.2686	1800	0.897	-	-	-
0.2835	1900	0.7825	-	-	-
0.2984	2000	0.9421	0.1899	0.9568	-
0.3133	2100	0.8651	-	-	-
0.3283	2200	0.8184	-	-	-
0.3432	2300	0.699	-	-	-
0.3581	2400	0.6704	-	-	-
0.3730	2500	0.6477	-	-	-
0.3879	2600	0.7077	-	-	-
0.4029	2700	0.7364	-	-	-
0.4178	2800	0.665	-	-	-
0.4327	2900	1.2512	-	-	-
0.4476	3000	1.3693	-	-	-
0.4625	3100	1.3959	-	-	-
0.4775	3200	1.4175	-	-	-
0.4924	3300	1.402	-	-	-
0.5073	3400	1.3832	-	-	-
0.5222	3500	1.3671	-	-	-
0.5372	3600	1.3666	-	-	-
0.5521	3700	1.3479	-	-	-
0.5670	3800	1.3272	-	-	-
0.5819	3900	1.3353	-	-	-
0.5968	4000	1.3177	0.0639	0.9902	-
0.6118	4100	1.3068	-	-	-
0.6267	4200	1.3054	-	-	-
0.6416	4300	1.3098	-	-	-
0.6565	4400	1.2839	-	-	-
0.6714	4500	1.2976	-	-	-
0.6864	4600	1.2669	-	-	-
0.7013	4700	1.208	-	-	-
0.7162	4800	1.194	-	-	-
0.7311	4900	1.1974	-	-	-
0.7460	5000	1.1834	-	-	-
0.7610	5100	1.1876	-	-	-
0.7759	5200	1.1743	-	-	-
0.7908	5300	1.1839	-	-	-
0.8057	5400	1.1778	-	-	-
0.8207	5500	1.1711	-	-	-
0.8356	5600	1.1809	-	-	-
0.8505	5700	1.1825	-	-	-
0.8654	5800	1.1795	-	-	-
0.8803	5900	1.1788	-	-	-
0.8953	6000	1.1819	0.0371	0.992	-
0.9102	6100	1.1741	-	-	-
0.9251	6200	1.1871	-	-	-
0.9400	6300	0.498	-	-	-
0.9549	6400	0.093	-	-	-
0.9699	6500	0.1597	-	-	-
0.9848	6600	0.2033	-	-	-
0.9997	6700	0.16	-	-	-
1.0	6702	-	-	-	0.9914

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}
}

	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