This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2. 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: 64 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity

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': 64, '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("PrabalAryal/Sentence_Transformer_v0.0.2")
# Run inference
sentences = [
    'Alle galerijverlichting is stuk',
    'De verlichting van alle loopbruggen werkt niet',
    'Toegangsdeur werkt niet',
]
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

Binary Classification

Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9624
cosine_accuracy_threshold	0.7545
cosine_f1	0.9623
cosine_f1_threshold	0.7545
cosine_precision	0.9665
cosine_recall	0.9581
cosine_ap	0.9911
cosine_mcc	0.9249

Training Details

Training Dataset

Unnamed Dataset

Size: 33,054 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 3 tokens
mean: 7.39 tokens
max: 18 tokens
min: 3 tokens
mean: 6.69 tokens
max: 21 tokens
min: 0.0
mean: 0.51
max: 1.0
Samples:
sentence_0 sentence_1 label
Defect Lek 1.0
Dakbedekking Weggewaaid 1.0
Het slot werkt niet, de deur is geblokkeerd deur met slot 0.0
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: 64
per_device_eval_batch_size: 64
num_train_epochs: 8
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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
num_train_epochs: 8
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
fp16: True
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: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	cosine_ap
0.1992	103	-	0.7347
0.3985	206	-	0.8260
0.5977	309	-	0.8678
0.7969	412	-	0.9077
0.9671	500	4.2296	-
0.9961	515	-	0.9347
1.0	517	-	0.9352
1.1954	618	-	0.9494
1.3946	721	-	0.9569
1.5938	824	-	0.9658
1.7930	927	-	0.9688
1.9342	1000	3.4734	-
1.9923	1030	-	0.9718
2.0	1034	-	0.9725
2.1915	1133	-	0.9763
2.3907	1236	-	0.9773
2.5899	1339	-	0.9788
2.7892	1442	-	0.9801
2.9014	1500	3.2631	-
2.9884	1545	-	0.9832
3.0	1551	-	0.9836
3.1876	1648	-	0.9833
3.3868	1751	-	0.9841
3.5861	1854	-	0.9855
3.7853	1957	-	0.9864
3.8685	2000	3.1454	-
3.9845	2060	-	0.9869
4.0	2068	-	0.9868
4.1838	2163	-	0.9877
4.3830	2266	-	0.9878
4.5822	2369	-	0.9883
4.7814	2472	-	0.9893
4.8356	2500	3.0617	-
4.9807	2575	-	0.9898
5.0	2585	-	0.9898
5.1799	2678	-	0.9890
5.3791	2781	-	0.9888
5.5783	2884	-	0.9894
5.7776	2987	-	0.9899
5.8027	3000	3.0113	-
5.9768	3090	-	0.9901
6.0	3102	-	0.9901
6.1760	3193	-	0.9905
6.3752	3296	-	0.9900
6.5745	3399	-	0.9909
6.7698	3500	2.9794	-
6.7737	3502	-	0.9909
6.9729	3605	-	0.9908
7.0	3619	-	0.9908
7.1721	3708	-	0.9908
7.3714	3811	-	0.9910
7.5706	3914	-	0.9911
7.7369	4000	2.9322	-
7.7698	4017	-	0.9911
7.9691	4120	-	0.9911
8.0	4136	-	0.9911

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.53.3
PyTorch: 2.6.0+cu124
Accelerate: 1.9.0
Datasets: 4.4.1
Tokenizers: 0.21.2

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}
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 3 tokens mean: 7.39 tokens max: 18 tokens	min: 3 tokens mean: 6.69 tokens max: 21 tokens	min: 0.0 mean: 0.51 max: 1.0

sentence_0	sentence_1	label
`Defect`	`Lek`	`1.0`
`Dakbedekking`	`Weggewaaid`	`1.0`
`Het slot werkt niet, de deur is geblokkeerd`	`deur met slot`	`0.0`