README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:68828
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
  - source_sentence: Men is de toegangssleutels verloren
    sentences:
      - De centrale verwarming
      - niet dringend
      - Weg
  - source_sentence: De bovenste constructie
    sentences:
      - Voldoende warm water in de hele woning
      - daklekkage
      - lek in kraan
  - source_sentence: De box in het souterrain
    sentences:
      - Vloer
      - lift niet
      - Nood uitgang
  - source_sentence: balkon
    sentences:
      - de brievenbus
      - uitgang garage dicht
      - afvoer de douche
  - source_sentence: De deur naar de kelderboxen is stuk
    sentences:
      - deur met dranger
      - De beugel om de plek vrij te houden
      - kelderboxen deur
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - cosine_mcc
model-index:
  - name: >-
      SentenceTransformer based on
      sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy
            value: 0.982086820083682
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.733125627040863
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9821498371335505
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.733125627040863
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9787068293949623
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9856171548117155
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9972864020390366
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.964197674565882
            name: Cosine Mcc

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("PrabalAryal/Sentence_Transformer_v0.0.4")
# Run inference
sentences = [
    'De deur naar de kelderboxen is stuk',
    'kelderboxen deur',
    'deur met dranger',
]
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	0.9821
cosine_accuracy_threshold	0.7331
cosine_f1	0.9821
cosine_f1_threshold	0.7331
cosine_precision	0.9787
cosine_recall	0.9856
cosine_ap	0.9973
cosine_mcc	0.9642

Training Details

Training Dataset

Unnamed Dataset

• Size: 68,828 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: 7.03 tokens max: 21 tokens	min: 3 tokens mean: 6.41 tokens max: 18 tokens	min: 0.0 mean: 0.49 max: 1.0

• Samples:

  sentence_0	sentence_1	label
  De sluiting van de toegangspoort is stuk	slot defect	1.0
  Woning	trapafgang	0.0
  De sleutels zijn kwijt	Nie	0.0

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• num_train_epochs: 10
• 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: 10
• 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
0.1998	215	-	0.7638
0.3996	430	-	0.8723
0.4647	500	4.4585	-
0.5994	645	-	0.9176
0.7993	860	-	0.9475
0.9294	1000	3.6015	-
0.9991	1075	-	0.9595
1.0	1076	-	0.9593
1.1989	1290	-	0.9705
1.3941	1500	3.3729	-
1.3987	1505	-	0.9793
1.5985	1720	-	0.9818
1.7983	1935	-	0.9854
1.8587	2000	3.2631	-
1.9981	2150	-	0.9866
2.0	2152	-	0.9866
2.1980	2365	-	0.9890
2.3234	2500	3.1295	-
2.3978	2580	-	0.9884
2.5976	2795	-	0.9916
2.7881	3000	3.0907	-
2.7974	3010	-	0.9916
2.9972	3225	-	0.9922
3.0	3228	-	0.9922
3.1970	3440	-	0.9928
3.2528	3500	3.0105	-
3.3968	3655	-	0.9932
3.5967	3870	-	0.9937
3.7175	4000	2.977	-
3.7965	4085	-	0.9939
3.9963	4300	-	0.9944
4.0	4304	-	0.9945
4.1822	4500	2.9488	-
4.1961	4515	-	0.9947
4.3959	4730	-	0.9950
4.5957	4945	-	0.9952
4.6468	5000	2.914	-
4.7955	5160	-	0.9954
4.9954	5375	-	0.9956
5.0	5380	-	0.9956
5.1115	5500	2.8927	-
5.1952	5590	-	0.9960
5.3950	5805	-	0.9959
5.5762	6000	2.8505	-
5.5948	6020	-	0.9963
5.7946	6235	-	0.9961
5.9944	6450	-	0.9962
6.0	6456	-	0.9962
6.0409	6500	2.8462	-
6.1942	6665	-	0.9963
6.3941	6880	-	0.9965
6.5056	7000	2.8024	-
6.5939	7095	-	0.9967
6.7937	7310	-	0.9969
6.9703	7500	2.8184	-
6.9935	7525	-	0.9968
7.0	7532	-	0.9967
7.1933	7740	-	0.9967
7.3931	7955	-	0.9967
7.4349	8000	2.7761	-
7.5929	8170	-	0.9968
7.7928	8385	-	0.9969
7.8996	8500	2.7736	-
7.9926	8600	-	0.9970
8.0	8608	-	0.9971
8.1924	8815	-	0.9972
8.3643	9000	2.7627	-
8.3922	9030	-	0.9970
8.5920	9245	-	0.9972
8.7918	9460	-	0.9972
8.8290	9500	2.7604	-
8.9916	9675	-	0.9972
9.0	9684	-	0.9972
9.1914	9890	-	0.9971
9.2937	10000	2.7467	-
9.3913	10105	-	0.9972
9.5911	10320	-	0.9973
9.7584	10500	2.7441	-
9.7909	10535	-	0.9973

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

@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

@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}
}