Sentence_Transformer_v0.0.1 / README.md

PrabalAryal

Add new SentenceTransformer model

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:8884
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
  - source_sentence: De deur tussen twee kamers
    sentences:
      - Verschillende buren hebben hetzelfde probleem
      - Alle lampen in de gemeenschappelijke ruimtes
      - De scheidingsdeur
  - source_sentence: De individuele CV
    sentences:
      - Er komt geen water uit de kraan
      - De centrale waterkraan
      - Mijn eigen CV-installatie
  - source_sentence: De vloer- of wandtegels zitten niet vast
    sentences:
      - Het privé-buitenverblijf
      - Er zijn tegels losgekomen
      - Een auto staat in de weg om weg te rijden
  - source_sentence: Barst in het glas
    sentences:
      - De hele VvE
      - Vaststaan door een foutgeparkeerde auto
      - Er is goedkeuring
  - source_sentence: De sproeier van de douche
    sentences:
      - De deur naar buiten
      - Warmwatertankje in de keuken
      - De douchesproeier is kapot
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-mpnet-base-v2
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy
            value: 0.9908906882591093
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7341352105140686
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9909547738693467
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7341352105140686
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9840319361277445
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9979757085020243
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9955570949668978
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.9818799573285504
            name: Cosine Mcc

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2. It maps sentences & paragraphs to a 768-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-mpnet-base-v2
Maximum Sequence Length: 64 tokens
Output Dimensionality: 768 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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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_v0.0.1")
# Run inference
sentences = [
    'De sproeier van de douche',
    'De douchesproeier is kapot',
    'De deur naar buiten',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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	0.9909
cosine_accuracy_threshold	0.7341
cosine_f1	0.991
cosine_f1_threshold	0.7341
cosine_precision	0.984
cosine_recall	0.998
cosine_ap	0.9956
cosine_mcc	0.9819

Training Details

Training Dataset

Unnamed Dataset

Size: 8,884 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: 8.6 tokens
max: 18 tokens

min: 4 tokens
mean: 8.86 tokens
max: 21 tokens

min: 0.0
mean: 0.51
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 3 tokens mean: 8.6 tokens max: 18 tokens	min: 4 tokens mean: 8.86 tokens max: 21 tokens	min: 0.0 mean: 0.51 max: 1.0

Samples:

sentence_0	sentence_1	label
`Het slot is kapot`	`Schade aan de sluiting`	`1.0`
`Ik kan er niet uit met de auto`	`De uitrit is versperd`	`1.0`
`De afvoer van de wasmachine is stuk`	`Lekkende kranen of leidingen`	`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: 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
0.1942	27	-	0.8916
0.3885	54	-	0.9339
0.5827	81	-	0.9614
0.7770	108	-	0.9740
0.9712	135	-	0.9706
1.0	139	-	0.9732
1.1655	162	-	0.9763
1.3597	189	-	0.9831
1.5540	216	-	0.9845
1.7482	243	-	0.9858
1.9424	270	-	0.9886
2.0	278	-	0.9896
2.1367	297	-	0.9904
2.3309	324	-	0.9900
2.5252	351	-	0.9907
2.7194	378	-	0.9921
2.9137	405	-	0.9919
3.0	417	-	0.9917
3.1079	432	-	0.9933
3.3022	459	-	0.9923
3.4964	486	-	0.9911
3.5971	500	3.1664	-
3.6906	513	-	0.9936
3.8849	540	-	0.9926
4.0	556	-	0.9928
4.0791	567	-	0.9931
4.2734	594	-	0.9949
4.4676	621	-	0.9940
4.6619	648	-	0.9930
4.8561	675	-	0.9932
5.0	695	-	0.9935
5.0504	702	-	0.9938
5.2446	729	-	0.9950
5.4388	756	-	0.9949
5.6331	783	-	0.9948
5.8273	810	-	0.9948
6.0	834	-	0.9946
6.0216	837	-	0.9945
6.2158	864	-	0.9955
6.4101	891	-	0.9955
6.6043	918	-	0.9955
6.7986	945	-	0.9956

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}
}