Add new SentenceTransformer model

218423f verified about 1 year ago

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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:110773
  - loss:ContrastiveLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
  - source_sentence: >-
      average monthly net wage/salary, employees, by province and occupation
      (rupiah), 2018
    sentences:
      - >-
        [Seri 2000] Laju Pertumbuhan PDB Triwulanan Atas Dasar Harga Konstan
        2000 Terhadap Triwulan Sebelumnya, 2001-2014
      - >-
        IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor
        (Supervisor), 2012-2014 (2012=100)
      - >-
        Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut
        Kelompok Umur dan Lapangan Pekerjaan Utama di 9 Sektor (Rupiah), 2017
  - source_sentence: >-
      data belanja dan konsumsi per orang di jambi, 2020: fokus pada makanan dan
      tingkat pengeluaran
    sentences:
      - >-
        Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi
        Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi
        Sulawesi Tenggara, 2018-2023
      - >-
        Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan
        Pendidikan Tertinggi yang Ditamatkan (ribu rupiah), 2017
      - >-
        Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi
        Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa
        Timur, 2018-2023
  - source_sentence: 'ALIRAN DANA RUPIAH: Q1 2008'
    sentences:
      - >-
        Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 dalam Format SNA 1968
        (65x65)
      - >-
        Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut
        Provinsi dan Jenis Pekerjaan Utama, 2024
      - Impor Besi dan Baja Menurut Negara Asal Utama, 2017-2023
  - source_sentence: 'Aliran Wdana Rupiah: Q1 2008'
    sentences:
      - Ekspor Karet Remah Menurut Negara Tujuan Utama, 2012-2023
      - >-
        Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut
        Kelompok Umur dan Lapangan Pekerjaan Utama di 17 Sektor (Rupiah), 2018
      - >-
        Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 dalam Format SNA 1968
        (65x65)
  - source_sentence: 'Aliran dana Rupiah: Q1 2008'
    sentences:
      - Ringkasan Neraca Arus Dana, Triwulan II, 2011*), (Miliar Rupiah)
      - Ringkasan Neraca Arus Dana, 2012 (Miliar Rupiah)
      - >-
        IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor
        (Supervisor), 2012-2014 (2012=100)
datasets:
  - yahyaabd/query-pos-neg-doc-pairs-statictable
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: allstats semantic mini v1 test
          type: allstats-semantic-mini-v1_test
        metrics:
          - type: cosine_accuracy
            value: 0.9678628590683177
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7482147812843323
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9677936769237264
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7444144487380981
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9595714405290031
            name: Cosine Precision
          - type: cosine_recall
            value: 0.976158038147139
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9921512853632306
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.9358669477790009
            name: Cosine Mcc
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: allstats semantic mini v1 dev
          type: allstats-semantic-mini-v1_dev
        metrics:
          - type: cosine_accuracy
            value: 0.9678491772924294
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7902499437332153
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9673587968896863
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7874833345413208
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9616887529731566
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9730960976448341
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9930288231258318
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.9357491510325107
            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 on the query-pos-neg-doc-pairs-statictable 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 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- query-pos-neg-doc-pairs-statictable

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yahyaabd/allstats-search-miniLM-v1-7")
# Run inference
sentences = [
    'Aliran dana Rupiah: Q1 2008',
    'IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 2012-2014 (2012=100)',
    'Ringkasan Neraca Arus Dana, 2012 (Miliar Rupiah)',
]
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

Datasets: allstats-semantic-mini-v1_test and allstats-semantic-mini-v1_dev
Evaluated with BinaryClassificationEvaluator

Metric	allstats-semantic-mini-v1_test	allstats-semantic-mini-v1_dev
cosine_accuracy	0.9679	0.9678
cosine_accuracy_threshold	0.7482	0.7902
cosine_f1	0.9678	0.9674
cosine_f1_threshold	0.7444	0.7875
cosine_precision	0.9596	0.9617
cosine_recall	0.9762	0.9731
cosine_ap	0.9922	0.993
cosine_mcc	0.9359	0.9357

Training Details

Training Dataset

query-pos-neg-doc-pairs-statictable

Dataset: query-pos-neg-doc-pairs-statictable at a31b58d
Size: 110,773 training samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string int
details
min: 9 tokens
mean: 21.22 tokens
max: 50 tokens

min: 6 tokens
mean: 28.24 tokens
max: 50 tokens

0: ~43.90%
1: ~56.10%

	query	doc	label
type	string	string	int
details	min: 9 tokens mean: 21.22 tokens max: 50 tokens	min: 6 tokens mean: 28.24 tokens max: 50 tokens	0: ~43.90% 1: ~56.10%

Samples:

query	doc	label
`Data orang yang naik/turun kapal, di pelabuhan yang dikelola maupun tidak, sekitar 2015`	`Tabel Input-Output Indonesia Transaksi Total Atas Dasar Harga Dasar (185 Produk), 2016 (Juta Rupiah)`	`0`
`data orang yang naik/turun kapal, di pelabuhan yang dikelola maupun tidak, sekitar 2015`	`Tabel Input-Output Indonesia Transaksi Total Atas Dasar Harga Dasar (185 Produk), 2016 (Juta Rupiah)`	`0`
`DATA ORANG YANG NAIK/TURUN KAPAL, DI PELABUHAN YANG DIKELOLA MAUPUN TIDAK, SEKITAR 2015`	`Tabel Input-Output Indonesia Transaksi Total Atas Dasar Harga Dasar (185 Produk), 2016 (Juta Rupiah)`	`0`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.5,
    "size_average": true
}

Evaluation Dataset

query-pos-neg-doc-pairs-statictable

Dataset: query-pos-neg-doc-pairs-statictable at a31b58d
Size: 23,763 evaluation samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string int
details
min: 7 tokens
mean: 20.75 tokens
max: 57 tokens

min: 6 tokens
mean: 27.44 tokens
max: 43 tokens

0: ~50.20%
1: ~49.80%

	query	doc	label
type	string	string	int
details	min: 7 tokens mean: 20.75 tokens max: 57 tokens	min: 6 tokens mean: 27.44 tokens max: 43 tokens	0: ~50.20% 1: ~49.80%

Samples:

query	doc	label
`Cek penghasilan bulanan (gaji bersih) buruh/pegawai, per provinsi dan jenis pekerjaannya, 2019`	`Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama, 2021`	`1`
`cek penghasilan bulanan (gaji bersih) buruh/pegawai, per provinsi dan jenis pekerjaannya, 2019`	`Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama, 2021`	`1`
`CEK PENGHASILAN BULANAN (GAJI BERSIH) BURUH/PEGAWAI, PER PROVINSI DAN JENIS PEKERJAANNYA, 2019`	`Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama, 2021`	`1`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.5,
    "size_average": true
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 1
warmup_ratio: 0.2
fp16: True
load_best_model_at_end: True
eval_on_start: True

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: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.2
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: True
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
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
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: True
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	allstats-semantic-mini-v1_test_cosine_ap	allstats-semantic-mini-v1_dev_cosine_ap
-1	-1	-	-	0.8699	-
0	0	-	0.0489	-	0.8658
0.0578	100	0.0222	0.0101	-	0.9458
0.1155	200	0.0087	0.0073	-	0.9631
0.1733	300	0.007	0.0059	-	0.9710
0.2311	400	0.0056	0.0049	-	0.9828
0.2889	500	0.0045	0.0044	-	0.9837
0.3466	600	0.0042	0.0041	-	0.9862
0.4044	700	0.0038	0.0038	-	0.9888
0.4622	800	0.0037	0.0037	-	0.9890
0.5199	900	0.0029	0.0036	-	0.9889
0.5777	1000	0.0031	0.0034	-	0.9907
0.6355	1100	0.0029	0.0033	-	0.9923
0.6932	1200	0.0025	0.0034	-	0.9922
0.7510	1300	0.0025	0.0033	-	0.9929
0.8088	1400	0.0024	0.0033	-	0.9928
0.8666	1500	0.0022	0.0033	-	0.9926
0.9243	1600	0.0023	0.0033	-	0.9929
0.9821	1700	0.0022	0.0032	-	0.993
-1	-1	-	-	0.9922	-

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.4.0
Transformers: 4.48.1
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}