Add new SentenceTransformer model

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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets:
  - yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:70280
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: Data SBH tahun 2012 di Mamuju
    sentences:
      - >-
        Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Harmonized
        System November 2013
      - SBH 2012 - Mamuju
      - IHK di 66 Kota di Indonesia 2013
  - source_sentence: Statistik konstruksi tahun 2020
    sentences:
      - Indeks Ketimpangan Gender 2022
      - >-
        Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Menurut Provinsi,
        1971-2020
      - >-
        Perkembangan Beberapa Indikator Utama sosial-Ekonomi Indonesia Edisi
        Februari 2016
  - source_sentence: Berapa besar inflasi pada bulan Oktober 2008?
    sentences:
      - >-
        Tinjauan Ekonomi Regional Indonesia Berdasarkan Data PDRB 2004-2008 Buku
        2
      - Statistik Sumber Daya Laut dan Pesisir 2020
      - Inflasi September 2008 sebesar 0,97 persen.
  - source_sentence: 'Sektor konstruksi Indonesia: data statistik 1990-2013'
    sentences:
      - >-
        Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut
        Provinsi dan Lapangan Pekerjaan Utama, 2023
      - Direktori Perusahaan Kehutanan 2019
      - Sensus Ekonomi 2006 Hasil Pendaftaran Perusahaan Sumatera Selatan
  - source_sentence: Perdagangan luar negeri, impor, Oktober 2020
    sentences:
      - Indikator Ekonomi September 2005
      - Statistik Potensi Desa Provinsi DI Yogyakarta 2005
      - Indikator Ekonomi November 1999
model-index:
  - name: >-
      SentenceTransformer based on
      sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: allstats semantic search mini v2 eval
          type: allstats-semantic-search-mini-v2-eval
        metrics:
          - type: pearson_cosine
            value: 0.9617082550278393
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8518022238549516
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: allstat semantic search mini v2 test
          type: allstat-semantic-search-mini-v2-test
        metrics:
          - type: pearson_cosine
            value: 0.9604638064122318
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8480797444308495
            name: Spearman Cosine

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 allstats-semantic-search-synthetic-dataset-v2-mini 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:
- allstats-semantic-search-synthetic-dataset-v2-mini

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-semantic-search-mini-model-v2-2")
# Run inference
sentences = [
    'Perdagangan luar negeri, impor, Oktober 2020',
    'Indikator Ekonomi November 1999',
    'Indikator Ekonomi September 2005',
]
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

Semantic Similarity

Datasets: allstats-semantic-search-mini-v2-eval and allstat-semantic-search-mini-v2-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	allstats-semantic-search-mini-v2-eval	allstat-semantic-search-mini-v2-test
pearson_cosine	0.9617	0.9605
spearman_cosine	0.8518	0.8481

Training Details

Training Dataset

allstats-semantic-search-synthetic-dataset-v2-mini

Dataset: allstats-semantic-search-synthetic-dataset-v2-mini at 8222b01
Size: 70,280 training samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string float
details
min: 3 tokens
mean: 10.92 tokens
max: 50 tokens

min: 4 tokens
mean: 14.68 tokens
max: 59 tokens

min: 0.0
mean: 0.52
max: 1.0

	query	doc	label
type	string	string	float
details	min: 3 tokens mean: 10.92 tokens max: 50 tokens	min: 4 tokens mean: 14.68 tokens max: 59 tokens	min: 0.0 mean: 0.52 max: 1.0

Samples:

query	doc	label
`Statistik perusahaan pembudidaya tanaman kehutanan 2018`	`Statistik Perusahaan Pembudidaya Tanaman Kehutanan 2018`	`0.97`
`Berapa persen pertumbuhan PDB Indonesia pada Triwulan III Tahun 2002?`	`Inflasi Bulan November 2002 Sebesar 1,85 %`	`0.0`
`Perdagangan luar negeri Indonesia, impor 2019, jilid 2`	`Pendataan Sapi Potong Sapi Perah (PSPK 2011) Sulawesi Barat`	`0.06`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Evaluation Dataset

allstats-semantic-search-synthetic-dataset-v2-mini

Dataset: allstats-semantic-search-synthetic-dataset-v2-mini at 8222b01
Size: 15,060 evaluation samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string float
details
min: 4 tokens
mean: 10.96 tokens
max: 48 tokens

min: 4 tokens
mean: 14.74 tokens
max: 70 tokens

min: 0.0
mean: 0.5
max: 1.0

	query	doc	label
type	string	string	float
details	min: 4 tokens mean: 10.96 tokens max: 48 tokens	min: 4 tokens mean: 14.74 tokens max: 70 tokens	min: 0.0 mean: 0.5 max: 1.0

Samples:

query	doc	label
`Review PDRB daerah di Pulau Sumatera 2010-2013`	`Statistik Pendidikan 2006`	`0.12`
`Analisis data angkatan kerja Agustus 2021`	`Booklet Survei Angkatan Kerja Nasional Agustus 2021`	`0.9`
`Berapa persen inflasi yang terjadi pada Juli 2015?`	`Inflasi pada bulan lain tidak disebutkan`	`0.0`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 24
warmup_ratio: 0.1
bf16: 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: 24
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
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	allstats-semantic-search-mini-v2-eval_spearman_cosine	allstat-semantic-search-mini-v2-test_spearman_cosine
0.4550	500	0.0643	0.0413	0.6996	-
0.9099	1000	0.0348	0.0280	0.7533	-
1.3649	1500	0.0254	0.0238	0.7737	-
1.8198	2000	0.0223	0.0205	0.7831	-
2.2748	2500	0.0181	0.0197	0.7894	-
2.7298	3000	0.0173	0.0184	0.7876	-
3.1847	3500	0.0152	0.0170	0.7954	-
3.6397	4000	0.0123	0.0175	0.7970	-
4.0946	4500	0.0125	0.0163	0.8118	-
4.5496	5000	0.01	0.0161	0.8047	-
5.0045	5500	0.0103	0.0157	0.8126	-
5.4595	6000	0.0079	0.0150	0.8224	-
5.9145	6500	0.0087	0.0156	0.8219	-
6.3694	7000	0.0071	0.0152	0.8145	-
6.8244	7500	0.0068	0.0153	0.8172	-
7.2793	8000	0.0061	0.0147	0.8216	-
7.7343	8500	0.0062	0.0146	0.8267	-
8.1893	9000	0.0055	0.0145	0.8325	-
8.6442	9500	0.005	0.0146	0.8335	-
9.0992	10000	0.0052	0.0143	0.8356	-
9.5541	10500	0.0043	0.0144	0.8313	-
10.0091	11000	0.0051	0.0144	0.8362	-
10.4641	11500	0.004	0.0145	0.8376	-
10.9190	12000	0.0039	0.0142	0.8331	-
11.3740	12500	0.0034	0.0141	0.8397	-
11.8289	13000	0.0033	0.0140	0.8398	-
12.2839	13500	0.0032	0.0143	0.8411	-
12.7389	14000	0.003	0.0141	0.8407	-
13.1938	14500	0.0031	0.0141	0.8379	-
13.6488	15000	0.0026	0.0141	0.8419	-
14.1037	15500	0.0028	0.0141	0.8442	-
14.5587	16000	0.0023	0.0138	0.8455	-
15.0136	16500	0.0025	0.0147	0.8359	-
15.4686	17000	0.0021	0.0141	0.8459	-
15.9236	17500	0.0023	0.0140	0.8433	-
16.3785	18000	0.002	0.0139	0.8465	-
16.8335	18500	0.002	0.0139	0.8461	-
17.2884	19000	0.0018	0.0139	0.8482	-
17.7434	19500	0.0018	0.0138	0.8477	-
18.1984	20000	0.0017	0.0138	0.8503	-
18.6533	20500	0.0016	0.0136	0.8493	-
19.1083	21000	0.0016	0.0139	0.8501	-
19.5632	21500	0.0015	0.0138	0.8478	-
20.0182	22000	0.0015	0.0139	0.8501	-
20.4732	22500	0.0013	0.0139	0.8508	-
20.9281	23000	0.0015	0.0139	0.8511	-
21.3831	23500	0.0013	0.0139	0.8517	-
21.8380	24000	0.0013	0.0139	0.8512	-
22.2930	24500	0.0012	0.0139	0.8512	-
22.7480	25000	0.0012	0.0138	0.8520	-
23.2029	25500	0.0012	0.0139	0.8520	-
23.6579	26000	0.0011	0.0139	0.8518	-
24.0	26376	-	-	-	0.8481

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.44.2
PyTorch: 2.4.1+cu121
Accelerate: 0.34.2
Datasets: 3.2.0
Tokenizers: 0.19.1

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",
}