metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:123640
  - loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
  - source_sentence: data perempuan dan laki-laki di indonesia 2022
    sentences:
      - Statistik Telekomunikasi Indonesia 2012
      - Perkembangan Indeks Produksi Triwulanan Industri Mikro dan Kecil 2023
      - Pada Agustus 2014, Jumlah wisman mencapai 826,8 ribu
  - source_sentence: hasil survei kebutuhan data 2011 di indonesia
    sentences:
      - Analisis Survei Kebutuhan Data 2011
      - Produk Domestik Bruto Indonesia Triwulanan 2007-2011
      - Direktori Perusahaan Air Bersih, Listrik, dan Gas 2022
  - source_sentence: komoditas apa yang produksinya naik 3,24 persen pada tahun 2013?
    sentences:
      - Indikator Ekonomi Juni 2017
      - Produksi jagung naik pada tahun 2013.
      - Statistik Keuangan Pemerintah Desa 2018
  - source_sentence: buku-buku statistik tahun 2007
    sentences:
      - >-
        Statistik Keuangan Badan Usaha Milik Negara dan Badan Usaha Milik Daerah
        2019
      - Statistik Harga Konsumen Perdesaan Kelompok Makanan 2011
      - Buletin Statistik Perdagangan Luar Negeri Impor Mei 2019
  - source_sentence: analisis kinerja ekspor indonesia feb 2014
    sentences:
      - Kajian Big Data Sinyal Pemulihan Indonesia dari Pandemi Covid-19
      - Laporan Bulanan Data Sosial Ekonomi Januari 2019
      - >-
        Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok
        Komoditi dan Negara Februari 2014
datasets:
  - yahyaabd/allstats-semantic-synthetic-dataset-v1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: >-
      SentenceTransformer based on
      sentence-transformers/paraphrase-multilingual-mpnet-base-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: allstats semantic base v1 eval
          type: allstats-semantic-base-v1-eval
        metrics:
          - type: pearson_cosine
            value: 0.9866451272402678
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.9032950863870964
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: allstat semantic base v1 test
          type: allstat-semantic-base-v1-test
        metrics:
          - type: pearson_cosine
            value: 0.9876833290128094
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.9063327700749637
            name: Spearman Cosine

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 on the allstats-semantic-synthetic-dataset-v1 dataset. 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: 128 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- allstats-semantic-synthetic-dataset-v1

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: 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("yahyaabd/allstats-semantic-base-v1")
# Run inference
sentences = [
    'analisis kinerja ekspor indonesia feb 2014',
    'Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara Februari 2014',
    'Laporan Bulanan Data Sosial Ekonomi Januari 2019',
]
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

Semantic Similarity

Datasets: allstats-semantic-base-v1-eval and allstat-semantic-base-v1-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	allstats-semantic-base-v1-eval	allstat-semantic-base-v1-test
pearson_cosine	0.9866	0.9877
spearman_cosine	0.9033	0.9063

Training Details

Training Dataset

allstats-semantic-synthetic-dataset-v1

Dataset: allstats-semantic-synthetic-dataset-v1 at d59a245
Size: 123,640 training samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string float
details
min: 6 tokens
mean: 10.64 tokens
max: 34 tokens

min: 4 tokens
mean: 14.06 tokens
max: 76 tokens

min: 0.0
mean: 0.49
max: 1.0

	query	doc	label
type	string	string	float
details	min: 6 tokens mean: 10.64 tokens max: 34 tokens	min: 4 tokens mean: 14.06 tokens max: 76 tokens	min: 0.0 mean: 0.49 max: 1.0

Samples:

query	doc	label
`Gambaran umum karakteristik usaha di Indonesia`	`Statistik Karakteristik Usaha 2022/2023`	`0.9`
`Tabel data jumlah sekolah, guru, dan murid MA di bawah Kementerian Agama per provinsi.`	`Jumlah Sekolah, Guru, dan Murid Madrasah Aliyah (MA) di Bawah Kementerian Agama Menurut Provinsi, tahun ajaran 2005/2006-2015/2016`	`0.96`
`bagaimana kinerja sektor konstruksi indonesia di triwulan ketiga tahun 2008?`	`Statistik Restoran/Rumah Makan 2007`	`0.09`

Loss: CosineSimilarityLoss with these parameters:

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

Evaluation Dataset

allstats-semantic-synthetic-dataset-v1

Dataset: allstats-semantic-synthetic-dataset-v1 at d59a245
Size: 26,494 evaluation samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string float
details
min: 5 tokens
mean: 10.48 tokens
max: 34 tokens

min: 5 tokens
mean: 13.86 tokens
max: 58 tokens

min: 0.0
mean: 0.49
max: 1.0

	query	doc	label
type	string	string	float
details	min: 5 tokens mean: 10.48 tokens max: 34 tokens	min: 5 tokens mean: 13.86 tokens max: 58 tokens	min: 0.0 mean: 0.49 max: 1.0

Samples:

query	doc	label
`Harga barang konsumsi Indonesia 2022: data per kota`	`Harga Konsumen Beberapa Barang Kelompok Makanan, Minuman, dan Tembakau 90 Kota di Indonesia 2022`	`0.92`
`data biaya hidup bali 2018`	`Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara, Maret 2018`	`0.1`
`ekspor barang indonesia november 2011: data lengkap`	`Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara Februari 2013`	`0.12`

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: 32
per_device_eval_batch_size: 32
num_train_epochs: 10
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: 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: 32
per_device_eval_batch_size: 32
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: 10
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: 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: False
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-base-v1-eval_spearman_cosine	allstat-semantic-base-v1-test_spearman_cosine
0.1294	500	0.0454	0.0267	0.7374	-
0.2588	1000	0.0243	0.0205	0.7527	-
0.3882	1500	0.0199	0.0169	0.7720	-
0.5176	2000	0.0186	0.0164	0.7733	-
0.6470	2500	0.0179	0.0158	0.7806	-
0.7764	3000	0.0158	0.0155	0.7826	-
0.9058	3500	0.0159	0.0155	0.7771	-
1.0352	4000	0.0155	0.0143	0.7847	-
1.1646	4500	0.0133	0.0141	0.7935	-
1.2940	5000	0.0128	0.0132	0.7986	-
1.4234	5500	0.0121	0.0120	0.8148	-
1.5528	6000	0.012	0.0118	0.8030	-
1.6822	6500	0.0118	0.0121	0.8132	-
1.8116	7000	0.0119	0.0109	0.8130	-
1.9410	7500	0.0107	0.0108	0.8132	-
2.0704	8000	0.009	0.0098	0.8181	-
2.1998	8500	0.0082	0.0099	0.8221	-
2.3292	9000	0.008	0.0100	0.8221	-
2.4586	9500	0.008	0.0095	0.8302	-
2.5880	10000	0.0083	0.0090	0.8284	-
2.7174	10500	0.0084	0.0093	0.8261	-
2.8468	11000	0.0084	0.0089	0.8283	-
2.9762	11500	0.0083	0.0093	0.8259	-
3.1056	12000	0.0056	0.0083	0.8362	-
3.2350	12500	0.006	0.0081	0.8357	-
3.3644	13000	0.0057	0.0078	0.8381	-
3.4938	13500	0.006	0.0081	0.8399	-
3.6232	14000	0.0058	0.0078	0.8420	-
3.7526	14500	0.0068	0.0078	0.8303	-
3.8820	15000	0.0056	0.0072	0.8502	-
4.0114	15500	0.0054	0.0073	0.8483	-
4.1408	16000	0.004	0.0068	0.8565	-
4.2702	16500	0.0042	0.0069	0.8493	-
4.3996	17000	0.0043	0.0069	0.8507	-
4.5290	17500	0.0045	0.0069	0.8536	-
4.6584	18000	0.0042	0.0064	0.8602	-
4.7878	18500	0.0043	0.0065	0.8537	-
4.9172	19000	0.0039	0.0062	0.8623	-
5.0466	19500	0.0041	0.0065	0.8601	-
5.1760	20000	0.0032	0.0060	0.8643	-
5.3054	20500	0.0032	0.0064	0.8657	-
5.4348	21000	0.0032	0.0062	0.8669	-
5.5642	21500	0.0031	0.0065	0.8633	-
5.6936	22000	0.003	0.0059	0.8682	-
5.8230	22500	0.0032	0.0057	0.8713	-
5.9524	23000	0.0032	0.0057	0.8688	-
6.0818	23500	0.0026	0.0055	0.8772	-
6.2112	24000	0.0023	0.0056	0.8708	-
6.3406	24500	0.0029	0.0056	0.8734	-
6.4700	25000	0.0027	0.0054	0.8748	-
6.5994	25500	0.0022	0.0054	0.8827	-
6.7288	26000	0.0021	0.0053	0.8823	-
6.8582	26500	0.0021	0.0053	0.8832	-
6.9876	27000	0.0025	0.0052	0.8839	-
7.1170	27500	0.002	0.0051	0.8887	-
7.2464	28000	0.0017	0.0050	0.8869	-
7.3758	28500	0.0019	0.0052	0.8845	-
7.5052	29000	0.0017	0.0051	0.8897	-
7.6346	29500	0.0017	0.0051	0.8920	-
7.7640	30000	0.0018	0.0050	0.8889	-
7.8934	30500	0.0019	0.0050	0.8931	-
8.0228	31000	0.002	0.0049	0.8889	-
8.1522	31500	0.0014	0.0049	0.8912	-
8.2816	32000	0.0013	0.0049	0.8922	-
8.4110	32500	0.0014	0.0049	0.8947	-
8.5404	33000	0.0014	0.0049	0.8960	-
8.6698	33500	0.0014	0.0049	0.8972	-
8.7992	34000	0.0014	0.0048	0.8982	-
8.9286	34500	0.0013	0.0048	0.9003	-
9.0580	35000	0.0014	0.0048	0.9001	-
9.1874	35500	0.0012	0.0048	0.8995	-
9.3168	36000	0.0011	0.0048	0.9008	-
9.4462	36500	0.001	0.0047	0.9015	-
9.5756	37000	0.0011	0.0047	0.9026	-
9.7050	37500	0.0011	0.0047	0.9027	-
9.8344	38000	0.001	0.0047	0.9035	-
9.9638	38500	0.0011	0.0047	0.9033	-
10.0	38640	-	-	-	0.9063

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.5.1+cu124
Accelerate: 1.2.1
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",
}