metadata

language:
  - id
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:10000
  - loss:SoftmaxLoss
base_model: indobenchmark/indobert-base-p2
datasets:
  - afaji/indonli
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: >-
      Dengan meniupnya, perawat bisa segera mengerti bahwa ia dipanggil dan akan
      segera datang menolong.
    sentences:
      - 38% pemilih tidak mendukung meninggalkan Uni Eropa.
      - Perawat mengerti bahwa ia dipanggil dan akan segera datang menolong.
      - >-
        Dari fakta-fakta tersebut dapat diindikasikan pembakaran gereja
        dilakukan secara sengaja.
  - source_sentence: >-
      Kebudayaan jawa lainnya adalah Sintren, Sintren adalan kesenian
      tradisional masyarakat Jawa, khususnya Pekalongan.
    sentences:
      - >-
        Sintren merupakan kesenian tradisional masyarakat Jawa yang ada sejak
        zaman kerajaan.
      - Klinik ini melarang pasiennya menghisap ganja.
      - Perubahan dunia saat itu dipengaruhi oleh Krisis Suez.
  - source_sentence: >-
      Saat ini, sudah empat wanita yang mengaku dilecehkan. Yang terakhir ialah
      aktris Rose McGowan, dengan tuntutan pemerkosaan.
    sentences:
      - Di Maroko Tenggara tidak pernah ada fosil vertebrata.
      - Tidak ada yang dilecehkan.
      - Ganja tidak boleh diberikan kepada pasien penyakit apapun.
  - source_sentence: >-
      Peperangan di tanah berubah dari lini depan statis Perang Dunia I menjadi
      peningkatan mobilitas dan persenjataan gabungan.
    sentences:
      - Peperangan di tanah awalnya berbentuk lini depan statis Perang Dunia I.
      - Ia berdarah keturunan India.
      - Kesultanan Yogyakarta berasal dari Kerajaan Mataram.
  - source_sentence: >-
      Bahan dasar Dalgona Coffee hanya tiga jenis yaitu bubuk kopi, gula, dan
      air. Banyak resep beredar dengan komposisi dua sendok bubuk kopi, dua
      sendok gula, dan dua sendok air panas.
    sentences:
      - Semua orang di dunia menyukai air putih.
      - Jutting berada di Pengadilan Tinggi Hongkong 5 tahun kemudian.
      - Resep komposisi Dalgona Coffee adalah 2 sendok bubuk kopi.
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on indobenchmark/indobert-base-p2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: -0.4766226820019628
            name: Pearson Cosine
          - type: spearman_cosine
            value: -0.4665046363205431
            name: Spearman Cosine
          - type: pearson_manhattan
            value: -0.46278474137062864
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: -0.46103038796182516
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: -0.4732431317820645
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: -0.4673139200425683
            name: Spearman Euclidean
          - type: pearson_dot
            value: -0.4679129419420587
            name: Pearson Dot
          - type: spearman_dot
            value: -0.4577457216480116
            name: Spearman Dot
          - type: pearson_max
            value: -0.46278474137062864
            name: Pearson Max
          - type: spearman_max
            value: -0.4577457216480116
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: -0.20358655624514646
            name: Pearson Cosine
          - type: spearman_cosine
            value: -0.20098073423584242
            name: Spearman Cosine
          - type: pearson_manhattan
            value: -0.16857445418120778
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: -0.18417229002858432
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: -0.17954736289799147
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: -0.1907831094006202
            name: Spearman Euclidean
          - type: pearson_dot
            value: -0.2158654981443921
            name: Pearson Dot
          - type: spearman_dot
            value: -0.2141585054513143
            name: Spearman Dot
          - type: pearson_max
            value: -0.16857445418120778
            name: Pearson Max
          - type: spearman_max
            value: -0.18417229002858432
            name: Spearman Max

SentenceTransformer based on indobenchmark/indobert-base-p2

This is a sentence-transformers model finetuned from indobenchmark/indobert-base-p2 on the afaji/indonli 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: indobenchmark/indobert-base-p2
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- afaji/indonli
Language: id

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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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("cassador/indobert-base-p2-nli-v1")
# Run inference
sentences = [
    'Bahan dasar Dalgona Coffee hanya tiga jenis yaitu bubuk kopi, gula, dan air. Banyak resep beredar dengan komposisi dua sendok bubuk kopi, dua sendok gula, dan dua sendok air panas.',
    'Resep komposisi Dalgona Coffee adalah 2 sendok bubuk kopi.',
    'Jutting berada di Pengadilan Tinggi Hongkong 5 tahun kemudian.',
]
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

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	-0.4766
spearman_cosine	-0.4665
pearson_manhattan	-0.4628
spearman_manhattan	-0.461
pearson_euclidean	-0.4732
spearman_euclidean	-0.4673
pearson_dot	-0.4679
spearman_dot	-0.4577
pearson_max	-0.4628
spearman_max	-0.4577

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	-0.2036
spearman_cosine	-0.201
pearson_manhattan	-0.1686
spearman_manhattan	-0.1842
pearson_euclidean	-0.1795
spearman_euclidean	-0.1908
pearson_dot	-0.2159
spearman_dot	-0.2142
pearson_max	-0.1686
spearman_max	-0.1842

Training Details

Training Dataset

afaji/indonli

Dataset: afaji/indonli
Size: 10,000 training samples
Columns: premise, hypothesis, and label
Approximate statistics based on the first 1000 samples:
premise hypothesis label
type string string int
details
min: 12 tokens
mean: 29.73 tokens
max: 179 tokens

min: 6 tokens
mean: 11.93 tokens
max: 35 tokens

0: ~31.40%
1: ~34.60%
2: ~34.00%

	premise	hypothesis	label
type	string	string	int
details	min: 12 tokens mean: 29.73 tokens max: 179 tokens	min: 6 tokens mean: 11.93 tokens max: 35 tokens	0: ~31.40% 1: ~34.60% 2: ~34.00%

Samples:

premise	hypothesis	label
`Presiden Joko Widodo (Jokowi) menyampaikan prediksi bahwa wabah virus Corona (COVID-19) di Indonesia akan selesai akhir tahun ini.`	`Prediksi akhir wabah tidak disampaikan Jokowi.`	`2`
`Meski biasanya hanya digunakan di fasilitas kesehatan, saat ini masker dan sarung tangan sekali pakai banyak dipakai di tingkat rumah tangga.`	`Masker sekali pakai banyak dipakai di tingkat rumah tangga.`	`0`
`Data dari Nielsen Music mencatat, "Joanne" telah terjual 201 ribu kopi di akhir minggu ini, seperti dilansir aceshowbiz.com.`	`Nielsen Music mencatat pada akhir minggu ini.`	`1`

Loss: SoftmaxLoss

Evaluation Dataset

afaji/indonli

Dataset: afaji/indonli
Size: 1,000 evaluation samples
Columns: premise, hypothesis, and label
Approximate statistics based on the first 1000 samples:
premise hypothesis label
type string string int
details
min: 9 tokens
mean: 28.09 tokens
max: 179 tokens

min: 6 tokens
mean: 12.01 tokens
max: 24 tokens

0: ~37.00%
1: ~29.20%
2: ~33.80%

	premise	hypothesis	label
type	string	string	int
details	min: 9 tokens mean: 28.09 tokens max: 179 tokens	min: 6 tokens mean: 12.01 tokens max: 24 tokens	0: ~37.00% 1: ~29.20% 2: ~33.80%

Samples:

premise	hypothesis	label
`Manuskrip tersebut berisi tiga catatan yang menceritakan bagaimana peristiwa jatuhnya meteorit serta laporan kematian akibat kejadian tersebut seperti dilansir dari Science Alert, Sabtu (25/4/2020).`	`Manuskrip tersebut tidak mencatat laporan kematian.`	`2`
`Dilansir dari Business Insider, menurut observasi dari Mauna Loa Observatory di Hawaii pada karbon dioksida (CO2) di level mencapai 410 ppm tidak langsung memberikan efek pada pernapasan, karena tubuh manusia juga masih membutuhkan CO2 dalam kadar tertentu.`	`Tidak ada observasi yang pernah dilansir oleh Business Insider.`	`2`
`Perekonomian Jakarta terutama ditunjang oleh sektor perdagangan, jasa, properti, industri kreatif, dan keuangan.`	`Sektor jasa memberi pengaruh lebih besar daripada industri kreatif dalam perekonomian Jakarta.`	`1`

Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
learning_rate: 2e-05
num_train_epochs: 4
warmup_ratio: 0.1
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 2e-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: 4
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: 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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	sts-dev_spearman_cosine	sts-test_spearman_cosine
0	0	-	-	-0.0893	-
0.08	100	1.0851	-	-	-
0.16	200	1.0163	-	-	-
0.24	300	0.9524	-	-	-
0.32	400	0.9257	-	-	-
0.4	500	0.9397	-	-	-
0.48	600	0.9125	-	-	-
0.56	700	0.913	-	-	-
0.64	800	0.8792	-	-	-
0.72	900	0.932	-	-	-
0.8	1000	0.9112	-	-	-
0.88	1100	0.8809	-	-	-
0.96	1200	0.8567	-	-	-
1.0	1250	-	0.8585	-0.4868	-
1.04	1300	0.8482	-	-	-
1.12	1400	0.7235	-	-	-
1.2	1500	0.714	-	-	-
1.28	1600	0.7053	-	-	-
1.3600	1700	0.7205	-	-	-
1.44	1800	0.7203	-	-	-
1.52	1900	0.6957	-	-	-
1.6	2000	0.7271	-	-	-
1.6800	2100	0.7302	-	-	-
1.76	2200	0.7054	-	-	-
1.8400	2300	0.7134	-	-	-
1.92	2400	0.6919	-	-	-
2.0	2500	0.7416	0.8465	-0.4085	-
2.08	2600	0.4955	-	-	-
2.16	2700	0.4484	-	-	-
2.24	2800	0.4413	-	-	-
2.32	2900	0.4567	-	-	-
2.4	3000	0.4889	-	-	-
2.48	3100	0.4284	-	-	-
2.56	3200	0.5041	-	-	-
2.64	3300	0.4755	-	-	-
2.7200	3400	0.4726	-	-	-
2.8	3500	0.4656	-	-	-
2.88	3600	0.4389	-	-	-
2.96	3700	0.4789	-	-	-
3.0	3750	-	1.0011	-0.4586	-
3.04	3800	0.3492	-	-	-
3.12	3900	0.2477	-	-	-
3.2	4000	0.2556	-	-	-
3.2800	4100	0.2531	-	-	-
3.36	4200	0.2767	-	-	-
3.44	4300	0.2665	-	-	-
3.52	4400	0.2493	-	-	-
3.6	4500	0.2757	-	-	-
3.68	4600	0.2662	-	-	-
3.76	4700	0.2666	-	-	-
3.84	4800	0.2748	-	-	-
3.92	4900	0.246	-	-	-
4.0	5000	0.2411	1.2455	-0.4665	-0.2010

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Accelerate: 0.31.0
Datasets: 2.20.0
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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",
}