This is a sentence-transformers model finetuned from indobenchmark/indobert-base-p2. 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 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': 512, 'do_lower_case': False, 'architecture': '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yosriku/Indobert-Base-p2-Trash-Medium-EXP1")
# Run inference
sentences = [
    'ini, katakan: “Halo,”nya. 4?',
    'Pasal 5 Cukup jelas. Pasal 6 Huruf a Cukup jelas. Huruf b',
    '(4) Setiap orang berhak untuk berperan dalam perlindungan dan pengelolaan lingkungan hidup sesuai dengan peraturan perundang-undangan.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3595, 0.2728],
#         [0.3595, 1.0000, 0.2936],
#         [0.2728, 0.2936, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 4,516 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 8 tokens
mean: 20.04 tokens
max: 80 tokens
min: 6 tokens
mean: 35.53 tokens
max: 117 tokens
min: 8 tokens
mean: 35.65 tokens
max: 117 tokens

Samples:

anchor	positive	negative
`Apa tujuan utama yang ingin dicapai melalui riset penggunaan teknologi gasifikasi di Pantai Parangtritis?`	`Penelitian ini bertujuan untuk mengetahui besarnya potensi energi listrik yang dihasilkan dari sampah organik d i Kawasan Wisata Pantai Parangtritis menggunakan proses gasifikasi`	`bahwa dalam pengelolaan sampah diperlukan kepastian hukum, kejelasan tanggung jawab dan kewenangan Pemerintah, pemerintahan daerah, serta peran masyarakat dan dunia usaha sehingga pengelolaan sampah dapat berjalan secara proporsional, efektif, dan`
`Jelaskan transparansi pemerintah terkait permohonan dan keputusan izin lingkungan kepada publik.`	`Pasal 39 (1) Menteri, gubernur, atau bupati/walikota sesuai dengan kewenangannya wajib mengumumkan setiap permohonan dan keputusan izin lingkungan`	`. bahwa untuk p enanganan sampah laut diperlukan komi tmen b. bahwa akibat pencemaran sampah plastik d i laut, telah ditemuk an k andung an plastik berukuran mikro dan nano pada biota dan surnb er daya laut di perairan Indonesia;`
`menjadi ini: Kalau kita ingin mendapatkan pengelolaan fisik sampah laut, maka ubah kalimat itu. Sekarang! mereka-sama? lagi sampah tersebut dibuang jadi`	`pengelolaan sampah yang bersumber dari darat; c. penanggulangan sampah di pesisir dan laut; d. mekanisme pendanaan, penguatan kelembagaan, pengawasan, dan penegakan hukum;`	`Pasal 118 Terhadap tindak pidana sebagaimana dimaksud dalam Pasal 116 ayat (1) huruf a, sanksi pidana dijatuhkan kepada badan usaha yang diwakili oleh pengurus yang berwenang mewakili di dalam dan di luar pengadilan sesuai dengan peraturan`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
learning_rate: 2e-05
num_train_epochs: 1
fp16: True
push_to_hub: True
hub_model_id: yosriku/Indobert-Base-p2-Trash-Medium-EXP1
hub_strategy: end
hub_private_repo: False

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 32
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
torch_empty_cache_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: 1
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
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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: True
resume_from_checkpoint: None
hub_model_id: yosriku/Indobert-Base-p2-Trash-Medium-EXP1
hub_strategy: end
hub_private_repo: False
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: no
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: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.0704	10	2.957
0.1408	20	2.6161
0.2113	30	2.2598
0.2817	40	2.4133
0.3521	50	2.1384
0.4225	60	2.1435
0.4930	70	2.2673
0.5634	80	2.1651
0.6338	90	2.3644
0.7042	100	2.2321
0.7746	110	2.0634
0.8451	120	2.4208
0.9155	130	2.2214
0.9859	140	2.3133

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.9.0+cu126
Accelerate: 1.12.0
Datasets: 4.0.0
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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}
}

	anchor	positive	negative
type	string	string	string
details	min: 8 tokens mean: 20.04 tokens max: 80 tokens	min: 6 tokens mean: 35.53 tokens max: 117 tokens	min: 8 tokens mean: 35.65 tokens max: 117 tokens