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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("cassador/2bs4lr2")
# Run inference
sentences = [
    'Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum.',
    'Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat CRTC.',
    'Pembuat Rooms hanya bisa membuat meeting yang terbuka.',
]
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	0.5893
spearman_cosine	0.5802
pearson_manhattan	0.5822
spearman_manhattan	0.5749
pearson_euclidean	0.5862
spearman_euclidean	0.5765
pearson_dot	0.591
spearman_dot	0.5847
pearson_max	0.591
spearman_max	0.5847

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.2927
spearman_cosine	0.267
pearson_manhattan	0.2598
spearman_manhattan	0.2546
pearson_euclidean	0.2669
spearman_euclidean	0.2563
pearson_dot	0.3098
spearman_dot	0.2953
pearson_max	0.3098
spearman_max	0.2953

Training Details

Training Dataset

afaji/indonli

Dataset: afaji/indonli
Size: 6,915 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: 29.26 tokens
max: 135 tokens
min: 6 tokens
mean: 12.13 tokens
max: 36 tokens
0: ~51.00%
1: ~49.00%

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.`	`0`
`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.`	`1`
`Seperti namanya, paket internet sahur Telkomsel ini ditujukan bagi pengguna yang menginginkan kuota ekstra, untuk menemani momen sahur sepanjang bulan puasa.`	`Paket internet sahur tidak ditujukan untuk saat sahur.`	`0`

Loss: SoftmaxLoss

Evaluation Dataset

afaji/indonli

Dataset: afaji/indonli
Size: 1,556 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: 28.07 tokens
max: 179 tokens
min: 6 tokens
mean: 12.15 tokens
max: 25 tokens
0: ~47.90%
1: ~52.10%

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.`	`0`
`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.`	`0`
`Seorang wanita asal New York mengaku sangat benci air putih.`	`Tidak ada orang dari New York yang membenci air putih.`	`0`

Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
learning_rate: 2e-05
num_train_epochs: 2
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: 4
per_device_eval_batch_size: 4
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: 2
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	-	-	0.1277	-
0.0578	100	0.6725	-	-	-
0.1157	200	0.5884	-	-	-
0.1735	300	0.5397	-	-	-
0.2313	400	0.583	-	-	-
0.2892	500	0.6089	-	-	-
0.3470	600	0.5719	-	-	-
0.4049	700	0.6327	-	-	-
0.4627	800	0.5983	-	-	-
0.5205	900	0.5009	-	-	-
0.5784	1000	0.6115	-	-	-
0.6362	1100	0.5186	-	-	-
0.6940	1200	0.5574	-	-	-
0.7519	1300	0.5939	-	-	-
0.8097	1400	0.5022	-	-	-
0.8676	1500	0.5355	-	-	-
0.9254	1600	0.532	-	-	-
0.9832	1700	0.4894	-	-	-
1.0	1729	-	0.4545	0.5332	-
1.0411	1800	0.4036	-	-	-
1.0989	1900	0.4111	-	-	-
1.1567	2000	0.3725	-	-	-
1.2146	2100	0.4287	-	-	-
1.2724	2200	0.3846	-	-	-
1.3302	2300	0.387	-	-	-
1.3881	2400	0.361	-	-	-
1.4459	2500	0.4419	-	-	-
1.5038	2600	0.3893	-	-	-
1.5616	2700	0.4324	-	-	-
1.6194	2800	0.3965	-	-	-
1.6773	2900	0.4438	-	-	-
1.7351	3000	0.3788	-	-	-
1.7929	3100	0.4741	-	-	-
1.8508	3200	0.27	-	-	-
1.9086	3300	0.4175	-	-	-
1.9665	3400	0.5599	-	-	-
2.0	3458	-	0.5981	0.5802	0.2670

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

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",
}

	premise	hypothesis	label
type	string	string	int
details	min: 12 tokens mean: 29.26 tokens max: 135 tokens	min: 6 tokens mean: 12.13 tokens max: 36 tokens	0: ~51.00% 1: ~49.00%