This is a sentence-transformers model finetuned from indobenchmark/indobert-large-p2. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

STSB Test

Model	Spearman Correlation
quarkss/indobert-large-stsb	0.8366
quarkss/indobert-base-stsb	0.8123
sentence-transformers/all-MiniLM-L6-v2	0.5952
indobenchmark/indobert-large-p2	0.5673
sentence-transformers/all-mpnet-base-v2	0.5531
sentence-transformers/stsb-bert-base	0.5349
indobenchmark/indobert-base-p2	0.5309

Model Details

Model Description

Model Type: Sentence Transformer
Base model: indobenchmark/indobert-large-p2
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 tokens
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}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("quarkss/indobert-large-stsb")
# Run inference
sentences = [
    'Seorang pria sedang berjalan dengan seekor kuda.',
    'Seorang pria sedang menuntun seekor kuda dengan tali kekang.',
    'Seorang pria sedang menembakkan pistol.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8692
spearman_cosine	0.8677
pearson_manhattan	0.8592
spearman_manhattan	0.8626
pearson_euclidean	0.8599
spearman_euclidean	0.8633
pearson_dot	0.8441
spearman_dot	0.8392
pearson_max	0.8692
spearman_max	0.8677

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8402
spearman_cosine	0.8366
pearson_manhattan	0.8276
spearman_manhattan	0.8316
pearson_euclidean	0.8278
spearman_euclidean	0.8316
pearson_dot	0.817
spearman_dot	0.8083
pearson_max	0.8402
spearman_max	0.8366

Training Details

Training Dataset

Unnamed Dataset

Size: 5,749 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 9.65 tokens
max: 25 tokens
min: 6 tokens
mean: 9.59 tokens
max: 24 tokens
min: 0.0
mean: 0.54
max: 1.0

Samples:

sentence1	sentence2	score
`Sebuah pesawat sedang lepas landas.`	`Sebuah pesawat terbang sedang lepas landas.`	`1.0`
`Seorang pria sedang memainkan seruling besar.`	`Seorang pria sedang memainkan seruling.`	`0.76`
`Seorang pria sedang mengoleskan keju parut di atas pizza.`	`Seorang pria sedang mengoleskan keju parut di atas pizza yang belum matang.`	`0.76`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
weight_decay: 0.01
num_train_epochs: 5
warmup_ratio: 0.1
fp16: 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: 16
per_device_eval_batch_size: 16
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: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
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
eval_on_start: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	spearman_cosine	spearman_max
0.2778	100	0.0867	-	-
0.5556	200	0.0351	-	-
0.8333	300	0.0303	-	-
1.1111	400	0.0202	-	-
1.3889	500	0.0154	0.8612	-
1.6667	600	0.0136	-	-
1.9444	700	0.0145	-	-
2.2222	800	0.0082	-	-
2.5	900	0.0072	-	-
2.7778	1000	0.0068	0.8660	-
3.0556	1100	0.0065	-	-
3.3333	1200	0.0044	-	-
3.6111	1300	0.0044	-	-
3.8889	1400	0.0045	-	-
4.1667	1500	0.0038	0.8677	-
4.4444	1600	0.0038	-	-
4.7222	1700	0.0035	-	-
5.0	1800	0.0034	-	0.8366

Framework Versions

Python: 3.10.13
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.0.1+cu117
Accelerate: 0.32.1
Datasets: 2.17.0
Tokenizers: 0.19.1

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",
}

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 9.65 tokens max: 25 tokens	min: 6 tokens mean: 9.59 tokens max: 24 tokens	min: 0.0 mean: 0.54 max: 1.0