cassador/4bs4lr2

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/4bs4lr2")
# 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	0.6086
spearman_cosine	0.5957
pearson_manhattan	0.5923
spearman_manhattan	0.5878
pearson_euclidean	0.6025
spearman_euclidean	0.5922
pearson_dot	0.6143
spearman_dot	0.6071
pearson_max	0.6143
spearman_max	0.6071

Semantic Similarity

• Dataset: sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.3358
spearman_cosine	0.3037
pearson_manhattan	0.2926
spearman_manhattan	0.2893
pearson_euclidean	0.3078
spearman_euclidean	0.2929
pearson_dot	0.3501
spearman_dot	0.3337
pearson_max	0.3501
spearman_max	0.3337

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: 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: 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: 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	-	-	0.1277	-
0.0578	100	0.706	-	-	-
0.1157	200	0.6251	-	-	-
0.1735	300	0.509	-	-	-
0.2313	400	0.5822	-	-	-
0.2892	500	0.6089	-	-	-
0.3470	600	0.5497	-	-	-
0.4049	700	0.6176	-	-	-
0.4627	800	0.584	-	-	-
0.5205	900	0.5317	-	-	-
0.5784	1000	0.6706	-	-	-
0.6362	1100	0.5508	-	-	-
0.6940	1200	0.569	-	-	-
0.7519	1300	0.6095	-	-	-
0.8097	1400	0.5107	-	-	-
0.8676	1500	0.5799	-	-	-
0.9254	1600	0.5481	-	-	-
0.9832	1700	0.4749	-	-	-
1.0	1729	-	0.4679	0.5346	-
1.0411	1800	0.4321	-	-	-
1.0989	1900	0.4594	-	-	-
1.1567	2000	0.4428	-	-	-
1.2146	2100	0.479	-	-	-
1.2724	2200	0.3944	-	-	-
1.3302	2300	0.434	-	-	-
1.3881	2400	0.3981	-	-	-
1.4459	2500	0.5058	-	-	-
1.5038	2600	0.4254	-	-	-
1.5616	2700	0.5089	-	-	-
1.6194	2800	0.4669	-	-	-
1.6773	2900	0.5093	-	-	-
1.7351	3000	0.4673	-	-	-
1.7929	3100	0.4964	-	-	-
1.8508	3200	0.366	-	-	-
1.9086	3300	0.5168	-	-	-
1.9665	3400	0.4976	-	-	-
2.0	3458	-	0.4956	0.5756	-
2.0243	3500	0.4112	-	-	-
2.0821	3600	0.3139	-	-	-
2.1400	3700	0.2579	-	-	-
2.1978	3800	0.3207	-	-	-
2.2556	3900	0.2962	-	-	-
2.3135	4000	0.3924	-	-	-
2.3713	4100	0.3059	-	-	-
2.4291	4200	0.2762	-	-	-
2.4870	4300	0.3425	-	-	-
2.5448	4400	0.3165	-	-	-
2.6027	4500	0.2786	-	-	-
2.6605	4600	0.3183	-	-	-
2.7183	4700	0.4492	-	-	-
2.7762	4800	0.2414	-	-	-
2.8340	4900	0.3064	-	-	-
2.8918	5000	0.3164	-	-	-
2.9497	5100	0.2612	-	-	-
3.0	5187	-	0.8414	0.6116	-
3.0075	5200	0.318	-	-	-
3.0654	5300	0.201	-	-	-
3.1232	5400	0.1045	-	-	-
3.1810	5500	0.1038	-	-	-
3.2389	5600	0.1365	-	-	-
3.2967	5700	0.1279	-	-	-
3.3545	5800	0.2304	-	-	-
3.4124	5900	0.1515	-	-	-
3.4702	6000	0.1682	-	-	-
3.5281	6100	0.2008	-	-	-
3.5859	6200	0.1955	-	-	-
3.6437	6300	0.103	-	-	-
3.7016	6400	0.1482	-	-	-
3.7594	6500	0.1093	-	-	-
3.8172	6600	0.1478	-	-	-
3.8751	6700	0.1708	-	-	-
3.9329	6800	0.2399	-	-	-
3.9907	6900	0.1805	-	-	-
4.0	6916	-	1.0672	0.5957	0.3037

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",
}