cassador/3bs4lr2

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/3bs4lr2")
# 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: IndoNLI-dev
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.0546
spearman_cosine	0.0581
pearson_manhattan	0.0644
spearman_manhattan	0.0662
pearson_euclidean	0.0647
spearman_euclidean	0.0667
pearson_dot	0.0215
spearman_dot	0.014
pearson_max	0.0647
spearman_max	0.0667

Semantic Similarity

• Dataset: IndoNLI-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	-0.0274
spearman_cosine	-0.0333
pearson_manhattan	-0.0466
spearman_manhattan	-0.0318
pearson_euclidean	-0.047
spearman_euclidean	-0.0319
pearson_dot	-0.0142
spearman_dot	-0.0162
pearson_max	-0.0142
spearman_max	-0.0162

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: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

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: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 4
• learning_rate: 2e-05
• 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: 3
• 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	IndoNLI-dev_spearman_cosine	IndoNLI-test_spearman_cosine
0	0	-	-	0.1277	-
0.0578	100	0.0488	-	-	-
0.1157	200	0.0403	-	-	-
0.1735	300	0.0173	-	-	-
0.2313	400	0.0052	-	-	-
0.2892	500	0.0077	-	-	-
0.3470	600	0.0065	-	-	-
0.4049	700	0.0199	-	-	-
0.4627	800	0.0318	-	-	-
0.5205	900	0.019	-	-	-
0.5784	1000	0.0128	-	-	-
0.6362	1100	0.0124	-	-	-
0.6940	1200	0.0224	-	-	-
0.7519	1300	0.0115	-	-	-
0.8097	1400	0.0082	-	-	-
0.8676	1500	0.0132	-	-	-
0.9254	1600	0.0225	-	-	-
0.9832	1700	0.0133	-	-	-
1.0	1729	-	0.0173	0.0465	-
1.0411	1800	0.0056	-	-	-
1.0989	1900	0.0027	-	-	-
1.1567	2000	0.0109	-	-	-
1.2146	2100	0.0021	-	-	-
1.2724	2200	0.0004	-	-	-
1.3302	2300	0.0082	-	-	-
1.3881	2400	0.001	-	-	-
1.4459	2500	0.0009	-	-	-
1.5038	2600	0.0021	-	-	-
1.5616	2700	0.0032	-	-	-
1.6194	2800	0.0061	-	-	-
1.6773	2900	0.0057	-	-	-
1.7351	3000	0.0127	-	-	-
1.7929	3100	0.0018	-	-	-
1.8508	3200	0.0007	-	-	-
1.9086	3300	0.0078	-	-	-
1.9665	3400	0.0017	-	-	-
2.0	3458	-	0.0078	0.0446	-
2.0243	3500	0.0003	-	-	-
2.0821	3600	0.0042	-	-	-
2.1400	3700	0.0005	-	-	-
2.1978	3800	0.0002	-	-	-
2.2556	3900	0.0006	-	-	-
2.3135	4000	0.0003	-	-	-
2.3713	4100	0.0048	-	-	-
2.4291	4200	0.0002	-	-	-
2.4870	4300	0.0043	-	-	-
2.5448	4400	0.0011	-	-	-
2.6027	4500	0.0005	-	-	-
2.6605	4600	0.0009	-	-	-
2.7183	4700	0.0013	-	-	-
2.7762	4800	0.0018	-	-	-
2.8340	4900	0.0004	-	-	-
2.8918	5000	0.0014	-	-	-
2.9497	5100	0.0045	-	-	-
3.0	5187	-	0.0083	0.0581	-0.0333

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.3.0+cu121
• Accelerate: 0.32.1
• Datasets: 2.20.0
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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

@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}
}