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:
  • Language: id

Model Sources

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/2bs8lr2")
# 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

Metric Value
pearson_cosine 0.6027
spearman_cosine 0.5828
pearson_manhattan 0.5866
spearman_manhattan 0.5647
pearson_euclidean 0.593
spearman_euclidean 0.5686
pearson_dot 0.5979
spearman_dot 0.5928
pearson_max 0.6027
spearman_max 0.5928

Semantic Similarity

Metric Value
pearson_cosine 0.2975
spearman_cosine 0.268
pearson_manhattan 0.2519
spearman_manhattan 0.2423
pearson_euclidean 0.262
spearman_euclidean 0.245
pearson_dot 0.3204
spearman_dot 0.3039
pearson_max 0.3204
spearman_max 0.3039

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
  • 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: 8
  • 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
  • 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 -
0.1156 100 0.6407 - - -
0.2312 200 0.5119 - - -
0.3468 300 0.5192 - - -
0.4624 400 0.5104 - - -
0.5780 500 0.5087 - - -
0.6936 600 0.515 - - -
0.8092 700 0.4965 - - -
0.9249 800 0.4771 - - -
1.0 865 - 0.4387 0.5481 -
1.0405 900 0.4122 - - -
1.1561 1000 0.3551 - - -
1.2717 1100 0.3332 - - -
1.3873 1200 0.3297 - - -
1.5029 1300 0.3292 - - -
1.6185 1400 0.3646 - - -
1.7341 1500 0.3375 - - -
1.8497 1600 0.3257 - - -
1.9653 1700 0.385 - - -
2.0 1730 - 0.4650 0.5828 0.2680

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",
}
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