README.md

metadata

language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:10330
  - loss:MultipleNegativesRankingLoss
base_model: indobenchmark/indobert-base-p2
datasets: []
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: Pura Ulun Danu terletak sekitar 56 kilometer dari Kota Denpasar.
    sentences:
      - Dalam tujuh bulan kehamilan, organ tubuh bayi sudah sempurna.
      - Dokter Adeline menjelaskan aturan-aturan agar diabetisi aman berpuasa.
      - Pura Ulun Danu terletak sekitar satu jam perjalanan dari Kota Denpasar.
  - source_sentence: >-
      Di luar ujung barat laut, taiga dominan, mencakup bagian besar dari
      seluruh Siberia.
    sentences:
      - Banyak keraguan mengenai tanggal kelahiran Gaudapa.
      - Sebagian besar Siberia terletak di ujung barat laut,.
      - Maia menyaksikan balapan tanpa alasan.
  - source_sentence: >-
      Widodo Cahyono Putro adalah seorang pelatih dan pemain sepak bola
      legendaris Indonesia.
    sentences:
      - Ia berjanji untuk jatuh di lubang yang sama.
      - Pemain sepak bola legendaris pasti menjadi pelatih sepak bola.
      - >-
        Nazaruddin menegaskan bahwa mantan Wakil Ketua Komisi II DPR itu
        menerima uang dari proyek e-KTP sebesar $500 ribu.
  - source_sentence: >-
      Salah satunya seorang lelaki yang sedang memakan permen karet yang dengan
      paksa dikeluarkan dari mulutnya.
    sentences:
      - Charles Leclerc gagal menjadi juara dunia F2.
      - Pendukung pembrontakan Cina sudah tidak ada.
      - Lelaki itu bukan salah satunya.
  - source_sentence: >-
      Tumenggung Wirapraja setelah mangkat dimakamkan di Kebon Alas Warudoyong,
      Kecamatan Panumbangan, Kabupaten Ciamis.
    sentences:
      - >-
        Peristiwa Pemberontakan Besar di Minahasa memiliki dampak besar pada
        tentara Sekutu.
      - >-
        Di hari libur ini, Pengunjung semua taman nasional tidak dibebaskan
        biaya.
      - >-
        Tumenggung Wirapraja dikremasi setelah dipastikan mangkat dan abunya
        kemudian dilarungkan ke Pantai Laut Selatan.
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on indobenchmark/indobert-base-p2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: -0.05296221890135024
            name: Pearson Cosine
          - type: spearman_cosine
            value: -0.06107163627723088
            name: Spearman Cosine
          - type: pearson_manhattan
            value: -0.06399377304712585
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: -0.06835801919486152
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: -0.0642574675392147
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: -0.06906447787846218
            name: Spearman Euclidean
          - type: pearson_dot
            value: -0.024528943319169508
            name: Pearson Dot
          - type: spearman_dot
            value: -0.024236369255517205
            name: Spearman Dot
          - type: pearson_max
            value: -0.024528943319169508
            name: Pearson Max
          - type: spearman_max
            value: -0.024236369255517205
            name: Spearman Max

SentenceTransformer based on indobenchmark/indobert-base-p2

This is a sentence-transformers model finetuned from indobenchmark/indobert-base-p2. 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: 75 tokens
• Output Dimensionality: 768 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': 75, '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("Hvare/Athena-indobert-finetuned-indonli")
# Run inference
sentences = [
    'Tumenggung Wirapraja setelah mangkat dimakamkan di Kebon Alas Warudoyong, Kecamatan Panumbangan, Kabupaten Ciamis.',
    'Tumenggung Wirapraja dikremasi setelah dipastikan mangkat dan abunya kemudian dilarungkan ke Pantai Laut Selatan.',
    'Di hari libur ini, Pengunjung semua taman nasional tidak dibebaskan biaya.',
]
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	-0.053
spearman_cosine	-0.0611
pearson_manhattan	-0.064
spearman_manhattan	-0.0684
pearson_euclidean	-0.0643
spearman_euclidean	-0.0691
pearson_dot	-0.0245
spearman_dot	-0.0242
pearson_max	-0.0245
spearman_max	-0.0242

Training Details

Training Dataset

Unnamed Dataset

• Size: 10,330 training samples
• Columns: sentence_0, sentence_1, and label
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	int
  details	min: 11 tokens mean: 29.47 tokens max: 75 tokens	min: 6 tokens mean: 12.25 tokens max: 28 tokens	0: ~35.90% 1: ~32.00% 2: ~32.10%

• Samples:

  sentence_0	sentence_1	label
  "" "Akan ada protes dan hal-hal lain, semua nya sudah direncanakan," "ungkap oposisi kepada El Mundo."	Protes dan hal-hal lain sudah direncanakan.	0
  Tak jarang, bangun kesiangan pun jadi alasan untuk tak berolahraga.	Salah satu alasan tidak berolahraga adalah bangun kesiangan.	0
  Namun, saingannya Prabowo Subianto juga mendeklarasikan kemenangan, membuat orang Indonesia bingung.	Prabowo menerima bahwa Dia kalah.	2

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 1
• multi_dataset_batch_sampler: round_robin

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: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• 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: False
• 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: round_robin

Training Logs

Epoch	Step	Training Loss	sts-dev_spearman_max
0.0991	64	-	-0.0411
0.1981	128	-	-0.0426
0.2972	192	-	-0.0419
0.3963	256	-	-0.0425
0.4954	320	-	-0.0384
0.5944	384	-	-0.0260
0.6935	448	-	-0.0216
0.7740	500	0.0531	-
0.7926	512	-	-0.0243
0.8916	576	-	-0.0241
0.9907	640	-	-0.0242
1.0	646	-	-0.0242

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: 2.19.2
• 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}
}