README.md

metadata

base_model: indobenchmark/indobert-base-p2
datasets:
  - quarkss/stsb-indo-mt
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:5749
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: Dua ekor anjing berenang di kolam renang.
    sentences:
      - Anjing-anjing sedang berenang di kolam renang.
      - Seekor binatang sedang berjalan di atas tanah.
      - Seorang pria sedang menyeka pinggiran mangkuk.
  - source_sentence: Seorang anak perempuan sedang mengiris mentega menjadi dua bagian.
    sentences:
      - Seorang wanita sedang mengiris tahu.
      - Dua orang berkelahi.
      - Seorang pria sedang menari.
  - source_sentence: Seorang gadis sedang makan kue mangkuk.
    sentences:
      - Seorang pria sedang mengiris bawang putih dengan alat pengiris mandolin.
      - Seorang pria sedang memotong dan memotong bawang.
      - Seorang wanita sedang makan kue mangkuk.
  - source_sentence: Sebuah helikopter mendarat di landasan helikopter.
    sentences:
      - Seorang pria sedang mengiris mentimun.
      - Seorang pria sedang memotong batang pohon dengan kapak.
      - Sebuah helikopter mendarat.
  - source_sentence: Seorang pria sedang berjalan dengan seekor kuda.
    sentences:
      - Seorang pria sedang menuntun seekor kuda dengan tali kekang.
      - Seorang pria sedang menembakkan pistol.
      - Seorang wanita sedang memetik tomat.
model-index:
  - name: SentenceTransformer based on indobenchmark/indobert-base-p2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: 0.8577280779646681
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8588776334781149
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8315261521874587
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8355406849443783
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8318083198603524
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8359194889385243
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7767060276322824
            name: Pearson Dot
          - type: spearman_dot
            value: 0.783607744137448
            name: Spearman Dot
          - type: pearson_max
            value: 0.8577280779646681
            name: Pearson Max
          - type: spearman_max
            value: 0.8588776334781149
            name: Spearman Max
          - type: pearson_cosine
            value: 0.8122790124383042
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8123119892530147
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7987643661729152
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7966661480553803
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7992882233155829
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.797227936168015
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.712195542080357
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7014898656834544
            name: Spearman Dot
          - type: pearson_max
            value: 0.8122790124383042
            name: Pearson Max
          - type: spearman_max
            value: 0.8123119892530147
            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.

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-base-p2
• Maximum Sequence Length: 512 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': 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("quarkss/indobert-base-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, 768]

# 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	0.8577
spearman_cosine	0.8589
pearson_manhattan	0.8315
spearman_manhattan	0.8355
pearson_euclidean	0.8318
spearman_euclidean	0.8359
pearson_dot	0.7767
spearman_dot	0.7836
pearson_max	0.8577
spearman_max	0.8589

Semantic Similarity

• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8123
spearman_cosine	0.8123
pearson_manhattan	0.7988
spearman_manhattan	0.7967
pearson_euclidean	0.7993
spearman_euclidean	0.7972
pearson_dot	0.7122
spearman_dot	0.7015
pearson_max	0.8123
spearman_max	0.8123

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:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

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	0.0615	-	-
0.5556	200	0.0336	-	-
0.8333	300	0.0331	-	-
1.1111	400	0.0235	-	-
1.3889	500	0.018	0.8472	-
1.6667	600	0.0164	-	-
1.9444	700	0.0159	-	-
2.2222	800	0.0097	-	-
2.5	900	0.0085	-	-
2.7778	1000	0.0084	0.8563	-
3.0556	1100	0.0076	-	-
3.3333	1200	0.0056	-	-
3.6111	1300	0.0054	-	-
3.8889	1400	0.0052	-	-
4.1667	1500	0.0047	0.8589	-
4.4444	1600	0.0045	-	-
4.7222	1700	0.004	-	-
5.0	1800	0.0042	-	0.8123

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

@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",
}