README.md

metadata

base_model: indobenchmark/indobert-base-p2
datasets: []
language: []
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:133472
  - loss:SoftmaxLoss
widget:
  - source_sentence: >-
      Dua tim anak-anak, yang satu berwarna hijau dan yang lainnya berwarna
      merah, bermain bersama dalam permainan Rugby saat hujan.
    sentences:
      - Tiga orang berada di dalam perahu.
      - seorang pria di atas sepeda
      - Tim rugby anak-anak, merah versus hijau bermain di tengah hujan.
  - source_sentence: Seorang pria melakukan perawatan di rel kereta api
    sentences:
      - Dua orang terlibat dalam percakapan.
      - Ada seorang wanita melakukan pekerjaan di rel kereta api.
      - orang-orang duduk di bar
  - source_sentence: Sepasang suami istri dengan pakaian renang berjalan di pantai.
    sentences:
      - pasangan itu duduk di dalam
      - Pria itu sedang makan.
      - Dua orang sedang berpose untuk difoto.
  - source_sentence: Dua orang sedang duduk di samping api unggun bertumpuk kayu di malam hari.
    sentences:
      - Seseorang memegang jeruk dan berjalan
      - Orang-orang duduk di luar di malam hari.
      - Orang-orang berada di luar.
  - source_sentence: >-
      Wanita profesional di meja pendaftaran acara sementara pria berjas
      melihat.
    sentences:
      - Orang-orang berkumpul untuk sebuah acara.
      - Seorang wanita sedang berjalan menuju taman.
      - Ada seorang anak yang tersenyum untuk difoto.
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: 0.23146247451934734
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.23182555096720683
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.19847600869622337
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.2038189662328075
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.198744291061789
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.20385658228775938
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.2561502821889763
            name: Pearson Dot
          - type: spearman_dot
            value: 0.25101474046220823
            name: Spearman Dot
          - type: pearson_max
            value: 0.2561502821889763
            name: Pearson Max
          - type: spearman_max
            value: 0.25101474046220823
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.5914831439397401
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.5978838704506128
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.5131648451956073
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.5147175261736068
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.5942850778734059
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6001963453484881
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.5880400881430983
            name: Pearson Dot
          - type: spearman_dot
            value: 0.5933998114680769
            name: Spearman Dot
          - type: pearson_max
            value: 0.5942850778734059
            name: Pearson Max
          - type: spearman_max
            value: 0.6001963453484881
            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: 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("cassador/indobert-snli-v1")
# Run inference
sentences = [
    'Wanita profesional di meja pendaftaran acara sementara pria berjas melihat.',
    'Orang-orang berkumpul untuk sebuah acara.',
    'Ada seorang anak yang tersenyum untuk difoto.',
]
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	0.2315
spearman_cosine	0.2318
pearson_manhattan	0.1985
spearman_manhattan	0.2038
pearson_euclidean	0.1987
spearman_euclidean	0.2039
pearson_dot	0.2562
spearman_dot	0.251
pearson_max	0.2562
spearman_max	0.251

Semantic Similarity

• Dataset: sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.5915
spearman_cosine	0.5979
pearson_manhattan	0.5132
spearman_manhattan	0.5147
pearson_euclidean	0.5943
spearman_euclidean	0.6002
pearson_dot	0.588
spearman_dot	0.5934
pearson_max	0.5943
spearman_max	0.6002

Training Details

Training Dataset

Unnamed Dataset

• Size: 133,472 training samples
• Columns: label, kalimat1, and kalimat2
• Approximate statistics based on the first 1000 samples:

  	label	kalimat1	kalimat2
  type	int	string	string
  details	0: ~50.00% 1: ~50.00%	min: 5 tokens mean: 16.47 tokens max: 48 tokens	min: 4 tokens mean: 9.62 tokens max: 22 tokens

• Samples:

  label	kalimat1	kalimat2
  0	Seseorang di atas kuda melompati pesawat yang rusak.	Seseorang sedang makan malam, memesan telur dadar.
  1	Seseorang di atas kuda melompati pesawat yang rusak.	Seseorang berada di luar ruangan, di atas kuda.
  1	Anak-anak tersenyum dan melambai ke kamera	Ada anak-anak yang hadir

• Loss: SoftmaxLoss

Evaluation Dataset

Unnamed Dataset

• Size: 6,607 evaluation samples
• Columns: label, kalimat1, and kalimat2
• Approximate statistics based on the first 1000 samples:

  	label	kalimat1	kalimat2
  type	int	string	string
  details	0: ~50.10% 1: ~49.90%	min: 5 tokens mean: 16.87 tokens max: 49 tokens	min: 3 tokens mean: 9.45 tokens max: 27 tokens

• Samples:

  label	kalimat1	kalimat2
  1	Dua wanita berpelukan sambil memegang paket untuk pergi.	Dua wanita memegang paket.
  0	Dua wanita berpelukan sambil memegang paket untuk pergi.	Orang-orang berkelahi di luar toko makanan.
  1	Dua anak kecil berbaju biru, satu dengan nomor 9 dan satu dengan nomor 2 berdiri di tangga kayu di kamar mandi dan mencuci tangan di wastafel.	Dua anak dengan kaus bernomor mencuci tangan mereka.

• Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• 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: steps
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• 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	sts-dev_spearman_cosine	sts-test_spearman_cosine
0	0	0.2318	-
2.0	8342	-	0.5979

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",
}