indoedu-e5-base / README.md
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Add new SentenceTransformer model
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metadata
language:
  - id
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6198
  - loss:CoSENTLoss
base_model: intfloat/multilingual-e5-base
datasets:
  - Pustekhan-ITB/stsb-indo-edu
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on intfloat/multilingual-e5-base
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: stsb indo edu dev
          type: stsb-indo-edu-dev
        metrics:
          - type: pearson_cosine
            value: 0.1930033858243812
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.17647076252403324
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: stsb indo edu test
          type: stsb-indo-edu-test
        metrics:
          - type: pearson_cosine
            value: 0.15065000397563194
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.1512326380689479
            name: Spearman Cosine

SentenceTransformer based on intfloat/multilingual-e5-base

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-base on the stsb-indo-edu 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: intfloat/multilingual-e5-base
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 dimensions
  • 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: XLMRobertaModel 
  (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})
  (2): Normalize()
)

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("ewideplus/indoedu-e5-base")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
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 stsb-indo-edu-dev stsb-indo-edu-test
pearson_cosine 0.193 0.1507
spearman_cosine 0.1765 0.1512

Training Details

Training Dataset

stsb-indo-edu

  • Dataset: stsb-indo-edu at f84d4d6
  • Size: 6,198 training samples
  • Columns: sentence1, sentence2, and score
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 score
    type list list float
    details
    • min: 18 elements
    • mean: 58.40 elements
    • max: 137 elements
    • min: 15 elements
    • mean: 54.31 elements
    • max: 118 elements
    • min: 0.0
    • mean: 0.46
    • max: 1.0
  • Samples:
    sentence1 sentence2 score
    ['query: P', 'query: e', 'query: l', 'query: a', 'query: j', ...] ['passage: T', 'passage: a', 'passage: r', 'passage: i', 'passage: a', ...] 0.76
    ['query: S', 'query: e', 'query: b', 'query: e', 'query: l', ...] ['passage: U', 'passage: p', 'passage: a', 'passage: y', 'passage: a', ...] 0.85
    ['query: B', 'query: e', 'query: b', 'query: e', 'query: r', ...] ['passage: I', 'passage: n', 'passage: i', 'passage: ', 'passage: m', ...] 0.63
  • Loss: CoSENTLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "pairwise_cos_sim"
    }
    

Evaluation Dataset

stsb-indo-edu

  • Dataset: stsb-indo-edu at f84d4d6
  • Size: 1,536 evaluation samples
  • Columns: sentence1, sentence2, and score
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 score
    type list list float
    details
    • min: 14 elements
    • mean: 86.67 elements
    • max: 172 elements
    • min: 22 elements
    • mean: 88.94 elements
    • max: 177 elements
    • min: 0.0
    • mean: 0.42
    • max: 1.0
  • Samples:
    sentence1 sentence2 score
    ['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...] ['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...] 1.0
    ['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...] ['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...] 0.95
    ['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...] ['passage: P', 'passage: r', 'passage: i', 'passage: a', 'passage: ', ...] 1.0
  • Loss: CoSENTLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "pairwise_cos_sim"
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 16
  • learning_rate: 1e-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: 32
  • 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
  • torch_empty_cache_steps: None
  • learning_rate: 1e-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: None
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • 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
  • use_liger_kernel: False
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss Validation Loss stsb-indo-edu-dev_spearman_cosine stsb-indo-edu-test_spearman_cosine
-1 -1 - - 0.0995 -
0.5155 100 6.2244 4.7594 0.1027 -
1.0309 200 6.1605 4.7518 0.1502 -
1.5464 300 6.16 4.7553 0.1564 -
2.0619 400 6.1609 4.7527 0.1714 -
2.5773 500 6.1593 4.7698 0.1495 -
3.0928 600 6.1517 4.7516 0.1657 -
3.6082 700 6.1555 4.7463 0.1787 -
4.1237 800 6.1452 4.7548 0.1665 -
4.6392 900 6.1523 4.7494 0.1765 -
-1 -1 - - - 0.1512

Framework Versions

  • Python: 3.11.11
  • Sentence Transformers: 3.4.1
  • Transformers: 4.48.3
  • PyTorch: 2.5.1+cu124
  • Accelerate: 1.3.0
  • Datasets: 3.3.2
  • Tokenizers: 0.21.0

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}