ihsan31415/fewshot-finetuned-ihsg-sbert

SentenceTransformer based on firqaaa/indo-sentence-bert-large

This is a sentence-transformers model finetuned from firqaaa/indo-sentence-bert-large. It maps sentences & paragraphs to a 2048-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: firqaaa/indo-sentence-bert-large <!-- at revision 4fa0d9d8e3a1ebc2632f321da6c863f905188034 -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 2048 dimensions
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

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': 1024, 'pooling_mode_cls_token': True, '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("ihsan31415/fewshot-finetuned-ihsg-sbert")
# Run inference
sentences = [
    'IHSG ditutup melemah setelah rilis laporan keuangan emiten tambang menunjukkan penurunan laba bersih akibat penurunan harga komoditas global.',
    'Fluktuasi harga komoditas seperti batu bara dan nikel memiliki pengaruh besar terhadap kinerja emiten tambang di Bursa Efek Indonesia.',
    'Sekolah menengah kejuruan mengadakan pameran hasil karya teknologi siswa, termasuk robot sederhana dan alat pendeteksi banjir.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 2048]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 220 training samples
• —Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>sentence_2</code>
• —Approximate statistics based on the first 220 samples: | | sentence0 | sentence1 | sentence_2 | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 23.9 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.24 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.13 tokens</li><li>max: 24 tokens</li></ul> |
• —Samples: | sentence0 | sentence1 | sentence_2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | <code>IHSG menguat setelah rilis data penjualan ritel nasional menunjukkan kenaikan yang lebih tinggi dari proyeksi analis.</code> | <code>Data ekonomi yang menunjukkan pemulihan konsumsi domestik biasanya mendukung penguatan indeks.</code> | <code>Lomba lukis mural bertema lingkungan hidup diselenggarakan oleh mahasiswa seni rupa.</code> | | <code>IHSG rebound setelah rilis data cadangan devisa yang naik ke USD 138 miliar, memberikan kepercayaan pasar terhadap stabilitas neraca eksternal Indonesia.</code> | <code>Kenaikan cadangan devisa dinilai mampu menopang nilai tukar rupiah dan memperkuat daya tahan pasar modal terhadap gejolak global.</code> | <code>Pagelaran musik jazz di taman kota menarik ribuan penonton pada akhir pekan lalu.</code> | | <code>IHSG menguat setelah IMF merilis laporan yang menyatakan bahwa Indonesia termasuk negara yang paling resilien di kawasan Asia Tenggara.</code> | <code>Pujian dari lembaga internasional dapat memperkuat sentimen positif investor terhadap pasar domestik.</code> | <code>Kompetisi debat Bahasa Inggris tingkat nasional diikuti oleh lebih dari 100 tim SMA.</code> |
• —Loss: <code>TripletLoss</code> with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 4
• —per_device_eval_batch_size: 4
• —num_train_epochs: 30
• —multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• —overwrite_output_dir: False
• —do_predict: False
• —eval_strategy: no
• —prediction_loss_only: True
• —per_device_train_batch_size: 4
• —per_device_eval_batch_size: 4
• —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: 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: 30
• —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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
• —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: round_robin

</details>

Training Logs

Framework Versions

• —Python: 3.11.13
• —Sentence Transformers: 4.1.0
• —Transformers: 4.52.4
• —PyTorch: 2.6.0+cu124
• —Accelerate: 1.7.0
• —Datasets: 2.14.4
• —Tokenizers: 0.21.1

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",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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