beasiswa_sbert

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2 <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Beasiswa SCG Sharing the Dream untuk Pelajar dan Mahasiswa Deskripsi: Kabar baik bagi kamu pelajar SMA/Sederajat kelas 10 sampai 12 serta mahasiswa D4 dan S1! Ada tawaranbeasiswadari SCG (Siam Cement Group) yang bisa kamu ikuti!BeasiswaSCG Sharing the Dreamkembali diselenggarakan untuk tahun 2024 dengan batas waktu pendaftaran sampai 9 Juni 2024.',
    'Saya tertarik untuk melanjutkan pendidikan di bidang teknologi dan tidak terkait langsung dengan beasiswa di bidang agama atau manajemen.',
    'Saya berencana untuk melanjutkan pendidikan di bidang manajemen sumber daya manusia dan mencari peluang beasiswa untuk memperluas pengetahuan saya di luar negeri.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 476 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
• Approximate statistics based on the first 476 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	<ul><li>min: 107 tokens</li><li>mean: 168.83 tokens</li><li>max: 256 tokens</li></ul>	<ul><li>min: 52 tokens</li><li>mean: 58.25 tokens</li><li>max: 64 tokens</li></ul>	<ul><li>min: 0.1</li><li>mean: 0.5</li><li>max: 0.9</li></ul>

• Samples:

  sentence_0	sentence_1	label
  <code>Beasiswa S1, S2 dan S3 dari Pemerintah Rumania Deskripsi: Hai Sobat Beasiswa.ID! Pemerintah Rumania sedang membuka beasiswa penuh untuk tahun akademik 2023 – 2024 bagi mahasiswa internasional di luar Uni Eropa, terutama Indonesia untuk melanjutkan studi ke Rumania. Ada puluhan kursi beasiswa biasanya disediakan tiap tahun untuk berbagai tingkatan.</code>	<code>Saya tertarik untuk melanjutkan pendidikan di bidang teknologi dan tidak terkait langsung dengan beasiswa di bidang agama atau manajemen.</code>	<code>0.1</code>
  <code>Beasiswa Konferensi Model Asia-Europe Meeting untuk Mahasiswa di Malta Deskripsi: Ingin merasakan menjadi diplomat muda selama satu minggu? IkutiKonferensi Model Asia-Europe Meeting (ASEM) Spin-off Malta2019untuk mahasiswaS1,S2,danS3dari semua jurusan. Program ini didukung oleh Asia-Europe Foundation (ASEF) dan akan dilaksanakan di Valletta, Republik Malta (Eropa).</code>	<code>Saya baru saja lulus S1 di bidang pendidikan agama Islam dan ingin melanjutkan studi untuk mendapatkan pengalaman belajar di luar negeri dan dalam negeri secara bersamaan.</code>	<code>0.9</code>
  <code>Beasiswa CIMB Niaga untuk Mahasiswa S1 dan D4 Deskripsi: Buat kamu yang sedang mencari beasiswa kuliah, yuk ikuti program BeasiswaCIMBNiaga tahun 2021. Beasiswa ini diselenggarakan oleh PT Bank CIMB Niaga Tbk untuk mendukung mimpi para generasi penerus bangsa untuk mendapatkan pendidikan yang memadai. Beasiswa CIMB Niaga ditawarkan untuk mahasiswa S1 dan D4 semester 5 di seluruh perguruan tinggi di Indonesia.</code>	<code>Saya baru saja lulus S1 di bidang pendidikan agama Islam dan ingin melanjutkan studi untuk mendapatkan pengalaman belajar di luar negeri dan dalam negeri secara bersamaan.</code>	<code>0.9</code>

• Loss: <code>CosineSimilarityLoss</code> with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 5
• 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: 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
• 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: 5
• 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}
• tp_size: 0
• 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
• 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>

Framework Versions

• Python: 3.11.12
• Sentence Transformers: 3.4.1
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.6.0
• Datasets: 3.6.0
• 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",
}

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