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ramamimu
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finetuning-MiniLM-L12-v2

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:704
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Inference Endpoints
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SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-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 Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, '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})
)

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 = [
    'Apa saja panduan umum pelaksanaan Program Magang di ITS?',
    'Mahasiswa dalam melaksanakan magang harus memenuhi ketentuan berikut: 1. Pelaksanaan Program Magang memiliki durasi minimal 1 bulan dan maksimal 6 bulan. 2. Selama Program Magang berlangsung, mahasiswa tidak harus mengajukan cuti. 3. Mahasiswa secara penuh waktu bekerja di lapangan sesuai kesepakatan. 4. Mahasiswa bisa mendapatkan izin untuk kegiatan akademik tertentu dengan kesepakatan pihak Mitra Magang. 5. Mahasiswa dapat mengajukan konversi mata kuliah dengan CPMK yang selaras. 6. Diperlukan pembimbing internal dari Dosen Departemen dan pembimbing lapangan dari Mitra Magang. 7. Sebelum Program Magang, wajib menandatangani perjanjian kerjasama dan nota kesepahaman.',
    '1. Mahasiswa mengurus surat rekomendasi departemen untuk pengajuan magang ke Mitra\n2. Mahasiswa mengajukan permohonan magang ke Mitra\n3. Mitra melakukan seleksi magang\n4. Mahasiswa menerima hasil seleksi magang dari Mitra\n5. Apabila tidak diterima, maka mahasiswa harus mengulang sejak langkah awal\n6. Apabila diterima, Mahasiswa melaporkan ke Departemen\n7. Mahasiswa/Departemen melakukan koordinasi dengan PK2 untuk pengurusan PKS dengan menyertakan Proposal Magang, Surat Rekomendasi Departemen dan Surat Penerimaan Magang\n8. Proses pengurusan PKS (Dapat dilakukan bersamaan dengan pelaksanaan Magang)',
]
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]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 704 training samples
  • Columns: sentence_0 and sentence_1
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1
    type string string
    details
    • min: 6 tokens
    • mean: 16.22 tokens
    • max: 42 tokens
    • min: 5 tokens
    • mean: 57.55 tokens
    • max: 128 tokens
  • Samples:
    sentence_0 sentence_1
    Bagaimana cara menulis dokumen pemerintah dalam daftar pustaka? Nama pengarang dan tahun publikasi sama dengan penulisan artikel dari jurnal. Judul dokumen dicetak miring/italic. Volume atau nomor (jika ada). Nama penerbit. Kota tempat diterbitkan.
    Apa tugas dosen wali dalam pelaksanaan MBKM? Dosen wali ditugaskan oleh Prodi untuk membuat perencanaan bersama dengan mahasiswa yang akan melaksanakan kegiatan MBKM, melakukan evaluasi terhadap kesesuaian bentuk dan lama pelaksanaan MBKM, serta melakukan penilaian atas rencana, pelaksanaan, dan evaluasi MBKM.
    Apa yang dimaksud dengan 'Hak Belajar Tiga Semester di Luar Program Studi'? Hak Belajar Tiga Semester di Luar Program Studi adalah kebijakan yang memberikan mahasiswa kesempatan untuk satu semester (setara dengan 20 SKS) menempuh pembelajaran di luar program studi pada perguruan tinggi yang sama, dan paling lama dua semester (setara dengan 40 SKS) di program studi yang sama atau berbeda di perguruan tinggi yang berbeda atau di luar perguruan tinggi.
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

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

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • 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: 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
  • 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}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True}
  • 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
  • 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_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin

Framework Versions

  • Python: 3.8.10
  • Sentence Transformers: 3.0.1
  • Transformers: 4.39.2
  • PyTorch: 2.3.1+cu121
  • Accelerate: 0.29.3
  • Datasets: 2.19.2
  • Tokenizers: 0.15.2

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",
}

MultipleNegativesRankingLoss 

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
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