stel42/bs-faq-mpnet-basev2-finetuned

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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: sentence-transformers/all-mpnet-base-v2
• Maximum Sequence Length: 384 tokens
• Output Dimensionality: 768 dimensions
• 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': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
  (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("stel42/bs-faq-mpnet-basev2-finetuned")
# Run inference
queries = [
    "RSUSW itu singkatan dari apa?",
]
documents = [
    'RSU Syubbanul Wathon adalah kepanjangan dari RSUSW',
    'Lokasinya di Jl. Magelang - Kopeng km 08 Tegalrejo Kabupaten Magelang 56192',
    'Nama lain Siloam Hospitals Bangka Belitung adalah SHBB atau Siloam Pangkalan Baru',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.8722, 0.2129, 0.0839]])

Evaluation

Metrics

Triplet

• Dataset: faq-test
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	1.0

Training Details

Training Dataset

Unnamed Dataset

• Size: 2,718 training samples
• Columns: query, answer_positive, and answer_negative
• Approximate statistics based on the first 1000 samples:

  	query	answer_positive	answer_negative
  type	string	string	string
  details	min: 6 tokens mean: 14.47 tokens max: 31 tokens	min: 10 tokens mean: 48.05 tokens max: 384 tokens	min: 10 tokens mean: 44.12 tokens max: 216 tokens

• Samples:

  query	answer_positive	answer_negative
  RS Jantung Diagram Cinere punya sebutan lain?	Nama lain RS Jantung Diagram Cinere adalah SHCN atau Siloam Cinere	Lokasinya di Jl. Kompol Maksum No.296, Peterongan, Kec. Semarang Sel., Kota Semarang, Jawa Tengah 50242
  aplikasi mysiloam itu fungsinya apa	Aplikasi MySiloam adalah aplikasi mobile untuk membantu pengguna menikmati kemudahan layanan kesehatan yang disediakan oleh Rumah Sakit Siloam. Beberapa fitur yang sudah membantu banyak orang adalah, pembuatan janji temu dokter, pemesanan tes dan layanan kesehatan, emergency, dan ambulance call.	Lokasinya di JL. Mampang Prapatan XVI, Kel. Duren Tiga, Kec. Pancoran, Kota Adm. Jakarta Selatan, Prov. DKI Jakarta
  Mau ke Siloam Surabaya, alamatnya apa?	Lokasinya di Jl. Raya Gubeng No.70, Gubeng, Kec. Gubeng, Kota SBY, Jawa Timur 60281	Ya, Anda dapat menanyakan kepada staff registrasi kami untuk mendapatkan perkiraan biaya sebelum perawatan Anda. Harap dicatat bahwa ada kemungkinan bahwa perkiraan biaya yang diberikan berbeda dari biaya akhir.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 302 evaluation samples
• Columns: query, answer_positive, and answer_negative
• Approximate statistics based on the first 302 samples:

  	query	answer_positive	answer_negative
  type	string	string	string
  details	min: 6 tokens mean: 14.56 tokens max: 26 tokens	min: 10 tokens mean: 48.08 tokens max: 260 tokens	min: 17 tokens mean: 41.42 tokens max: 111 tokens

• Samples:

  query	answer_positive	answer_negative
  SHPL ada di jalan apa?	Lokasinya di Jl. POM IX, Lorok Pakjo, Kec. Ilir Bar. I, Kota Palembang, Sumatera Selatan 30137	Pendaftaran MCU / Medical Check Up melalui Pembayaran pada pendaftaran MCU / Medical Check Up dapat dilakukan dengan transfer ke rekening Bank, Credit Card, dan pembayaran cash pada saat melakukan registrasi.
  Dimana Siloam TB Simatupang?	Lokasinya di Jl. RA. Kartini No. 8, Cilandak Jakarta Selatan	Lama waktu yang dibutuhkan untuk menjalankan MCU / Medical Check Up sekitar 2-6 jam tergantung dari jenis paket pemeriksaan yang dijalankan.
  siloam putera bahagia disebut apa lagi?	Nama lain Siloam Hospitals Putera Bahagia adalah SHCB atau Siloam Harjamukti	Lokasinya di Jl. Kelapa Dua Raya No.1001, Klp. Dua, Kec. Klp. Dua, Kabupaten Tangerang, Banten 15810

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• learning_rate: 2e-05
• num_train_epochs: 10
• warmup_ratio: 0.1
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 8
• 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: 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: 10
• 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
• 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, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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
• hub_revision: None
• 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: no
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: True
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	faq-test_cosine_accuracy
0.2941	100	0.9634	-	-
0.5882	200	0.2638	0.1916	-
0.8824	300	0.1286	-	-
1.1765	400	0.0794	0.0689	-
1.4706	500	0.0314	-	-
1.7647	600	0.0236	0.0228	-
2.0588	700	0.0178	-	-
2.3529	800	0.0127	0.0207	-
2.6471	900	0.0112	-	-
2.9412	1000	0.0061	0.0268	-
0.5882	100	0.0189	-	-
1.1765	200	0.0173	0.0289	-
1.7647	300	0.0235	-	-
2.3529	400	0.0148	0.0212	-
2.9412	500	0.0111	-	-
3.5294	600	0.0099	0.0209	-
4.1176	700	0.0093	-	-
4.7059	800	0.0102	0.0247	-
5.2941	900	0.0076	-	-
5.8824	1000	0.0038	0.0148	-
6.4706	1100	0.0073	-	-
7.0588	1200	0.0031	0.0262	-
7.6471	1300	0.0045	-	-
8.2353	1400	0.0048	0.0235	-
8.8235	1500	0.0042	-	-
9.4118	1600	0.0052	0.0216	-
10.0	1700	0.0086	-	-
-1	-1	-	-	1.0

Framework Versions

• Python: 3.11.6
• Sentence Transformers: 5.1.2
• Transformers: 4.57.1
• PyTorch: 2.9.1+cu128
• Accelerate: 1.11.0
• Datasets: 4.4.1
• Tokenizers: 0.22.1

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",
}

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}
}