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.

python

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:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

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:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

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

bibtex

@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

bibtex

@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}
}