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kevinadityai/minilm-ai-faq-embeddings-v3

sourceHugging Faceupdated 1y agoView on Hugging Face

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Model Card

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 Type: Sentence Transformer
—Base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
—Maximum Sequence Length: 128 tokens
—Output Dimensionality: 384 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': 128, 'do_lower_case': False, 'architecture': '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:

bash

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("kevinadityai/minilm-ai-faq-embeddings-v3")
# Run inference
queries = [
    "Di mana ada lokasi Rumah Sakit Siloam?",
]
documents = [
    'Ada 41 Rumah Sakit modern yang terdiri dari 14 Rumah Sakit di Jabodetabek dan 27 rumah sakit yang tersebar di Jawa, Sumatera, Kalimantan, Sulawesi, serta Bali dan Nusa Tenggara.',
    'Nama lain Siloam Wenang adalah Siloam Hospitals Manado atau SHMN',
    'Ada 41 Rumah Sakit modern yang terdiri dari 14 Rumah Sakit di Jabodetabek dan 27 rumah sakit yang tersebar di Jawa, Sumatera, Kalimantan, Sulawesi, serta Bali dan Nusa Tenggara.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 384] [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.8557, -0.2976,  0.8557]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Triplet

—Dataset: ai-faq-validation
—Evaluated with <code>TripletEvaluator</code>

Metric	Value
cosine_accuracy	1.0

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Training Details

Training Dataset

Unnamed Dataset

—Size: 92,112 training samples
—Columns: <code>query</code>, <code>answerpositive</code>, and <code>answernegative</code>
—Approximate statistics based on the first 1000 samples: | | query | answerpositive | answernegative | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 11 tokens</li><li>mean: 12.52 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 42.25 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 33.1 tokens</li><li>max: 128 tokens</li></ul> |
—Samples: | query | answerpositive | answernegative | |:----------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Di mana ada lokasi Rumah Sakit Siloam?</code> | <code>Ada 41 Rumah Sakit modern yang terdiri dari 14 Rumah Sakit di Jabodetabek dan 27 rumah sakit yang tersebar di Jawa, Sumatera, Kalimantan, Sulawesi, serta Bali dan Nusa Tenggara.</code> | <code>Siloam Hospitals menawarkan banyak pilihan jenis kamar rawat inap. Silahkan pilih rumah sakit yang akan Anda kunjungi untuk mengetahui jenis kamar rawat inap yang ditawarkan di setiap unit.</code> | | <code>Di mana ada lokasi Rumah Sakit Siloam?</code> | <code>Ada 41 Rumah Sakit modern yang terdiri dari 14 Rumah Sakit di Jabodetabek dan 27 rumah sakit yang tersebar di Jawa, Sumatera, Kalimantan, Sulawesi, serta Bali dan Nusa Tenggara.</code> | <code>Untuk memastikan keamanan dan kualitas suplai darah, kami secara eksklusif menerima darah dari Palang Merah Indonesia.</code> | | <code>Di mana ada lokasi Rumah Sakit Siloam?</code> | <code>Ada 41 Rumah Sakit modern yang terdiri dari 14 Rumah Sakit di Jabodetabek dan 27 rumah sakit yang tersebar di Jawa, Sumatera, Kalimantan, Sulawesi, serta Bali dan Nusa Tenggara.</code> | <code>Kardiologi: Di Cardiac Center kami terdapat Unit Perawatan Jantung (CCU) yang berfokus pada perawatan pasien setelah serangan jantung atau operasi jantung, CT-Scan multi-irisan, Ekokardiografi, Elektrokardiogram (ECG), Pengobatan nuklir, Cath Lab, dan lainnya. Ilmu Saraf: Pusat Ilmu Saraf Siloam menawarkan perawatan lanjutan untuk operasi stereotaktik radiasi menggunakan pisau Gamma dan perawatan untuk Hydrocephalus. Onkologi: Pusat onkologi kami menawarkan perawatan seperti operasi radio pisau gamma, kedokteran nuklir dengan PET-CT dan SPECT-CT Scan, terapi radiasi dengan Rapid Arc Linear Accelerator (LINAC), terapi radionuklida, dan lainnya. Ortopedi: Pusat Keunggulan Siloam dalam Ortopedi menyediakan diagnosis, perawatan, dan rehabilitasi ahli untuk gangguan tulang, sendi, atau jaringan ikat. Meliputi pencegahan patah tulang osteoporosis, Bone Mass Densitometry dan Frax, diagnosa cedera atau penyakit kompleks, CT Scan 2D/3D, 1,5 Tesla dan 3 Tesla MRI, artroplasti revisi kompleks di ...</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

Evaluation Dataset

Unnamed Dataset

—Size: 1,000 evaluation samples
—Columns: <code>query</code>, <code>answerpositive</code>, and <code>answernegative</code>
—Approximate statistics based on the first 1000 samples: | | query | answerpositive | answernegative | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 11 tokens</li><li>mean: 12.52 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 42.25 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 33.1 tokens</li><li>max: 128 tokens</li></ul> |
—Samples: | query | answerpositive | answernegative | |:----------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Di mana ada lokasi Rumah Sakit Siloam?</code> | <code>Ada 41 Rumah Sakit modern yang terdiri dari 14 Rumah Sakit di Jabodetabek dan 27 rumah sakit yang tersebar di Jawa, Sumatera, Kalimantan, Sulawesi, serta Bali dan Nusa Tenggara.</code> | <code>Siloam Hospitals menawarkan banyak pilihan jenis kamar rawat inap. Silahkan pilih rumah sakit yang akan Anda kunjungi untuk mengetahui jenis kamar rawat inap yang ditawarkan di setiap unit.</code> | | <code>Di mana ada lokasi Rumah Sakit Siloam?</code> | <code>Ada 41 Rumah Sakit modern yang terdiri dari 14 Rumah Sakit di Jabodetabek dan 27 rumah sakit yang tersebar di Jawa, Sumatera, Kalimantan, Sulawesi, serta Bali dan Nusa Tenggara.</code> | <code>Untuk memastikan keamanan dan kualitas suplai darah, kami secara eksklusif menerima darah dari Palang Merah Indonesia.</code> | | <code>Di mana ada lokasi Rumah Sakit Siloam?</code> | <code>Ada 41 Rumah Sakit modern yang terdiri dari 14 Rumah Sakit di Jabodetabek dan 27 rumah sakit yang tersebar di Jawa, Sumatera, Kalimantan, Sulawesi, serta Bali dan Nusa Tenggara.</code> | <code>Kardiologi: Di Cardiac Center kami terdapat Unit Perawatan Jantung (CCU) yang berfokus pada perawatan pasien setelah serangan jantung atau operasi jantung, CT-Scan multi-irisan, Ekokardiografi, Elektrokardiogram (ECG), Pengobatan nuklir, Cath Lab, dan lainnya. Ilmu Saraf: Pusat Ilmu Saraf Siloam menawarkan perawatan lanjutan untuk operasi stereotaktik radiasi menggunakan pisau Gamma dan perawatan untuk Hydrocephalus. Onkologi: Pusat onkologi kami menawarkan perawatan seperti operasi radio pisau gamma, kedokteran nuklir dengan PET-CT dan SPECT-CT Scan, terapi radiasi dengan Rapid Arc Linear Accelerator (LINAC), terapi radionuklida, dan lainnya. Ortopedi: Pusat Keunggulan Siloam dalam Ortopedi menyediakan diagnosis, perawatan, dan rehabilitasi ahli untuk gangguan tulang, sendi, atau jaringan ikat. Meliputi pencegahan patah tulang osteoporosis, Bone Mass Densitometry dan Frax, diagnosa cedera atau penyakit kompleks, CT Scan 2D/3D, 1,5 Tesla dan 3 Tesla MRI, artroplasti revisi kompleks di ...</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

Training Hyperparameters

Non-Default Hyperparameters

—eval_strategy: steps
—per_device_train_batch_size: 64
—per_device_eval_batch_size: 64
—learning_rate: 2e-05
—num_train_epochs: 1
—warmup_ratio: 0.1
—fp16: True
—dataloader_num_workers: 4
—batch_sampler: no_duplicates

All Hyperparameters

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

—overwrite_output_dir: False
—do_predict: False
—eval_strategy: steps
—prediction_loss_only: True
—per_device_train_batch_size: 64
—per_device_eval_batch_size: 64
—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: 1
—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
—use_ipex: False
—bf16: False
—fp16: True
—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: 4
—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}
—parallelism_config: None
—deepspeed: None
—label_smoothing_factor: 0.0
—optim: adamwtorchfused
—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
—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: False
—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: False
—prompts: None
—batch_sampler: no_duplicates
—multi_dataset_batch_sampler: proportional
—router_mapping: {}
—learning_rate_mapping: {}

</details>

Training Logs

Epoch	Step	Training Loss	Validation Loss	ai-faq-validation_cosine_accuracy
-1	-1	-	-	0.8450
0.1389	100	1.0289	0.0005	1.0
0.3472	500	0.0028	0.0000	1.0
0.6944	1000	0.0024	0.0000	1.0

Framework Versions

—Python: 3.12.11
—Sentence Transformers: 5.1.1
—Transformers: 4.56.2
—PyTorch: 2.8.0+cu126
—Accelerate: 1.10.1
—Datasets: 4.0.0
—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}
}

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