minilm-ai-faq-embeddings-v4

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 <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
• Maximum Sequence Length: 128 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': 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:

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("kevinadityai/minilm-ai-faq-embeddings-v4")
# Run inference
queries = [
    "boleh jenguk pasien di luar jam kunjungan?",
]
documents = [
    'Dalam beberapa kasus, tamu tidak diizinkan untuk berkunjung setelah jam menjenguk untuk memastikan pemulihan kesehatan pasien. Namun, pengecualian diperbolehkan dalam beberapa kasus.',
    'Lokasinya di Jl. Duren Tiga Raya No. 20, Duren Tiga, Jakarta Selatan',
    'Sebagian besar dokter kami memiliki kemampuan berkomunikasi secara bilingual dan dapat berkomunikasi dalam bahasa Inggris. Selain itu, kami menyediakan juru bahasa Korea di Siloam Hospitals Lippo Village. Anda juga dapat menemukan spesialis Jepang di J-Clinic yang berlokasi di MRCCC Siloam Hospitals dan Siloam Hospitals Cikarang.',
]
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([[ 0.6357, -0.1197,  0.2127]])

<!-- ### 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.* -->

Evaluation

Metrics

Triplet

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

<!-- ## 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: 3,020 training samples
• Columns: <code>query</code>, <code>answer_positive</code>, and <code>answer_negative</code>
• Approximate statistics based on the first 1000 samples:

  	query	answer_positive	answer_negative
  type	string	string	string
  details	<ul><li>min: 4 tokens</li><li>mean: 10.87 tokens</li><li>max: 20 tokens</li></ul>	<ul><li>min: 8 tokens</li><li>mean: 44.62 tokens</li><li>max: 128 tokens</li></ul>	<ul><li>min: 8 tokens</li><li>mean: 27.58 tokens</li><li>max: 68 tokens</li></ul>

• Samples:

  query	answer_positive	answer_negative
  <code>siloam dimana aja</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>Jika asuransi tidak bekerja sama, maka sistem pembayaran akan dilakukan dengan sistem reimburse. Kami pastikan dokumen pendukung yang dibutuhkan untuk reimburse diberikan kepada pasien.</code>
  <code>lokasi rs 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>BIMC Kuta adalah kepanjangan dari BIMC Kuta</code>
  <code>siloam ada dimana</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>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.</code>

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

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

Evaluation Dataset

Unnamed Dataset

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

  	query	answer_positive	answer_negative
  type	string	string	string
  details	<ul><li>min: 7 tokens</li><li>mean: 11.16 tokens</li><li>max: 20 tokens</li></ul>	<ul><li>min: 20 tokens</li><li>mean: 44.54 tokens</li><li>max: 128 tokens</li></ul>	<ul><li>min: 8 tokens</li><li>mean: 27.73 tokens</li><li>max: 54 tokens</li></ul>

• Samples:

  query	answer_positive	answer_negative
  <code>siloam dimana aja</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>Jika asuransi tidak bekerja sama, maka sistem pembayaran akan dilakukan dengan sistem reimburse. Kami pastikan dokumen pendukung yang dibutuhkan untuk reimburse diberikan kepada pasien.</code>
  <code>lokasi rs 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>BIMC Kuta adalah kepanjangan dari BIMC Kuta</code>
  <code>siloam ada dimana</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>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.</code>

• Loss: <code>MultipleNegativesRankingLoss</code> 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: 128
• per_device_eval_batch_size: 128
• learning_rate: 2e-05
• num_train_epochs: 10
• warmup_ratio: 0.1
• 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: 128
• per_device_eval_batch_size: 128
• 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
• 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, '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
• 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

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

@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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