gte-ipb-clinical

GTE Multilingual fine-tuned on clinical-to-drug mapping

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-multilingual-base. 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: Alibaba-NLP/gte-multilingual-base <!-- at revision 9bbca17d9273fd0d03d5725c7a4b0f6b45142062 -->
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown -->

• Language: multilingual
• License: apache-2.0

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': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Keluhan: nan\nAnamnesa Pemeriksaan Dokter: mual pusing',
    'Obat: Buscopan\nDeskripsi Obat: Obat untuk mengurangi kejang otot polos saluran pencernaan',
    'Obat: Blocand 16 mg\nDeskripsi Obat: Obat antihipertensi golongan ARB dosis tinggi untuk menurunkan tekanan darah',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

<!-- ### 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.* --> <!-- ## 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: 217,680 training samples
• Columns: <code>anchor</code> and <code>positive</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	<ul><li>min: 17 tokens</li><li>mean: 42.44 tokens</li><li>max: 177 tokens</li></ul>	<ul><li>min: 15 tokens</li><li>mean: 22.55 tokens</li><li>max: 38 tokens</li></ul>

• Samples:

  anchor	positive
  <code>Keluhan: nan<br>Anamnesa Pemeriksaan Dokter: gusi kiri bengkak
  <br>nyeri tenggorokan, ada amandel 2 hari yang lalu
  <br>puisng </code>	<code>Obat: Eflagen 50 mg<br>Deskripsi Obat: Obat antiinflamasi nonsteroid (NSAID) untuk mengurangi nyeri</code>
  <code>Keluhan: pasien mengatakan kuku kaki jempol kiri merah dan nyeri sejak 1 bulan yll hilang timbul<br>Anamnesa Pemeriksaan Dokter: pasien mengatakan kuku kaki jempol kiri merah dan nyeri sejak 1 bulan yll hilang timbul. merah + bengkak + </code>	<code>Obat: Mefinal 500 mg<br>Deskripsi Obat: Obat antiinflamasi nonsteroid (NSAID) untuk mengurangi nyeri</code>
  <code>Keluhan: batuk dahak sudah 1 minggu<br>Anamnesa Pemeriksaan Dokter: batuk dahak sejak 1 minggu
  <br>RPO : Acetylcystein, OBH</code>	<code>Obat: Cefixime 200 mg<br>Deskripsi Obat: Antibiotik golongan sefalosporin untuk mengobati infeksi bakteri</code>

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

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

Evaluation Dataset

Unnamed Dataset

• Size: 24,187 evaluation samples
• Columns: <code>anchor</code> and <code>positive</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	<ul><li>min: 16 tokens</li><li>mean: 42.79 tokens</li><li>max: 112 tokens</li></ul>	<ul><li>min: 15 tokens</li><li>mean: 22.79 tokens</li><li>max: 38 tokens</li></ul>

• Samples:

  anchor	positive
  <code>Keluhan: NYERI PERUT, MUAL, MUNTAH 1KALI HARI INI,<br>Anamnesa Pemeriksaan Dokter: NYERI PERUT, MUAL, MUNTAH 1KALI HARI INI, tenggorokan gatla
  <br>rpo myalanta
  <br>alergi obat dsiangkal </code>	<code>Obat: Lambucid<br>Deskripsi Obat: Antasida untuk meredakan gejala asam lambung berlebih</code>
  <code>Keluhan: badan lemas tadi pagi, mual- muntah- batuk pilek- <br>Anamnesa Pemeriksaan Dokter: lemas sejak pagi ini. demam - pingsan - sempat terjatuh karena lemas. pola makan tidak teratur.
  <br>rpo tablet tambah darah. riw anemia + dikatakan saat SMA 3 tahun yll. tidak ingat hb berapa. </code>	<code>Obat: Caviplex<br>Deskripsi Obat: Suplemen multivitamin untuk memenuhi kebutuhan vitamin dan mineral</code>
  <code>Keluhan: rencana rujukan rsMD spP tgl 23-04-24
  <br>keluhan hari ini kepala pusing terasa berat, batuk, nyeri badan. <br>Anamnesa Pemeriksaan Dokter: rencana rujukan rsMD spP tgl 23-04-24 keluhan hari ini kepala pusing terasa berat, batuk, nyeri badan.</code>	<code>Obat: Profat sirup<br>Deskripsi Obat: Suplemen penambah darah sirup untuk anak</code>

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

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: True
• 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: 32
• per_device_eval_batch_size: 32
• 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: 5e-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: 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}
• 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: 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

</details>

Training Logs

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

Framework Versions

• Python: 3.11.13
• Sentence Transformers: 4.1.0
• Transformers: 4.53.2
• PyTorch: 2.6.0+cu124
• Accelerate: 1.8.1
• Datasets: 2.14.4
• Tokenizers: 0.21.2

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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