metadata

base_model: thenlper/gte-base
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:206874
  - loss:ContrastiveLoss
widget:
  - source_sentence: >-
      Cardiac silhouette size is top normal. Aorta is tortuous and demonstrates
      mild atherosclerotic calcifications diffusely. Hilar contours are normal.
      Pulmonary vasculature is normal. Lungs are clear. No pleural effusion or
      pneumothorax is present. No acute osseous abnormality is detected.  
    sentences:
      - 'No acute cardiopulmonary process. '
      - 'No acute cardiopulmonary abnormality. '
      - 'Normal chest radiographs. '
  - source_sentence: >-
      The lungs are mildly hyperexpanded but clear. No pleural effusion or
      pneumothorax is seen. The cardiac and mediastinal silhouettes are
      unremarkable.  
    sentences:
      - 'Findings worrisome for early/mild left lower lobe pneumonia. '
      - 'No acute cardiopulmonary process. The mediastinum is not widened. '
      - 'No radiographic evidence of acute cardiopulmonary disease. '
  - source_sentence: >-
      Lung volumes are slightly low. The cardiomediastinal silhouette and
      pulmonary vasculature a similar to the prior examination, and
      unremarkable, accounting for low lung volumes. Midline sternal wires are
      intact and well aligned. Mediastinal clips and anastomotic markers are
      noted. The lungs are clear. There is no pleural effusion or pneumothorax.
      Bilateral shoulder prostheses are partially imaged.  
    sentences:
      - 'No acute cardiopulmonary process. '
      - 'No acute intrathoracic abnormality. '
      - >-
        Pulmonary edema, increasing pleural effusions, known mass in the right
        lower lung. 
  - source_sentence: >-
      The left hemi thorax remains opacified. The right lung is now clear. The
      right mediastinal silhouette is unchanged. An endotracheal tube feeding
      tube and right internal jugular catheter remain in place.  
    sentences:
      - 'The right lung now appears clear. No other significant change. '
      - 'No acute cardiopulmonary abnormality. '
      - >-
        Chest findings within normal limits, no secondary metastases suspicious
        lesions identified. 
  - source_sentence: >-
      The atient is status post coronary artery bypass graft surgery. The heart
      is mildly enlarged. There is a large hiatal hernia with an air-fluid
      level. Otherwise, the mediastinal and hilar contours are unremarkable. The
      lungs appear clear. The chest is hyperinflated. There is no pleural
      effusion or pneumothorax. Bony structures are unremarkable.  
    sentences:
      - >-
        1. Left apical pneumothorax still small, but considerably larger. Left
        base pneumothorax also slightly larger. 2. Minimal lucency adjacent to
        the the aortic knob may also represent part of the left lung
        pneumothorax. Attention to this area on followup films to exclude any
        mediastinal air is requested. 3. Extensive subcutaneous emphysema,
        equivocally slightly greater than on the prior film. 4. Minimal interval
        change in position of the left chest tube. 5. Right pneumothorax also
        increased, still small in width, but now seen not only at the right lung
        apex, but also along the right lateral chest wall and at the right
        costophrenic angle in the adjoining lung base. 
      - 'No evidence of acute disease. Normal cardiac size. '
      - >-
        No evidence of acute disease. Hyperinflation. Large hiatal hernia.
        Status post coronary artery bypass graft surgery. 
model-index:
  - name: SentenceTransformer based on thenlper/gte-base
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: validation
          type: validation
        metrics:
          - type: pearson_cosine
            value: 0.8022517557853334
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.810529949353046
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8243043367211444
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8105359053829688
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.824484835649088
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8105299161732425
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.802251755767147
            name: Pearson Dot
          - type: spearman_dot
            value: 0.8105299280214241
            name: Spearman Dot
          - type: pearson_max
            value: 0.824484835649088
            name: Pearson Max
          - type: spearman_max
            value: 0.8105359053829688
            name: Spearman Max

SentenceTransformer based on thenlper/gte-base

This is a sentence-transformers model finetuned from thenlper/gte-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: thenlper/gte-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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("hyojuuun/gte_base_MIMICCXR_FT")
# Run inference
sentences = [
    'The atient is status post coronary artery bypass graft surgery. The heart is mildly enlarged. There is a large hiatal hernia with an air-fluid level. Otherwise, the mediastinal and hilar contours are unremarkable. The lungs appear clear. The chest is hyperinflated. There is no pleural effusion or pneumothorax. Bony structures are unremarkable.  ',
    'No evidence of acute disease. Hyperinflation. Large hiatal hernia. Status post coronary artery bypass graft surgery. ',
    '1. Left apical pneumothorax still small, but considerably larger. Left base pneumothorax also slightly larger. 2. Minimal lucency adjacent to the the aortic knob may also represent part of the left lung pneumothorax. Attention to this area on followup films to exclude any mediastinal air is requested. 3. Extensive subcutaneous emphysema, equivocally slightly greater than on the prior film. 4. Minimal interval change in position of the left chest tube. 5. Right pneumothorax also increased, still small in width, but now seen not only at the right lung apex, but also along the right lateral chest wall and at the right costophrenic angle in the adjoining lung base. ',
]
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]

Evaluation

Metrics

Semantic Similarity

Dataset: validation
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8023
spearman_cosine	0.8105
pearson_manhattan	0.8243
spearman_manhattan	0.8105
pearson_euclidean	0.8245
spearman_euclidean	0.8105
pearson_dot	0.8023
spearman_dot	0.8105
pearson_max	0.8245
spearman_max	0.8105

Training Details

Training Dataset

Unnamed Dataset

Size: 206,874 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 3 tokens
mean: 78.31 tokens
max: 324 tokens

min: 4 tokens
mean: 26.68 tokens
max: 165 tokens

min: 0.0
mean: 0.5
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 3 tokens mean: 78.31 tokens max: 324 tokens	min: 4 tokens mean: 26.68 tokens max: 165 tokens	min: 0.0 mean: 0.5 max: 1.0

Samples:

sentence_0	sentence_1	label
`The lung volumes are low which accentuates the linear and interstitial opacities. An ill-defined opacity in the left lung in the third/fourth interspace has increased since the prior can be early pneumonia. No pneumothorax. Mild to moderate gastric and small bowel distension partially visualized.`	`No evidence of acute cardiopulmonary disease.`	`0.0`
`PA and lateral views of the chest were provided demonstrating no focal consolidation, effusion or pneumothorax. The cardiomediastinal silhouette is normal. Bony structures are intact. No free air below the right hemidiaphragm.`	`No acute intrathoracic process.`	`1.0`
`Previously seen right-sided PICC is no longer seen. Enlargement of the cardiomediastinal silhouette is grossly stable. There are low lung volumes, which accentuate the bronchovascular markings. No focal consolidation is seen. There is no pleural effusion or pneumothorax.`	`Low lung volumes but no focal consolidation to suggest pneumonia.`	`1.0`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.5,
    "size_average": true
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 96
per_device_eval_batch_size: 96
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 96
per_device_eval_batch_size: 96
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
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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}
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
dispatch_batches: None
split_batches: 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
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	validation_spearman_max
0.0464	100	-	0.6178
0.0928	200	-	0.6904
0.1392	300	-	0.7290
0.1856	400	-	0.7596
0.2320	500	0.0191	0.7715
0.2784	600	-	0.7783
0.3248	700	-	0.7851
0.3712	800	-	0.7885
0.4176	900	-	0.7942
0.4640	1000	0.0118	0.7965
0.5104	1100	-	0.8061
0.5568	1200	-	0.8035
0.6032	1300	-	0.8082
0.6497	1400	-	0.8105

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.2.0
Transformers: 4.44.2
PyTorch: 2.4.1+cu121
Accelerate: 0.34.2
Datasets: 3.0.1
Tokenizers: 0.19.1

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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}