---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:27120
- loss:ContrastiveLoss
base_model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext
widget:
- source_sentence: 1-bromo-1-chloro-2,2,2-trifluoroethane [SEP] ious degrees. Both
compounds are metabolised in the same way as 1-bromo-1-chloro-2,2,2-trifluoroethane
(halothane) to form reactive trifluoroacetyl halide intermediat
sentences:
- 'Nephrotic Syndrome [SEP] A condition characterized by severe PROTEINURIA, greater
than 3.5 g/day in an average adult. The substantial loss of protein in '
- "Personality Disorders [SEP] A major deviation from normal patterns of behavior.\n\
\ "
- "Propylene Glycol [SEP] A clear, colorless, viscous organic solvent and diluent\
\ used in pharmaceutical preparations.\n "
- source_sentence: bupivacaine [SEP] was to investigate the influence of calcium channel
blockers on bupivacaine-induced acute toxicity. For each of the three tested calcium
ch
sentences:
- "Bupivacaine [SEP] A widely used local anesthetic agent.\n "
- "Urinary Bladder Neoplasms [SEP] Tumors or cancer of the URINARY BLADDER.\n \
\ "
- Spondylarthropathies [SEP] Heterogeneous group of arthritic diseases sharing clinical
and radiologic features. They are associated with the HLA-B27 ANTIGEN
- source_sentence: 'proteinuria [SEP] and an increase in fractional Li excretion.
Lithium also caused proteinuria and systolic hypertension in absence of glomerulosclerosis.
HP '
sentences:
- "Levofloxacin [SEP] The L-isomer of Ofloxacin.\n "
- Gastroesophageal Reflux [SEP] Retrograde flow of gastric juice (GASTRIC ACID)
and/or duodenal contents (BILE ACIDS; PANCREATIC JUICE) into the distal ESOPHAGU
- Carcinoma, Hepatocellular [SEP] A primary malignant neoplasm of epithelial liver
cells. It ranges from a well-differentiated tumor with EPITHELIAL CELLS indisti
- source_sentence: 'radiculopathy [SEP] OBJECTIVE: Conventional treatment methods
of lumbusacral radiculopathy are physical therapy, epidural steroid injections,
oral medicat'
sentences:
- Seizures [SEP] Clinical or subclinical disturbances of cortical function due to
a sudden, abnormal, excessive, and disorganized discharge of br
- Desipramine [SEP] A tricyclic dibenzazepine compound that potentiates neurotransmission.
Desipramine selectively blocks reuptake of norepinephrine
- Amphetamine [SEP] A powerful central nervous system stimulant and sympathomimetic.
Amphetamine has multiple mechanisms of action including blockin
- source_sentence: Death [SEP] Death from chemotherapy in gestational trophoblastic
disease.
sentences:
- Coma [SEP] A profound state of unconsciousness associated with depressed cerebral
activity from which the individual cannot be aroused. Com
- Neurotoxicity Syndromes [SEP] Neurologic disorders caused by exposure to toxic
substances through ingestion, injection, cutaneous application, or other method
- Vascular Diseases [SEP] Pathological processes involving any of the BLOOD VESSELS
in the cardiac or peripheral circulation. They include diseases of ART
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on cambridgeltl/SapBERT-from-PubMedBERT-fulltext
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext). 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:** [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
(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})
)
```
## 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("Stevenf232/context_fine-tuned-SapBERT1")
# Run inference
sentences = [
'Death [SEP] Death from chemotherapy in gestational trophoblastic disease.',
'Neurotoxicity Syndromes [SEP] Neurologic disorders caused by exposure to toxic substances through ingestion, injection, cutaneous application, or other method',
'Coma [SEP] A profound state of unconsciousness associated with depressed cerebral activity from which the individual cannot be aroused. Com',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5542, 0.6546],
# [0.5542, 1.0000, 0.4659],
# [0.6546, 0.4659, 1.0000]])
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 27,120 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: 9 tokens
- mean: 29.3 tokens
- max: 82 tokens
| - min: 4 tokens
- mean: 24.34 tokens
- max: 40 tokens
| - min: 0.0
- mean: 0.19
- max: 1.0
|
* Samples:
| sentence_0 | sentence_1 | label |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| prolactinomas [SEP] l prolactin greater than 20 ng./ml. in 1.86% of 1,821 patients, prolactinomas in 7, 0.38%). Bromocriptine was definitely effective in cases w | Nicotine [SEP] Nicotine is highly toxic alkaloid. It is the prototypical agonist at nicotinic cholinergic receptors where it dramatically stimu | 0.0 |
| acetazolamide [SEP] reatment for periodic paralysis and myotonia. Three patients on acetazolamide (15%) developed renal calculi. Extracorporeal lithotripsy succe | Neutropenia [SEP] A decrease in the number of NEUTROPHILS found in the blood.
| 0.0 |
| methylergonovine [SEP] Effect of direct intracoronary administration of methylergonovine in patients with and without variant angina. | Methylergonovine [SEP] A homolog of ERGONOVINE containing one more CH2 group. (Merck Index, 11th ed)
| 1.0 |
* Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `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`: None
- `warmup_ratio`: None
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: True
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `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
- `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
- `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_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `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
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}