SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2
Maximum Sequence Length: 256 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': 256, 'do_lower_case': False}) with Transformer model: 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})
  (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("jaimevera1107/all-MiniLM-L12-v2-pubmed")
# Run inference
sentences = [
    "What were the findings of the study on cyclic 3',5'-nucleotide phosphodiesterase in bovine thyroid regarding its activity and the factors influencing it?",
    "The study investigated the properties of cyclic 3',5'-nucleotide phosphodiesterase in bovine thyroid, revealing that its activity is stimulated by Mg2+ and requires a Ca2+-dependent activating factor, with distinct enzyme forms and kinetic behaviors observed.",
    '[The kinetics of lithium in the rat serum, brain and liver]. The kinetics of lithium in the serum, liver and brain of rats is described. The serum levels resembled those of man, whereas considerable quantitative differences were observed when comparing specific kinetic parameters. The brain level increased with the increasing doses, approaching the corresponding serum level. Concentration differences between different brain areas could be observed only after repeated administrations. Striatum, cortex and hippocampus showed significantly higher levels than the thalamus. The liver content remained low with increasing doses, and was below the brain level.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Training Details

Training Dataset

Unnamed Dataset

Size: 67,560 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: 7 tokens
mean: 74.75 tokens
max: 256 tokens

min: 6 tokens
mean: 59.33 tokens
max: 256 tokens

min: 0.0
mean: 0.68
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 7 tokens mean: 74.75 tokens max: 256 tokens	min: 6 tokens mean: 59.33 tokens max: 256 tokens	min: 0.0 mean: 0.68 max: 1.0

Samples:

sentence_0	sentence_1	label
`What were the outcomes for asthmatic patients in a trial of indoramin regarding airflow improvement and migraine frequency reduction?`	Long-term trial of an alpha adrenoceptor blocking drug (Indoramin) in asthma. A preliminary report. Eight patients suffering from both asthma and migraine underwent a clinical trial for 3 months of indoramin, an alpha adrenoceptor antagonist with antihistamine and antiserotonin activity. Patients were told indoramin was prescribed for migraine prophylaxis. In three asthmatic patients there was a marked increase in airflow meter (AFM) readings which were recorded daily, the remaining five showing no significant change or a decrease in AFM readings. Indoramin did not appear to potentiate the action of the beta sympathomimetic aerosols. It is suggested that a small population of asthmatic patients may derive therapeutic benefit from an alpha adrenoceptor antagonist. Seven of the eight patients experienced a 50% decrease in the frequency of their migraine headaches.	`0.5`
The ontogeny of L-alpha-hydroxyacid oxidase isozymes in the mouse. Mouse liver hydroxyacid oxidase isozymes are present at low levels at birth and increase in activity until day 13 after which HAOX-B almost disappears and HZOS-A is reduced to approximately one half the maximum level in the adultkidney. HAOX-G appears near day 13 post partum and increases until adult levels are reached, the female having four times the activity of the male. The pregnant female has significantly lower levels of HAOX-A and HAOX-B in the liver and higher activity of HAOX-B in the kidney. Developmental changes occur in the extent of epigenetic modification of mouse liver HAOX-A during the early neonatal period.	`In mice, liver hydroxyacid oxidase isozymes show developmental changes, with HAOX-B decreasing after day 13 and HAOX-G increasing, while pregnant females exhibit lower HAOX-A and HAOX-B levels in the liver but higher HAOX-B activity in the kidney.`	`1.0`
`What were the findings regarding renal inflammation and leptospires in a study of striped skunks from Louisiana?`	`In a study of 100 striped skunks from Louisiana, 50% exhibited renal inflammation, and 10% with severe lesions showed azotemia, while leptospires were cultured from 30% of the skunks.`	`0.5`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 4
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
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
num_train_epochs: 4
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: 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
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
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.2367	500	0.0659
0.4735	1000	0.042
0.7102	1500	0.0351
0.9470	2000	0.0328
1.1837	2500	0.0291
1.4205	3000	0.0269
1.6572	3500	0.0265
1.8939	4000	0.026
2.1307	4500	0.0245
2.3674	5000	0.0231
2.6042	5500	0.0219
2.8409	6000	0.0229
3.0777	6500	0.0227
3.3144	7000	0.0206
3.5511	7500	0.02
3.7879	8000	0.0201

Framework Versions

Python: 3.11.9
Sentence Transformers: 4.1.0
Transformers: 4.52.3
PyTorch: 2.7.0+cu118
Accelerate: 1.7.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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",
}