SentenceTransformer based on NeuML/pubmedbert-base-embeddings
This is a sentence-transformers model finetuned from NeuML/pubmedbert-base-embeddings on the cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation dataset. It maps sentences & paragraphs to a 1024-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: NeuML/pubmedbert-base-embeddings
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 dimensions
- Similarity Function: Cosine Similarity
- Training Dataset:
- Language: code
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): MMContextEncoder(
(text_encoder): BertModel(
(embeddings): BertEmbeddings(
(word_embeddings): Embedding(30522, 768, padding_idx=0)
(position_embeddings): Embedding(512, 768)
(token_type_embeddings): Embedding(2, 768)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): BertEncoder(
(layer): ModuleList(
(0-11): 12 x BertLayer(
(attention): BertAttention(
(self): BertSdpaSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): BertPooler(
(dense): Linear(in_features=768, out_features=768, bias=True)
(activation): Tanh()
)
)
(text_adapter): AdapterModule(
(net): Sequential(
(0): Linear(in_features=768, out_features=512, bias=True)
(1): ReLU(inplace=True)
(2): Linear(in_features=512, out_features=1024, bias=True)
(3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(pooling): Pooling({'word_embedding_dimension': 1024, '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 ๐ค Hub
model = SentenceTransformer("jo-mengr/mmcontext-pubmedbert-geneformer-100k_adapter")
# Run inference
sentences = [
'sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_14644',
"This measurement was conducted with 10x 5' v2. Sample is a CD8-positive, alpha-beta memory T cell, specifically a cytotoxic T cell, from the lamina propria tissue of an individual in her eighth decade of life.",
"This measurement was conducted with 10x 3' v3. Classical monocytes derived from the blood of a female individual in her seventies.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, -0.2246, -0.1095],
# [-0.2246, 1.0000, 0.9513],
# [-0.1095, 0.9513, 1.0000]])
Evaluation
Metrics
Triplet
- Dataset:
cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1
- Evaluated with
TripletEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.5163 |
Training Details
Training Dataset
cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation
- Dataset: cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation at 9916878
- Size: 81,143 training samples
- Columns:
anchor
,positive
,negative_1
, andnegative_2
- Approximate statistics based on the first 1000 samples:
anchor positive negative_1 negative_2 type string string string string details - min: 56 characters
- mean: 58.72 characters
- max: 60 characters
- min: 92 characters
- mean: 216.13 characters
- max: 900 characters
- min: 101 characters
- mean: 215.14 characters
- max: 870 characters
- min: 56 characters
- mean: 58.75 characters
- max: 60 characters
- Samples:
anchor positive negative_1 negative_2 sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_26009
This measurement was conducted with 10x 3' v2. A proliferating lymphocyte cell sample, obtained from a 34-year-old female Asian individual, derived from peripheral blood mononuclear cells.
This measurement was conducted with 10x 3' v2. Sample is a 25-year-old female with European ethnicity, having CD8-positive, alpha-beta T cell type. This cell type exhibits elevated expression of type 1 interferon-stimulated genes (ISGs) in monocytes, reduction of naรฏve CD4+ T cells correlating with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells.
sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_14165
sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_6333
This measurement was conducted with 10x 5' v1. Sample is a cell from the omentum tissue, specifically an effector memory CD4-positive, alpha-beta T cell, from a female in her sixth decade.
This measurement was conducted with 10x 5' v2. Conventional dendritic cell from the jejunal epithelium of a female in her eighth decade.
sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_2714
sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_271
This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male, specifically from the thalamic complex, specifically the thalamus (THM) - posterior nuclear complex of thalamus (PoN) - medial geniculate nuclei (MG).
This measurement was conducted with 10x 3' v3. Neuron from the thalamic complex (thalamus, posterior nuclear complex of thalamus, medial geniculate nuclei) of a 42-year-old male, identified as a midbrain-derived inhibitory neuron.
sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_425
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Evaluation Dataset
cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation
- Dataset: cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation at 9916878
- Size: 9,011 evaluation samples
- Columns:
anchor
,positive
,negative_1
, andnegative_2
- Approximate statistics based on the first 1000 samples:
anchor positive negative_1 negative_2 type string string string string details - min: 56 characters
- mean: 58.73 characters
- max: 60 characters
- min: 99 characters
- mean: 209.99 characters
- max: 941 characters
- min: 102 characters
- mean: 213.87 characters
- max: 981 characters
- min: 56 characters
- mean: 58.73 characters
- max: 60 characters
- Samples:
anchor positive negative_1 negative_2 sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_490
This measurement was conducted with 10x 3' v3. Cell sample from the cortex of kidney, taken from a 43-year-old male of European ethnicity with a reported history of kidney cancer. The cell type is identified as a kidney collecting duct intercalated cell.
This measurement was conducted with 10x 3' v3. Kidney collecting duct intercalated cell from a 43-year old European male with kidney cancer, taken from the cortex of kidney and cryopreserved for further analysis.
sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_9
sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_269
This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male cerebellum, specifically from the Cerebellar Vermis - CBV region, with European self-reported ethnicity, analyzed at the nucleus level.
This measurement was conducted with 10x 3' v3. Endothelial cells derived from the cerebellum (specifically, cerebellar vermis) of a 42-year-old male, classified under the vascular supercluster term.
sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_923
sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_10258
This measurement was conducted with 10x 5' v1. Cell sample from the tonsil of a 9-year-old female with recurrent tonsillitis, characterized as a centroblast B cell with IGLC2, IGLV7-43, IGLJ3 immunoglobulin genes expressed.
This measurement was conducted with 10x 5' v1. Centroblast cells derived from a 3-year-old male human tonsil sample, with obstructive sleep apnea and recurrent tonsillitis, undergoing affinity maturation and differentiation into memory or plasma cells.
sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_9654
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 256per_device_eval_batch_size
: 256learning_rate
: 0.05num_train_epochs
: 2warmup_ratio
: 0.1bf16
: Truegradient_checkpointing
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 256per_device_eval_batch_size
: 256per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 0.05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 2max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsehub_revision
: Nonegradient_checkpointing
: Truegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseliger_kernel_config
: Noneeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportionalrouter_mapping
: {}learning_rate_mapping
: {}
Training Logs
Epoch | Step | Training Loss | cellxgene pseudo bulk 100k multiplets natural language annotation loss | cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_cosine_accuracy |
---|---|---|---|---|
0.3155 | 100 | 4.3009 | 20.4535 | 0.5063 |
0.6309 | 200 | 3.2356 | 22.4190 | 0.5055 |
0.9464 | 300 | 2.9358 | 19.8626 | 0.5072 |
1.2618 | 400 | 2.7478 | 19.9669 | 0.5104 |
1.5773 | 500 | 2.634 | 18.4317 | 0.5134 |
1.8927 | 600 | 2.554 | 17.2588 | 0.5163 |
Framework Versions
- Python: 3.11.6
- Sentence Transformers: 5.0.0
- Transformers: 4.55.0.dev0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.9.0
- Datasets: 2.19.1
- Tokenizers: 0.21.4
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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Model tree for jo-mengr/mmcontext-pubmedbert-geneformer-100k_adapter
Finetuned
NeuML/pubmedbert-base-embeddings
Evaluation results
- Cosine Accuracy on cellxgene pseudo bulk 100k multiplets natural language annotation cell sentence 1self-reported0.516