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---
language:
- code
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:282074
- loss:MultipleNegativesRankingLoss
base_model: NeuML/pubmedbert-base-embeddings
widget:
- source_sentence: ABCB7
sentences:
- This gene encodes a tetrameric mitochondrial flavoprotein, which is a member of
the acyl-CoA dehydrogenase family. This enzyme catalyzes the initial step of the
mitochondrial fatty acid beta-oxidation pathway. Mutations in this gene have been
associated with short-chain acyl-CoA dehydrogenase (SCAD) deficiency. Alternative
splicing results in two variants which encode different isoforms. [provided by
RefSeq, Oct 2014]
- The membrane-associated protein encoded by this gene is a member of the superfamily
of ATP-binding cassette (ABC) transporters. ABC proteins transport various molecules
across extra- and intra-cellular membranes. ABC genes are divided into seven distinct
subfamilies (ABC1, MDR/TAP, MRP, ALD, OABP, GCN20, White). This protein is a member
of the MDR/TAP subfamily. Members of the MDR/TAP subfamily are involved in multidrug
resistance as well as antigen presentation. This gene encodes a half-transporter
involved in the transport of heme from the mitochondria to the cytosol. With iron/sulfur
cluster precursors as its substrates, this protein may play a role in metal homeostasis.
Mutations in this gene have been associated with mitochondrial iron accumulation
and isodicentric (X)(q13) and sideroblastic anemia. Alternatively spliced transcript
variants encoding multiple isoforms have been observed for this gene. [provided
by RefSeq, Nov 2012]
- The membrane-associated protein encoded by this gene is a member of the superfamily
of ATP-binding cassette (ABC) transporters. ABC proteins transport various molecules
across extra- and intracellular membranes. ABC genes are divided into seven distinct
subfamilies (ABC1, MDR/TAP, MRP, ALD, OABP, GCN20, and White). This encoded protein
is a member of the ABC1 subfamily. Members of the ABC1 subfamily comprise the
only major ABC subfamily found exclusively in multicellular eukaryotes. This gene
is clustered among 4 other ABC1 family members on 17q24, but neither the substrate
nor the function of this gene is known. Alternative splicing of this gene results
in several transcript variants; however, not all variants have been fully described.
[provided by RefSeq, Jul 2008]
- source_sentence: ABCC8
sentences:
- The protein encoded by this gene is a member of the superfamily of ATP-binding
cassette (ABC) transporters. ABC proteins transport various molecules across extra-
and intra-cellular membranes. ABC genes are divided into seven distinct subfamilies
(ABC1, MDR/TAP, MRP, ALD, OABP, GCN20, White). This protein is a member of the
MRP subfamily which is involved in multi-drug resistance. This protein functions
as a modulator of ATP-sensitive potassium channels and insulin release. Mutations
in the ABCC8 gene and deficiencies in the encoded protein have been observed in
patients with hyperinsulinemic hypoglycemia of infancy, an autosomal recessive
disorder of unregulated and high insulin secretion. Mutations have also been associated
with non-insulin-dependent diabetes mellitus type II, an autosomal dominant disease
of defective insulin secretion. Alternatively spliced transcript variants have
been found for this gene. [provided by RefSeq, Jul 2020]
- Predicted to enable GTPase activator activity and zinc ion binding activity. Predicted
to be involved in protein transport. Located in membrane. [provided by Alliance
of Genome Resources, Jul 2025]
- The protein encoded by this gene is a member of the superfamily of ATP-binding
cassette (ABC) transporters. ABC proteins transport various molecules across extra-
and intra-cellular membranes. ABC genes are divided into seven distinct subfamilies
(ABC1, MDR/TAP, MRP, ALD, OABP, GCN20, White). This ABC full transporter is a
member of the MRP subfamily which is involved in multi-drug resistance. The product
of this gene participates in physiological processes involving bile acids, conjugated
steroids, and cyclic nucleotides. In addition, a SNP in this gene is responsible
for determination of human earwax type. This gene and family member ABCC12 are
determined to be derived by duplication and are both localized to chromosome 16q12.1.
Multiple alternatively spliced transcript variants have been described for this
gene. [provided by RefSeq, Jul 2008]
- source_sentence: sample_idx:census_cf83c98a-3791-4537-bbde-a719f6d73c13_866
sentences:
- sample_idx:census_cf83c98a-3791-4537-bbde-a719f6d73c13_11
- This measurement was conducted with 10x 3' v3. Sample is a 10-month old male with
mature NK T cells, specifically CD16+ NK cells, found in blood.
- This measurement was conducted with 10x 3' v3. Blast cells derived from a 1-month-old
human with a presumed MLL-AF10 fusion, projected as cDC-like cells.
- source_sentence: sample_idx:census_9a64bf99-ebe5-4276-93a8-bee9dff1cd47_235
sentences:
- sample_idx:census_9a64bf99-ebe5-4276-93a8-bee9dff1cd47_740
- This measurement was conducted with 10x 3' v2. Fibroblast cells derived from breast
tissue of a 35-year old European female who has undergone prophylactic surgery.
The individual has two pregnancies and two births, and carries the BRCA1 genotype.
- This measurement was conducted with 10x 3' v2. Leukocyte subtype from breast tissue
of a 65-year old female with European self-reported ethnicity, post-menopausal
status, and BRCA2 genotype.
- source_sentence: MALAT1 EEF1A1 FTH1 JUND TPT1 EIF1 NFKBIA GAPDH JUN FOS FTL H3-3B
ZFAS1 PTMA VIM FAU NACA RACK1 PNRC1 CEBPB DUSP1 UBC EIF4A1 TMSB4X CD44 PPP1R15A
HSP90AB1 NEAT1 TNFAIP3 SAT1 BTF3 TM4SF1 NPM1 GADD45B ZFP36 CD63 IGFBP7 JUNB YBX1
HSP90AA1 LGALS1 SERF2 EPS8 EEF2 UBB SNHG8 TMSB10 YBX3 CD59 HES1 EGR1 SLC38A2 HNRNPDL
IER2 ANXA5 ID4 NME2 EEF1G SLC25A5 KLF6 LAPTM4A PABPC1 SLC25A3 HNRNPC
sentences:
- This measurement was conducted with 10x 3' v3. Mural cells derived from a 45-year
old female breast (organoid) tissue, following prophylactic mastectomy.
- This measurement was conducted with 10x 3' v3. Supernatant sample containing fibroblasts
from the mammary gland of a 36-year-old female who underwent prophylactic mastectomy.
- MALAT1 FTH1 S100A6 SAT1 EEF1A1 TMSB10 TPT1 MGP ACTB NEAT1 PTMA TMSB4X NFKBIA JUND
NACA H3-3B EIF1 S100A11 FAU UBC FTL MYL6 GAPDH RACK1 TM4SF1 ZFAS1 MYL12B CEBPD
SOX4 H1-10 S100A10 DSTN SERF2 ANXA2 ZFP36L1 KLF6 HSP90AB1 GADD45B CD63 ARHGAP29
IER2 MGST1 HSP90AA1 CD59 JUN PPIA EEF1G GADD45A BTF3 NME2 CD9 MYL12A CHCHD2 LGALS3
EIF4A1 GNAS HSPB1 PPDPF COX4I1 BTG1 TUBA1A OAZ1 SRP14 CALM2
datasets:
- jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation
- jo-mengr/descriptions_genes
- jo-mengr/descriptions_cell_types
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on NeuML/pubmedbert-base-embeddings
results:
- task:
type: triplet
name: Triplet
dataset:
name: cellxgene pseudo bulk 100k multiplets natural language annotation cell
sentence 1 caption
type: cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption
metrics:
- type: cosine_accuracy
value: 0.8082343935966492
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: cellxgene pseudo bulk 100k multiplets natural language annotation cell
sentence 2 caption
type: cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_caption
metrics:
- type: cosine_accuracy
value: 0.529574990272522
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: gene description
type: gene_description
metrics:
- type: cosine_accuracy
value: 0.1589999943971634
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: cell type description
type: cell_type_description
metrics:
- type: cosine_accuracy
value: 0.7929999828338623
name: Cosine Accuracy
---
# SentenceTransformer based on NeuML/pubmedbert-base-embeddings
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) on 4 datasets. 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:** [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) <!-- at revision d6eaca8254bc229f3ca42749a5510ae287eb3486 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation)
- [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation)
- [gene_description](https://huggingface.co/datasets/jo-mengr/descriptions_genes)
- [cell_type_description](https://huggingface.co/datasets/jo-mengr/descriptions_cell_types)
- **Language:** code
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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): Identity()
)
(pooling): 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})
(omics_adapter): AdapterModule(
(net): Sequential(
(0): Linear(in_features=3936, out_features=768, bias=True)
(1): BatchNorm1d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(omics_encoder): MiniOmicsModel(
(embeddings): Embedding(90155, 3936, padding_idx=0)
)
)
)
```
## 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("jo-mengr/mmcontext-pubmedbert-gs-mixed-num_adapter")
# Run inference
sentences = [
'MALAT1 EEF1A1 FTH1 JUND TPT1 EIF1 NFKBIA GAPDH JUN FOS FTL H3-3B ZFAS1 PTMA VIM FAU NACA RACK1 PNRC1 CEBPB DUSP1 UBC EIF4A1 TMSB4X CD44 PPP1R15A HSP90AB1 NEAT1 TNFAIP3 SAT1 BTF3 TM4SF1 NPM1 GADD45B ZFP36 CD63 IGFBP7 JUNB YBX1 HSP90AA1 LGALS1 SERF2 EPS8 EEF2 UBB SNHG8 TMSB10 YBX3 CD59 HES1 EGR1 SLC38A2 HNRNPDL IER2 ANXA5 ID4 NME2 EEF1G SLC25A5 KLF6 LAPTM4A PABPC1 SLC25A3 HNRNPC',
"This measurement was conducted with 10x 3' v3. Mural cells derived from a 45-year old female breast (organoid) tissue, following prophylactic mastectomy.",
"This measurement was conducted with 10x 3' v3. Supernatant sample containing fibroblasts from the mammary gland of a 36-year-old female who underwent prophylactic mastectomy.",
]
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, 0.0205, 0.0425],
# [0.0205, 1.0000, 0.8263],
# [0.0425, 0.8263, 1.0000]])
```
<!--
### 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.*
-->
## Evaluation
### Metrics
#### Triplet
* Datasets: `cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption`, `cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_caption`, `gene_description` and `cell_type_description`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption | cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_caption | gene_description | cell_type_description |
|:--------------------|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-----------------|:----------------------|
| **cosine_accuracy** | **0.8082** | **0.5296** | **0.159** | **0.793** |
<!--
## 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 Datasets
<details><summary>cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption</summary>
#### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption
* Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [b141493](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/b141493854960a0e33c4583cab3c497379c1f8f0)
* Size: 81,143 training samples
* Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative_1 | negative_2 |
|:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
| type | string | string | string | string |
| details | <ul><li>min: 56 characters</li><li>mean: 58.72 characters</li><li>max: 60 characters</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 48.4 tokens</li><li>max: 159 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 48.65 tokens</li><li>max: 158 tokens</li></ul> | <ul><li>min: 56 characters</li><li>mean: 58.75 characters</li><li>max: 60 characters</li></ul> |
* Samples:
| anchor | positive | negative_1 | negative_2 |
|:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|
| <code>sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_26009</code> | <code>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.</code> | <code>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 naïve CD4+ T cells correlating with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells.</code> | <code>sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_14165</code> |
| <code>sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_6333</code> | <code>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.</code> | <code>This measurement was conducted with 10x 5' v2. Conventional dendritic cell from the jejunal epithelium of a female in her eighth decade.</code> | <code>sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_2714</code> |
| <code>sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_271</code> | <code>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).</code> | <code>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.</code> | <code>sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_425</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_caption</summary>
#### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_caption
* Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [f099f27](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/f099f27823d7287fe85e5fb7fa56ed56e8ec5a73)
* Size: 81,143 training samples
* Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative_1 | negative_2 |
|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| type | string | string | string | string |
| details | <ul><li>min: 135 tokens</li><li>mean: 152.0 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 48.4 tokens</li><li>max: 159 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 48.65 tokens</li><li>max: 158 tokens</li></ul> | <ul><li>min: 135 tokens</li><li>mean: 151.82 tokens</li><li>max: 181 tokens</li></ul> |
* Samples:
| anchor | positive | negative_1 | negative_2 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>TMSB4X TMSB10 ACTB MALAT1 GNLY NKG7 IFITM2 LGALS1 GZMA EEF1A1 PFN1 HMGB2 FTH1 PTMA HSP90AA1 GZMB ARHGDIB HNRNPA2B1 PLAAT4 FAU CMC1 VIM MYL12A CBX3 ATP5F1E HCST IFI44L KLRF1 H3-3A COX6C ARL6IP1 CFL1 ISG15 HMGB1 S100A4 ATP5MF RORA MYL6 CORO1A OAZ1 KLRB1 ID2 HMGN3 CCNI RBM39 CAP1 SERF2 ELOC FCER1G S100A9 IFI16 YWHAZ EIF1 CALR HMGN2 SKAP2 SLC25A5 ZZZ3 YBX1 NUCB2 CDC42 GSTP1 FTL ATP5F1D</code> | <code>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.</code> | <code>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 naïve CD4+ T cells correlating with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells.</code> | <code>MALAT1 TMSB4X EEF1A1 CD74 BTG1 PTMA TMSB10 TPT1 FAU EIF1 FTH1 FTL CXCR4 TSC22D3 DUSP1 UBA52 ACTB CD37 CD52 NACA RACK1 EZR CD69 LAPTM5 H3-3A FOS ISG20 YBX1 CIRBP EIF3E OAZ1 COX7C SAT1 COX4I1 H3-3B SH3BGRL3 UBC UBB JUNB COMMD6 VIM CYBA KLF6 STK17B FUS HNRNPC MYL6 GADD45B LGALS1 EIF3L SRSF5 NFKBIA ANKRD12 CORO1A TLE5 NOP53 CHCHD2 PFN1 DDX5 ARPC3 COX7A2 YPEL5 ARL4A SRGN</code> |
| <code>EEF1A1 MALAT1 FTH1 JUNB TPT1 FOS TMSB10 BTG1 TMSB4X ZFP36L2 NACA PABPC1 ACTB FAU VIM H3-3B EIF1 ZFP36 SARAF PTMA IL7R JUN RACK1 EEF2 UBA52 GAPDH FTL FXYD5 DUSP1 S100A4 CD69 CXCR4 UBC TSC22D3 CFL1 KLF6 ARHGDIB KLF2 BTG2 CITED2 IER2 TUBB4B CD3E EEF1G SLC2A3 NFKBIA PFN1 SRGN SNX9 COX4I1 DNAJB1 SERF2 CD8A PCBP2 IL32 BIRC3 SMAP2 FUS GADD45B MYL12A OAZ1 ATP5F1E TUBA4A PNRC1</code> | <code>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.</code> | <code>This measurement was conducted with 10x 5' v2. Conventional dendritic cell from the jejunal epithelium of a female in her eighth decade.</code> | <code>CD74 MALAT1 EEF1A1 FOS TPT1 TMSB4X TMSB10 ACTB FAU JUN CD37 DUSP1 RACK1 JUNB EIF1 PTMA FTL DNAJB1 H3-3B CD52 NACA BTG1 TSC22D3 FTH1 PABPC1 EEF2 UBA52 EEF1G HSP90AA1 LAPTM5 CYBA PPP1R15A HSP90AB1 CD69 ARHGDIB ZFP36 SERF2 UBC H3-3A PCBP2 HLA-DRB5 KLF6 PFN1 DDX5 HSPA8 ARPC3 CD83 CCNI CXCR4 ATP5F1E SARAF TUBA1A ZFP36L1 TOMM7 HERPUD1 YBX1 RHOA MEF2C FXYD5 MYL6 SRSF5 MYL12A CORO1A OAZ1</code> |
| <code>MALAT1 GRIK1 SYT1 PCDH9 RORA NRG1 CADPS ZFPM2 LRRC4C LINGO2 RALYL PTPRD SPHKAP CNTNAP5 SLC8A1 CCSER1 HDAC9 CELF2 R3HDM1 CNTN4 RBMS3 PCDH7 GALNT13 UNC5D ROBO1 SYNPR SNAP25 GPM6A ANK3 FRMPD4 CHRM2 RYR2 KHDRBS2 CADM1 CACNA1D RGS6 PDE4D DOCK4 UNC13C CDH18 FAT3 MEG3 NR2F2-AS1 HMCN1 GULP1 CAMK2D ZEB1 SYN2 DYNC1I1 OXR1 DPP10 OSBPL6 FRAS1 PPP3CA ZNF385D ZMAT4 PCBP3 HS6ST3 ERC2 PLEKHA5 CDK14 MAP2 NCOA1 ATP8A2</code> | <code>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).</code> | <code>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.</code> | <code>MALAT1 PCDH9 PTPRD NRG1 SYT1 DPP10 ROBO1 TENM2 LRRC4C RBMS3 CNTNAP5 LINGO2 CDH18 SLC8A1 DMD PDE4D RYR2 ATP1B1 RGS6 PTPRT CHRM3 ADGRL2 NOVA1 NTNG1 PCDH7 TAFA2 CCSER1 ANK3 MEG3 MAP2 PLCB4 CACNA2D1 PRKG1 LINC03000 RMST RORA FOXP2 LHFPL3 MEG8 TNRC6A DAB1 KCTD8 RALYL GNAS INPP4B OLFM3 CNTN4 FRMD4A LINC00632 GAPDH ENOX1 AHI1 GPM6A EBF1 LRFN5 PCSK1N SEMA5A KIAA1217 CALY MAP1B SNAP25 GABRB2 CDH8 GRIP1</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>gene_description</summary>
#### gene_description
* Dataset: [gene_description](https://huggingface.co/datasets/jo-mengr/descriptions_genes) at [dd22363](https://huggingface.co/datasets/jo-mengr/descriptions_genes/tree/dd22363de0a7c501f41ba324fb3b8d6ecdd14dc7)
* Size: 116,208 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative_1 |
|:--------|:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 4.74 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 72.99 tokens</li><li>max: 247 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 37.82 tokens</li><li>max: 247 tokens</li></ul> |
* Samples:
| anchor | positive | negative_1 |
|:------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
| <code>A1BG</code> | <code>The protein encoded by this gene is a plasma glycoprotein of unknown function. The protein shows sequence similarity to the variable regions of some immunoglobulin supergene family member proteins. [provided by RefSeq, Jul 2008]</code> | <code>A1BG antisense RNA 1</code> |
| <code>A1BG</code> | <code>The protein encoded by this gene is a plasma glycoprotein of unknown function. The protein shows sequence similarity to the variable regions of some immunoglobulin supergene family member proteins. [provided by RefSeq, Jul 2008]</code> | <code>G antigen 12D</code> |
| <code>A1BG</code> | <code>The protein encoded by this gene is a plasma glycoprotein of unknown function. The protein shows sequence similarity to the variable regions of some immunoglobulin supergene family member proteins. [provided by RefSeq, Jul 2008]</code> | <code>G antigen 12B</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>cell_type_description</summary>
#### cell_type_description
* Dataset: [cell_type_description](https://huggingface.co/datasets/jo-mengr/descriptions_cell_types) at [eaa8e6b](https://huggingface.co/datasets/jo-mengr/descriptions_cell_types/tree/eaa8e6b6f7eaef0decabf775c6c51d129733f0b9)
* Size: 3,580 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative_1 |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 6.52 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 35.89 tokens</li><li>max: 188 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 35.79 tokens</li><li>max: 188 tokens</li></ul> |
* Samples:
| anchor | positive | negative_1 |
|:------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>A2 amacrine cell</code> | <code>A bistratifed retinal amacrine cell with a small dendritic field, dendrite stratification in S1-S2, and a second dendrite stratification in S5. This cell type releases the neurotransmitter glycine.</code> | <code>An amacrine cell with a flat dendritic arbor and a medium dendritic field. Starburst amacrine cells have post-synaptic terminals in S2. This cell type releases the neurotransmitters gamma-aminobutyric acid (GABA) and acetylcholine.</code> |
| <code>A2 amacrine cell</code> | <code>A bistratifed retinal amacrine cell with a small dendritic field, dendrite stratification in S1-S2, and a second dendrite stratification in S5. This cell type releases the neurotransmitter glycine.</code> | <code>An ON diffuse bipolar cell that predominantly connects to ON parasol cells and lateral amacrine cells.</code> |
| <code>A2 amacrine cell</code> | <code>A bistratifed retinal amacrine cell with a small dendritic field, dendrite stratification in S1-S2, and a second dendrite stratification in S5. This cell type releases the neurotransmitter glycine.</code> | <code>An amacrine cell that uses GABA as a neurotransmitter.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
### Evaluation Datasets
<details><summary>cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption</summary>
#### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption
* Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [b141493](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/b141493854960a0e33c4583cab3c497379c1f8f0)
* Size: 9,011 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative_1 | negative_2 |
|:--------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
| type | string | string | string | string |
| details | <ul><li>min: 56 characters</li><li>mean: 58.73 characters</li><li>max: 60 characters</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 47.49 tokens</li><li>max: 157 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 48.43 tokens</li><li>max: 206 tokens</li></ul> | <ul><li>min: 56 characters</li><li>mean: 58.73 characters</li><li>max: 60 characters</li></ul> |
* Samples:
| anchor | positive | negative_1 | negative_2 |
|:--------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|
| <code>sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_490</code> | <code>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.</code> | <code>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.</code> | <code>sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_9</code> |
| <code>sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_269</code> | <code>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.</code> | <code>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.</code> | <code>sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_923</code> |
| <code>sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_10258</code> | <code>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.</code> | <code>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.</code> | <code>sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_9654</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_caption</summary>
#### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_caption
* Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [f099f27](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/f099f27823d7287fe85e5fb7fa56ed56e8ec5a73)
* Size: 9,011 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative_1 | negative_2 |
|:--------|:--------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string | string |
| details | <ul><li>min: 134 tokens</li><li>mean: 152.94 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 47.49 tokens</li><li>max: 157 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 48.43 tokens</li><li>max: 206 tokens</li></ul> | <ul><li>min: 137 tokens</li><li>mean: 152.7 tokens</li><li>max: 178 tokens</li></ul> |
* Samples:
| anchor | positive | negative_1 | negative_2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>MALAT1 EEF1A1 FTH1 TMSB4X ACTB FTL RTN4 ATP6V0B TPT1 FAU S100A6 NDUFA4 ATP5F1E COX7C ITM2B IGFBP7 EIF1 C12orf75 CD9 COX7B SERF2 ATP1B1 COX8A TXNIP NDUFB2 MYL6 PPDPF COX6B1 UQCR11 APOE COX4I1 CALM2 UQCRB S100A11 UQCRQ COX6C ATP5MG BSG ATP6AP2 UQCR10 PTMA NACA UBL5 UBA52 TMSB10 ADGRF5 HSP90AA1 GSTP1 ATP5F1D CHCHD2 GAPDH COX7A2 SKP1 HSPE1 PRDX1 CYSTM1 LGALS3 CD63 ATP5MJ CKB NDUFS5 ATP5ME UBB MAL</code> | <code>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.</code> | <code>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.</code> | <code>MALAT1 EEF1A1 CRYAB S100A6 ITM2B ACTB TPT1 PTMA FTL PEBP1 H3-3B GSTP1 ADIRF IGFBP7 S100A10 HIPK2 MYL6 SERF2 TPM1 FAU FTH1 ID4 EIF1 TMSB10 HSP90AA1 SKP1 IGFBP2 IGFBP5 PRDX1 MYL12B CYSTM1 CLU ATP5F1E AHNAK PPDPF DSTN ID1 COX7C JUND SRP14 ATP1B1 HINT1 NDUFA4 PPIA NACA TMA7 NEAT1 CD9 SYNE2 LAPTM4A GNAS CIRBP ATP5F1D DDX17 EDF1 CCND1 LDHB RTN4 TMEM59 NR4A1 KTN1 SAT1 TMBIM6 APP</code> |
| <code>MALAT1 KCND2 NRXN1 CDH18 NRXN3 ZNF385D CADM2 RALYL NKAIN2 CADPS2 RIMS1 FSTL5 GRID2 TRPM3 CHN2 DPP6 JMJD1C RORA PDE1A UNC13C TIAM1 NRG1 SNAP25 ZFPM2 CALN1 LSAMP CNTN1 ABLIM1 SYNE1 ANK3 CA10 NFIA ZBTB20 NTM CADM1 OPCML RELN DNM3 NEBL ERC1 SCN2A PPP3CA CACNA1A GALNT13 LRRC4C GPM6A RABGAP1L RIT2 CAMK4 GRIA4 PTPRD RBFOX3 MCTP1 LHFPL6 PCLO MEG3 PDE10A NOVA1 RTN1 ZNF385B CNTN4 GABRB2 SPOCK1 OXR1</code> | <code>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.</code> | <code>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.</code> | <code>MALAT1 ATP10A COBLL1 GPCPD1 PTPRG SLC39A10 FLT1 FLI1 TSPAN5 THSD4 RUNDC3B CCNY IGFBP7 ST6GALNAC3 PRKCH ST6GAL1 MECOM ESYT2 TBC1D4 IGF1R TACC1 HERC4 CDH2 TCF4 ABCB1 DOCK9 SORBS2 USP54 CBFA2T2 TSC22D1 QKI EPAS1 APP NFIB AOPEP ELMO1 ZNF704 PTPRM NET1 A2M FGD6 EPHA3 NEBL RAPGEF2 ACVR1 SPTBN1 BBS9 KLF2 MKLN1 EXOC6 LEF1 PPP3CA RBMS3 LRMDA WDFY3 BCL2L1 TTC3 SIPA1L1 CFLAR ADGRF5 MAP4K4 SCARB1 RAPGEF4 ABLIM1</code> |
| <code>EEF1A1 ACTB GAPDH HMGN2 PTMA SERF2 TMSB4X CD74 PABPC1 FTH1 TMSB10 FAU PFN1 HMGN1 OAZ1 HMGB1 TPT1 PPIA NACA BTF3 MALAT1 MYL6 ATP5MG CFL1 RACK1 ODC1 ATP5F1E TMA7 SLC25A5 ELOB ARPC3 NPM1 COX7C ANP32B C4orf3 EIF1 PCBP2 KLF6 LAPTM5 COX8A RHOA HSPA8 H3-3B PTP4A2 UBA52 OST4 CIRBP LGALS1 EIF3L STMN1 PPDPF COX4I1 RAN EIF3F PPP1CC COMMD6 NDUFA4 YBX1 PEBP1 COTL1 COX7A2 HSPE1 CCNI TRIR</code> | <code>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.</code> | <code>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.</code> | <code>CD74 MALAT1 EEF1A1 ACTB TMSB4X LAPTM5 PTMA TPT1 TMSB10 CXCR4 FAU BTG1 TXNIP PABPC1 FTH1 NACA FTL IRF1 RBM3 CD83 CCNI SARAF BTF3 HNRNPA3 HLA-DRB5 UBA52 MEF2C CORO1A UBE2D3 ATP5F1E PDIA6 UBC GABARAP CFL1 CALR RACK1 HSPA5 EIF4B RHOA HNRNPC SRSF5 PFN1 HSPA8 CNOT2 IFT57 HNRNPA2B1 COX7C ITM2B SH3BGRL3 PNRC1 PDIA3 EEF2 UBB PARP14 SNX2 LAP3 SLC25A5 POU2F2 ADAM28 ZNF800 CYBA GDI2 STK17B EIF3I</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>gene_description</summary>
#### gene_description
* Dataset: [gene_description](https://huggingface.co/datasets/jo-mengr/descriptions_genes) at [dd22363](https://huggingface.co/datasets/jo-mengr/descriptions_genes/tree/dd22363de0a7c501f41ba324fb3b8d6ecdd14dc7)
* Size: 1,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative_1 |
|:--------|:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 4.74 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 72.99 tokens</li><li>max: 247 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 37.82 tokens</li><li>max: 247 tokens</li></ul> |
* Samples:
| anchor | positive | negative_1 |
|:------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
| <code>A1BG</code> | <code>The protein encoded by this gene is a plasma glycoprotein of unknown function. The protein shows sequence similarity to the variable regions of some immunoglobulin supergene family member proteins. [provided by RefSeq, Jul 2008]</code> | <code>A1BG antisense RNA 1</code> |
| <code>A1BG</code> | <code>The protein encoded by this gene is a plasma glycoprotein of unknown function. The protein shows sequence similarity to the variable regions of some immunoglobulin supergene family member proteins. [provided by RefSeq, Jul 2008]</code> | <code>G antigen 12D</code> |
| <code>A1BG</code> | <code>The protein encoded by this gene is a plasma glycoprotein of unknown function. The protein shows sequence similarity to the variable regions of some immunoglobulin supergene family member proteins. [provided by RefSeq, Jul 2008]</code> | <code>G antigen 12B</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>cell_type_description</summary>
#### cell_type_description
* Dataset: [cell_type_description](https://huggingface.co/datasets/jo-mengr/descriptions_cell_types) at [eaa8e6b](https://huggingface.co/datasets/jo-mengr/descriptions_cell_types/tree/eaa8e6b6f7eaef0decabf775c6c51d129733f0b9)
* Size: 1,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative_1 |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 6.52 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 35.89 tokens</li><li>max: 188 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 35.79 tokens</li><li>max: 188 tokens</li></ul> |
* Samples:
| anchor | positive | negative_1 |
|:------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>A2 amacrine cell</code> | <code>A bistratifed retinal amacrine cell with a small dendritic field, dendrite stratification in S1-S2, and a second dendrite stratification in S5. This cell type releases the neurotransmitter glycine.</code> | <code>An amacrine cell with a flat dendritic arbor and a medium dendritic field. Starburst amacrine cells have post-synaptic terminals in S2. This cell type releases the neurotransmitters gamma-aminobutyric acid (GABA) and acetylcholine.</code> |
| <code>A2 amacrine cell</code> | <code>A bistratifed retinal amacrine cell with a small dendritic field, dendrite stratification in S1-S2, and a second dendrite stratification in S5. This cell type releases the neurotransmitter glycine.</code> | <code>An ON diffuse bipolar cell that predominantly connects to ON parasol cells and lateral amacrine cells.</code> |
| <code>A2 amacrine cell</code> | <code>A bistratifed retinal amacrine cell with a small dendritic field, dendrite stratification in S1-S2, and a second dendrite stratification in S5. This cell type releases the neurotransmitter glycine.</code> | <code>An amacrine cell that uses GABA as a neurotransmitter.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `bf16`: True
#### 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`: 256
- `per_device_eval_batch_size`: 256
- `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`: 2e-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`: 4
- `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`: True
- `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`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | cellxgene pseudo bulk 100k multiplets natural language annotation cell sentence 1 caption loss | cellxgene pseudo bulk 100k multiplets natural language annotation cell sentence 2 caption loss | gene description loss | cell type description loss | cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption_cosine_accuracy | cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_caption_cosine_accuracy | gene_description_cosine_accuracy | cell_type_description_cosine_accuracy |
|:------:|:----:|:-------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:---------------------:|:--------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------:|:-------------------------------------:|
| 0.0907 | 100 | 12.55 | 14.3562 | 20.9395 | 6.7263 | 2.7643 | 0.5129 | 0.5296 | 0.1590 | 0.7930 |
| 0.1815 | 200 | 11.46 | 13.4363 | 20.9395 | 6.7263 | 2.7643 | 0.5491 | 0.5296 | 0.1590 | 0.7930 |
| 0.2722 | 300 | 11.1669 | 11.4948 | 20.9395 | 6.7263 | 2.7643 | 0.5885 | 0.5296 | 0.1590 | 0.7930 |
| 0.3630 | 400 | 10.8355 | 8.1331 | 20.9395 | 6.7263 | 2.7643 | 0.6519 | 0.5296 | 0.1590 | 0.7930 |
| 0.4537 | 500 | 10.4825 | 6.8902 | 20.9395 | 6.7263 | 2.7643 | 0.6945 | 0.5296 | 0.1590 | 0.7930 |
| 0.5445 | 600 | 9.68 | 6.3524 | 20.9395 | 6.7263 | 2.7643 | 0.7209 | 0.5296 | 0.1590 | 0.7930 |
| 0.6352 | 700 | 10.0539 | 5.8229 | 20.9395 | 6.7263 | 2.7643 | 0.7426 | 0.5296 | 0.1590 | 0.7930 |
| 0.7260 | 800 | 9.8377 | 5.5092 | 20.9395 | 6.7263 | 2.7643 | 0.7533 | 0.5296 | 0.1590 | 0.7930 |
| 0.8167 | 900 | 9.4124 | 5.3195 | 20.9395 | 6.7263 | 2.7643 | 0.7594 | 0.5296 | 0.1590 | 0.7930 |
| 0.9074 | 1000 | 9.1121 | 5.1125 | 20.9395 | 6.7263 | 2.7643 | 0.7666 | 0.5296 | 0.1590 | 0.7930 |
| 0.9982 | 1100 | 8.9044 | 4.9929 | 20.9395 | 6.7263 | 2.7643 | 0.7707 | 0.5296 | 0.1590 | 0.7930 |
| 1.0889 | 1200 | 8.3629 | 4.9229 | 20.9395 | 6.7263 | 2.7643 | 0.7728 | 0.5296 | 0.1590 | 0.7930 |
| 1.1797 | 1300 | 9.8079 | 4.8866 | 20.9395 | 6.7263 | 2.7643 | 0.7780 | 0.5296 | 0.1590 | 0.7930 |
| 1.2704 | 1400 | 8.7319 | 4.8212 | 20.9395 | 6.7263 | 2.7643 | 0.7789 | 0.5296 | 0.1590 | 0.7930 |
| 1.3612 | 1500 | 9.3434 | 4.7591 | 20.9395 | 6.7263 | 2.7643 | 0.7830 | 0.5296 | 0.1590 | 0.7930 |
| 1.4519 | 1600 | 9.7414 | 4.7403 | 20.9395 | 6.7263 | 2.7643 | 0.7848 | 0.5296 | 0.1590 | 0.7930 |
| 1.5426 | 1700 | 8.4936 | 4.7062 | 20.9395 | 6.7263 | 2.7643 | 0.7864 | 0.5296 | 0.1590 | 0.7930 |
| 1.6334 | 1800 | 9.7521 | 4.6572 | 20.9395 | 6.7263 | 2.7643 | 0.7889 | 0.5296 | 0.1590 | 0.7930 |
| 1.7241 | 1900 | 10.2904 | 4.6307 | 20.9395 | 6.7263 | 2.7643 | 0.7916 | 0.5296 | 0.1590 | 0.7930 |
| 1.8149 | 2000 | 9.6486 | 4.5902 | 20.9395 | 6.7263 | 2.7643 | 0.7905 | 0.5296 | 0.1590 | 0.7930 |
| 1.9056 | 2100 | 8.7473 | 4.5393 | 20.9395 | 6.7263 | 2.7643 | 0.7938 | 0.5296 | 0.1590 | 0.7930 |
| 1.9964 | 2200 | 9.2474 | 4.5402 | 20.9395 | 6.7263 | 2.7643 | 0.7954 | 0.5296 | 0.1590 | 0.7930 |
| 2.0871 | 2300 | 7.9247 | 4.5048 | 20.9395 | 6.7263 | 2.7643 | 0.7947 | 0.5296 | 0.1590 | 0.7930 |
| 2.1779 | 2400 | 8.6917 | 4.4733 | 20.9395 | 6.7263 | 2.7643 | 0.7988 | 0.5296 | 0.1590 | 0.7930 |
| 2.2686 | 2500 | 9.5375 | 4.4493 | 20.9395 | 6.7263 | 2.7643 | 0.8008 | 0.5296 | 0.1590 | 0.7930 |
| 2.3593 | 2600 | 9.357 | 4.4294 | 20.9395 | 6.7263 | 2.7643 | 0.8038 | 0.5296 | 0.1590 | 0.7930 |
| 2.4501 | 2700 | 10.3025 | 4.4175 | 20.9395 | 6.7263 | 2.7643 | 0.8024 | 0.5296 | 0.1590 | 0.7930 |
| 2.5408 | 2800 | 8.8288 | 4.3937 | 20.9395 | 6.7263 | 2.7643 | 0.8030 | 0.5296 | 0.1590 | 0.7930 |
| 2.6316 | 2900 | 9.9533 | 4.3904 | 20.9395 | 6.7263 | 2.7643 | 0.8026 | 0.5296 | 0.1590 | 0.7930 |
| 2.7223 | 3000 | 8.2301 | 4.3472 | 20.9395 | 6.7263 | 2.7643 | 0.8051 | 0.5296 | 0.1590 | 0.7930 |
| 2.8131 | 3100 | 9.3791 | 4.3642 | 20.9395 | 6.7263 | 2.7643 | 0.8035 | 0.5296 | 0.1590 | 0.7930 |
| 2.9038 | 3200 | 9.2864 | 4.3402 | 20.9395 | 6.7263 | 2.7643 | 0.8055 | 0.5296 | 0.1590 | 0.7930 |
| 2.9946 | 3300 | 9.1685 | 4.3224 | 20.9395 | 6.7263 | 2.7643 | 0.8063 | 0.5296 | 0.1590 | 0.7930 |
| 3.0853 | 3400 | 9.8922 | 4.3117 | 20.9395 | 6.7263 | 2.7643 | 0.8062 | 0.5296 | 0.1590 | 0.7930 |
| 3.1760 | 3500 | 9.4374 | 4.2951 | 20.9395 | 6.7263 | 2.7643 | 0.8065 | 0.5296 | 0.1590 | 0.7930 |
| 3.2668 | 3600 | 9.3947 | 4.2956 | 20.9395 | 6.7263 | 2.7643 | 0.8066 | 0.5296 | 0.1590 | 0.7930 |
| 3.3575 | 3700 | 8.1479 | 4.2946 | 20.9395 | 6.7263 | 2.7643 | 0.8075 | 0.5296 | 0.1590 | 0.7930 |
| 3.4483 | 3800 | 8.798 | 4.2745 | 20.9395 | 6.7263 | 2.7643 | 0.8079 | 0.5296 | 0.1590 | 0.7930 |
| 3.5390 | 3900 | 9.6093 | 4.2821 | 20.9395 | 6.7263 | 2.7643 | 0.8067 | 0.5296 | 0.1590 | 0.7930 |
| 3.6298 | 4000 | 8.4309 | 4.2793 | 20.9395 | 6.7263 | 2.7643 | 0.8092 | 0.5296 | 0.1590 | 0.7930 |
| 3.7205 | 4100 | 10.1105 | 4.2691 | 20.9395 | 6.7263 | 2.7643 | 0.8091 | 0.5296 | 0.1590 | 0.7930 |
| 3.8113 | 4200 | 9.7027 | 4.2653 | 20.9395 | 6.7263 | 2.7643 | 0.8110 | 0.5296 | 0.1590 | 0.7930 |
| 3.9020 | 4300 | 8.5948 | 4.2721 | 20.9395 | 6.7263 | 2.7643 | 0.8091 | 0.5296 | 0.1590 | 0.7930 |
| 3.9927 | 4400 | 8.4845 | 4.2822 | 20.9395 | 6.7263 | 2.7643 | 0.8082 | 0.5296 | 0.1590 | 0.7930 |
### 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
```bibtex
@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
```bibtex
@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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