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---
language:
- code
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:81143
- loss:MultipleNegativesRankingLoss
base_model: NeuML/pubmedbert-base-embeddings
widget:
- source_sentence: EEF1A1 FTL CD74 MALAT1 TPT1 ACTB TMSB10 WARS1 HSPA8 LCP1 EIF1 PTMA
HSP90AB1 RBM3 FAU TYMP VIM GSTP1 CALR RACK1 TMSB4X HSP90AA1 HSPA5 SYNGR2 STAT1
FTH1 IRF1 PPDPF BTF3 LAPTM5 HSP90B1 GDI2 WDR1 CORO1A ATP5F1E TMBIM6 HINT1 NACA
HERPUD1 MYL6 GADD45B PGK1 DDX5 GAPDH MOB1A ACTR3 CSDE1 EIF4B PABPC1 RBX1 ATP5F1B
ARPC3 PRDX1 NCOA3 PRDX5 RAN ACTR2 SNRPG SNRNP200 ALDOA ATP5F1A YWHAZ PPP1CA TALDO1
sentences:
- This measurement was conducted with 10x 5' v1. Naive B cell from blood of a 26-year
old male, activated with CD3.
- EEF1A1 MALAT1 TMSB4X NACA TPT1 PABPC1 FAU PTMA FTL FTH1 NPM1 HSPA5 LDHB COX4I1
LCP1 SH3BGRL3 EEF2 EIF1 RACK1 GSTP1 SMCHD1 ELOB DDX5 GAPDH GTF3A BTF3 HNRNPU TAGLN2
RNF149 SSR2 YWHAB HNRNPF AKAP13 CALR OST4 MYCBP2 IL32 VIM TMSB10 GABARAPL2 THRAP3
ARID4B EIF4B TRAM1 HSP90AA1 ERP29 FXYD5 EZR RAB18 EIF3L MYH9 EIF3E PDCD5 RABAC1
FKBP8 CHCHD2 DOCK8 HDLBP SRSF7 TMED5 MYL12B TRIR NCOA3 EIF2S2
- This measurement was conducted with 10x 5' v1. A 26-year-old male individual's
blood sample, containing naive thymus-derived CD4-positive, alpha-beta T cells,
with no activation or treatment, and in G1 phase.
- source_sentence: MALAT1 EEF1A1 TPT1 PTMA ACTB TMSB4X H3-3B FTL FTH1 TMSB10 LGALS1
VIM CYBA FAU EIF1 NACA RACK1 UBA52 HSP90AA1 CD63 SH3BGRL3 LMO4 HMGB1 S100A4 UBC
HNRNPU HSP90AB1 DDX5 DUSP1 HNRNPA2B1 SOX4 JUND DBI S100A6 GSTP1 MYL6 PFN1 GAPDH
SRGN SERF2 TAGLN2 IER2 UBB CFL1 JUN YBX1 PABPC1 OAZ1 ARPC3 CCNI DAD1 BTG1 ATP5MC2
BTF3 ZFP36L2 TSC22D3 EEF2 FOS IFITM2 PPIA KLF6 GNAS DYNLL1 MYL12A
sentences:
- This measurement was conducted with 10x 3' v3. Blasts cells derived from the blood
of a 4-month old male.
- MALAT1 TPT1 HSP90B1 SSR4 SUB1 EEF1A1 SAT1 XBP1 SPCS1 ITM2C PPIB SEC61B TMBIM6
SEC61G CYBA FAU UBC NACA SELENOS TMSB10 SEC11C UBE2J1 CALR TXNIP HSPA5 ACTB SELENOK
SPCS2 RRBP1 UBA52 H3-3B SERF2 FTH1 EIF1 SEC62 NUCB2 SSR2 VIM ERLEC1 MYL6 SRGN
ATP5F1E PTMA NEAT1 TRAM1 GNAS KLF6 LMAN1 MYDGF TMEM59 IFI6 ARPC2 H1-10 CD74 HERPUD1
HSP90AA1 OAZ1 GAPDH SSR3 CCNI SPCS3 COX4I1 ITM2B TXN
- This measurement was conducted with 10x 3' v3. This is a megakaryocyte-erythroid
progenitor cell (MEP-like) derived from a 1-month-old female patient with KMT2A-rearranged
(KMT2A-r) infant acute lymphoblastic leukemia (ALL). The cell exhibits increased
lineage plasticity, downregulated steroid response pathways, and belongs to a
hematopoietic stem and progenitor-like (HSPC-like) population that forms an immunosuppressive
signaling circuit with cytotoxic lymphocytes.
- source_sentence: MALAT1 NRXN3 NRXN1 DPP10 ADARB2 IL1RAPL1 CADM2 MEIS2 ROBO2 PBX1
SOX2-OT CELF2 RALYL DPP6 PBX3 KALRN SLC8A1 MEG3 EPHA6 GRIK1 ERBB4 SPOCK3 ENOX1
ZNF385D KCND2 ADCY2 PDE4D GRIP1 TCF4 SNTG1 NTRK2 PCDH9 NKAIN2 MAML3 DAB1 GRIA1
LRFN5 NTM EPHA5 ANK3 LSAMP MAP2 DCLK1 TRPM3 KIRREL3 PPM1E KCNQ5 SIPA1L1 CHSY3
RORA CACNA2D1 CADPS TAFA2 KLHL29 TIAM1 GABRB3 ROBO1 FUT9 ATRNL1 FGF13 NCAM1 AKAP6
L3MBTL4 IL1RAPL2
sentences:
- MALAT1 PLP1 PCDH9 IL1RAPL1 PTPRD QKI ST18 MAN2A1 KIRREL3 PDE4B GPM6B ANK3 EDIL3
MOB3B MBP PHLPP1 MAP7 TMEFF2 PPP2R2B ZEB2 MAML3 SLC44A1 PTGDS FOXP1 DOCK4 SLC24A2
MAP4K5 SGK1 APP DOCK5 ELMO1 FMNL2 SIK3 FRMD5 SHTN1 GRM3 LINC00609 PICALM APLP1
DNAJC6 MACF1 TMEM165 EXOC6B HHIP YPEL2 CTNNA3 SOX2-OT DBNDD2 CD22 VRK2 FUT8 PLEKHH1
ANKRD44 SLCO1A2 COBL ARHGAP21 CCDC88A FOXO3 ATP8A1 HIP1 ENPP2 PPM1B SECISBP2L
IGF1R
- This measurement was conducted with 10x 3' v3. Neuron cell type from a 50-year-old
male human cerebral cortex, specifically from the Cingulate gyrus, rostral (CgGr),
Ventral division of MFC - A24 region, with European self-reported ethnicity, analyzed
at the nucleus level.
- This measurement was conducted with 10x 3' v3. Neuron cell type from a 50-year-old
male human cerebral cortex, specifically the rostral cingulate gyrus, ventral
division of MFC, A24, with European ethnicity.
- source_sentence: MALAT1 TPT1 SSR4 HSP90AA1 EEF1A1 JUN KLF6 FTL FOS BTG2 SAT1 JUNB
PPIB CD74 XBP1 DUSP1 SEC11C RGCC UBC SERF2 HSP90B1 HERPUD1 FAU TSC22D3 CYBA HM13
SERP1 NEAT1 CD38 TMBIM6 RPN1 PSAP OST4 TMSB10 LMAN1 SEC61B RRBP1 DNAJB1 RHOB EIF1
UBE2J1 HSPA5 SSR3 KLF2 P4HB MYDGF SPCS2 ITM2C UBB TMED9 SEL1L SUB1 SPCS1 SEC61G
MCL1 FTH1 CALR RABAC1 COX7A2 NCL RAB30 PABPC1 SEL1L3 KDELR1
sentences:
- This measurement was conducted with 10x 5' v2. Memory B cell derived from a 65-79
year-old male, taken from the mesenteric lymph node.
- This measurement was conducted with 10x 5' v2. IgA plasma cell sample taken from
the mesenteric lymph node of a 65-79 year-old female.
- EEF1A1 TPT1 TMSB4X ACTB MALAT1 FOS DUSP1 KLF2 FAU JUNB PTMA TMSB10 DNAJB1 FTL
JUN NACA FTH1 TSC22D3 EIF1 PFN1 HSPA8 LDHB H3-3B BTG1 ZFP36L2 NPM1 IL32 VIM PABPC1
CORO1A COX4I1 BTF3 UBC DUSP2 EEF2 EEF1G ARHGEF1 HSP90AB1 CIRBP MYL12A NR4A1 ZFP36
ANXA1 ITM2B NOSIP PNRC1 UQCRB BTG2 LAPTM5 PCBP1 COMMD6 S100A4 PPIA UBA52 CD44
FAM107B YBX1 HSP90AA1 GAPDH HSPE1 SRSF7 SERP1 CXCR4 PPDPF
- source_sentence: EEF1A1 TMSB4X MALAT1 CD74 H3-3B FAU TPT1 ACTB FTH1 PTMA EIF1 ZFP36L1
UBA52 NPM1 PPIA HSP90AA1 RGS2 SAT1 TSC22D3 EEF2 HMGB1 GRN STK17B COTL1 EDF1 CD83
PRDX1 ZFP36 COX4I1 ANP32B EML4 TAF1D UQCRH NACA RACK1 ENO1 RBM3 PFN1 PARK7 IRF1
SNRPD2 SNRPB COX7C KLF2 ATP6V1F ZNF331 BTF3 EIF3H HNRNPDL UQCRB EIF4A2 TAGLN2
ARPC2 YWHAB SF1 EIF3F ZFAS1 H4C3 TMSB10 HERPUD1 SLC2A3 WNK1 MEF2A ARHGAP15
sentences:
- MALAT1 CD74 EEF1A1 ACTB EIF1 TMSB4X PTMA TSC22D3 FTL TPT1 BTG1 FTH1 UBC TMSB10
KLF6 FAU PNRC1 HSP90AB1 CD83 LAPTM5 JUN NACA RACK1 HLA-DRB5 DDX5 KLF2 IRF8 GPR183
TXNIP PPP1R15A NFKBIA YPEL5 H3-3B ZFP36L2 YWHAZ UBA52 CYBA OAZ1 DUSP1 SARAF RHOA
MYL12A COTL1 PFN1 HSPA8 MCL1 TAGLN2 TUBA1A CALM1 HMGN2 BCLAF1 PABPC1 HSP90AA1
SMAP2 EZR ARPC3 ACTR3 EPC1 CXCR4 SEPTIN7 ZFP36 SNX9 EEF2 FOS
- This measurement was conducted with 10x 5' v1. Memory B cell derived from a 3-year-old
male human tonsil tissue, expressing IGHJ4*02, IGHV4-59*01, IGKV3-20, IGKJ2, and
IgG1 isotype.
- This measurement was conducted with 10x 5' v1. Plasmablast cell sample from a
3-year-old male, taken from the tonsil tissue, expressing IgM isotype, with IGH_IN_FRAME,
IGH_FUNCTIONAL, IGH_JUNCTION_LENGTH 48.0, IGH_J_CALL IGHJ3*02, IGH_V_CALL_GENOTYPED
IGHV4-39*01, IGK_C_Gene IGKC, IGK_FullLength 2, IGK_Productive 2, IGK_VDJ_Gene
IGKV3-20 None IGKJ1.
datasets:
- jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation
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 2
type: cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2
metrics:
- type: cosine_accuracy
value: 0.4768616259098053
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 the [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) dataset. 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 Dataset:**
- [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation)
- **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()
)
)
(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})
)
)
```
## 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-100k-v3")
# Run inference
sentences = [
'EEF1A1 TMSB4X MALAT1 CD74 H3-3B FAU TPT1 ACTB FTH1 PTMA EIF1 ZFP36L1 UBA52 NPM1 PPIA HSP90AA1 RGS2 SAT1 TSC22D3 EEF2 HMGB1 GRN STK17B COTL1 EDF1 CD83 PRDX1 ZFP36 COX4I1 ANP32B EML4 TAF1D UQCRH NACA RACK1 ENO1 RBM3 PFN1 PARK7 IRF1 SNRPD2 SNRPB COX7C KLF2 ATP6V1F ZNF331 BTF3 EIF3H HNRNPDL UQCRB EIF4A2 TAGLN2 ARPC2 YWHAB SF1 EIF3F ZFAS1 H4C3 TMSB10 HERPUD1 SLC2A3 WNK1 MEF2A ARHGAP15',
"This measurement was conducted with 10x 5' v1. Plasmablast cell sample from a 3-year-old male, taken from the tonsil tissue, expressing IgM isotype, with IGH_IN_FRAME, IGH_FUNCTIONAL, IGH_JUNCTION_LENGTH 48.0, IGH_J_CALL IGHJ3*02, IGH_V_CALL_GENOTYPED IGHV4-39*01, IGK_C_Gene IGKC, IGK_FullLength 2, IGK_Productive 2, IGK_VDJ_Gene IGKV3-20 None IGKJ1.",
"This measurement was conducted with 10x 5' v1. Memory B cell derived from a 3-year-old male human tonsil tissue, expressing IGHJ4*02, IGHV4-59*01, IGKV3-20, IGKJ2, and IgG1 isotype.",
]
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, 1.0000, 1.0000],
# [1.0000, 1.0000, 1.0000],
# [1.0000, 1.0000, 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
* Dataset: `cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.4769** |
<!--
## 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 Dataset
#### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation
* Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [7041d95](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/7041d95e2a15ee7135001c9a0df35c22a45ea4ea)
* 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: 356 characters</li><li>mean: 385.24 characters</li><li>max: 450 characters</li></ul> | <ul><li>min: 92 characters</li><li>mean: 216.13 characters</li><li>max: 900 characters</li></ul> | <ul><li>min: 101 characters</li><li>mean: 215.14 characters</li><li>max: 870 characters</li></ul> | <ul><li>min: 338 characters</li><li>mean: 384.66 characters</li><li>max: 433 characters</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"
}
```
### Evaluation Dataset
#### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation
* Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [7041d95](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/7041d95e2a15ee7135001c9a0df35c22a45ea4ea)
* 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: 347 characters</li><li>mean: 386.7 characters</li><li>max: 437 characters</li></ul> | <ul><li>min: 99 characters</li><li>mean: 209.99 characters</li><li>max: 941 characters</li></ul> | <ul><li>min: 102 characters</li><li>mean: 213.87 characters</li><li>max: 981 characters</li></ul> | <ul><li>min: 347 characters</li><li>mean: 386.42 characters</li><li>max: 433 characters</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"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 0.05
- `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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 0.05
- `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 loss | cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_2_cosine_accuracy |
|:------:|:----:|:-------------:|:----------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:|
| 0.1577 | 100 | 5.7909 | 5.3928 | 0.5671 |
| 0.3155 | 200 | 5.7198 | 5.9454 | 0.4392 |
| 0.4732 | 300 | 5.9533 | 5.9454 | 0.4067 |
| 0.6309 | 400 | 5.9509 | 5.9454 | 0.3111 |
| 0.7886 | 500 | 5.9507 | 5.9454 | 0.3115 |
| 0.9464 | 600 | 5.9507 | 5.9454 | 0.2933 |
| 1.1041 | 700 | 5.9499 | 5.9454 | 0.3154 |
| 1.2618 | 800 | 5.9507 | 5.9454 | 0.4052 |
| 1.4196 | 900 | 5.9508 | 5.9454 | 0.4603 |
| 1.5773 | 1000 | 5.9511 | 5.9455 | 0.4445 |
| 1.7350 | 1100 | 5.9513 | 5.9455 | 0.4450 |
| 1.8927 | 1200 | 5.9512 | 5.9455 | 0.4710 |
| 2.0505 | 1300 | 5.9506 | 5.9455 | 0.4730 |
| 2.2082 | 1400 | 5.9517 | 5.9455 | 0.4721 |
| 2.3659 | 1500 | 5.9517 | 5.9455 | 0.4705 |
| 2.5237 | 1600 | 5.9517 | 5.9455 | 0.4723 |
| 2.6814 | 1700 | 5.9517 | 5.9455 | 0.4751 |
| 2.8391 | 1800 | 5.9517 | 5.9455 | 0.4722 |
| 2.9968 | 1900 | 5.9517 | 5.9455 | 0.4729 |
| 3.1546 | 2000 | 5.9509 | 5.9455 | 0.4677 |
| 3.3123 | 2100 | 5.9517 | 5.9455 | 0.4693 |
| 3.4700 | 2200 | 5.9517 | 5.9455 | 0.4728 |
| 3.6278 | 2300 | 5.9517 | 5.9455 | 0.4702 |
| 3.7855 | 2400 | 5.9517 | 5.9455 | 0.4718 |
| 3.9432 | 2500 | 5.9517 | 5.9455 | 0.4769 |
### 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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