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---
language:
- code
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:94500
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Primary CD8+ T cells from a subject identified as CL-MCRL, exposed
to the GPR epitope with a dpi (days post-infection) of 87.5.
sentences:
- Cancer cell line (CCL23) derived from a carcinoma patient.
- Primary CD34+ human cells in three-phase in vitro culture, isolated on day 13,
with GG1dd zf vector transduction.
- 23-year-old primary nonETP leukemic blasts from bone marrow.
- source_sentence: Hematopoietic cells with PI-AnnexinV-GFP+CD33+ phenotype from a
xenograft strain NRG-3GS.
sentences:
- H9 embryonic stem cells treated with recombinant Wnt3a for 8 hours in culture.
- iCell Hepatocytes that have been treated with 075\_OLBO\_10 in a study involving
BO class and dose 10.
- 48 hour treatment of colorectal carcinoma cell line HCT116 (colorectal cancer)
with control treatment.
- source_sentence: Memory B cells derived from a female thoracic lymph node, obtained
from a donor in their seventh decade.
sentences:
- Neuron cell type from the Pulvinar of thalamus, derived from a 42-year-old human
individual.
- Germinal center B cell derived from the tonsil tissue of a 3-year-old male with
recurrent tonsillitis.
- B cell sample from a 55-year old female Asian individual with managed systemic
lupus erythematosus (SLE). The cell was derived from peripheral blood mononuclear
cells (PBMCs).
- source_sentence: Pericyte cells, part of the smooth muscle lineage, extracted from
the transition zone of a 74-year-old human prostate.
sentences:
- A CD8-positive, alpha-beta memory T cell, CD45RO-positive, specifically identified
as Tem/Effector cytotoxic T cells, as determined by CellTypist prediction. The
cell was obtained from the lung tissue of a female individual in her eighth decade.
- CD4-positive, alpha-beta T cell sample taken from a 53-year old female Asian individual
with managed systemic lupus erythematosus (SLE).
- Natural killer cell from a 32-year old female of European descent with managed
systemic lupus erythematosus (SLE).
- source_sentence: Sample is a basal cell of prostate epithelium, taken from the transition
zone of the prostate gland in a 72-year old male. It belongs to the Epithelia
lineage and Population BE.
sentences:
- Neuron cell type from a 42-year old male cerebral cortex tissue, specifically
from the rostral gyrus dorsal division of MFC A32, classified as Deep-layer corticothalamic
and 6b.
- Dendritic cell from the transition zone of prostate of a 29-year-old male, specifically
from the EREG+ population.
- Neuron from the mediodorsal nucleus of thalamus, which is part of the medial nuclear
complex of thalamus (MNC) in the thalamic complex, taken from a 42-year-old male
human donor with European ethnicity. The neuron belongs to the Thalamic excitatory
supercluster.
datasets:
- jo-mengr/cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation
- jo-mengr/geo_70k_multiplets_natural_language_annotation
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer
results:
- task:
type: triplet
name: Triplet
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 0.9402857422828674
name: Cosine Accuracy
- type: cosine_accuracy
value: 0.9371428489685059
name: Cosine Accuracy
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained on the [cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation) and [geo_70k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/geo_70k_multiplets_natural_language_annotation) datasets. It maps sentences & paragraphs to a None-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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** None tokens
- **Output Dimensionality:** None dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation)
- [geo_70k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/geo_70k_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(28996, 768, padding_idx=0)
(position_embeddings): Embedding(512, 768)
(token_type_embeddings): Embedding(2, 768)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): BertEncoder(
(layer): ModuleList(
(0-11): 12 x BertLayer(
(attention): BertAttention(
(self): BertSdpaSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): BertPooler(
(dense): Linear(in_features=768, out_features=768, bias=True)
(activation): Tanh()
)
)
(text_adapter): AdapterModule(
(net): Sequential(
(0): Linear(in_features=768, out_features=512, bias=True)
(1): ReLU(inplace=True)
(2): Linear(in_features=512, out_features=2048, bias=True)
(3): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(omics_adapter): AdapterModule(
(net): Sequential(
(0): Linear(in_features=64, out_features=512, bias=True)
(1): ReLU(inplace=True)
(2): Linear(in_features=512, out_features=2048, bias=True)
(3): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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-100k-natural_language_annotation-pca-1024")
# Run inference
sentences = [
'Sample is a basal cell of prostate epithelium, taken from the transition zone of the prostate gland in a 72-year old male. It belongs to the Epithelia lineage and Population BE.',
'Neuron cell type from a 42-year old male cerebral cortex tissue, specifically from the rostral gyrus dorsal division of MFC A32, classified as Deep-layer corticothalamic and 6b.',
'Neuron from the mediodorsal nucleus of thalamus, which is part of the medial nuclear complex of thalamus (MNC) in the thalamic complex, taken from a 42-year-old male human donor with European ethnicity. The neuron belongs to the Thalamic excitatory supercluster.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### 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
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9403** |
#### Triplet
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9371** |
<!--
## 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
#### cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation
* Dataset: [cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation) at [a6241c4](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation/tree/a6241c46b7e108ff9106fd7a1838117096e2c3c6)
* Size: 31,500 training samples
* Columns: <code>anndata_ref</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code>
* Approximate statistics based on the first 1000 samples:
| | anndata_ref | positive | negative_1 | negative_2 |
|:--------|:-------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------|
| type | dict | string | string | dict |
| details | <ul><li></li></ul> | <ul><li>min: 53 characters</li><li>mean: 163.04 characters</li><li>max: 743 characters</li></ul> | <ul><li>min: 43 characters</li><li>mean: 163.42 characters</li><li>max: 609 characters</li></ul> | <ul><li></li></ul> |
* Samples:
| anndata_ref | positive | negative_1 | negative_2 |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cZdKEMQFMKGHc6E/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GDgf9MfckNmk2Bf/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GWrtoRASdZAWdPa/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/FAiRMKztdjLYG23/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TDTo6seSi6qrGTq/download'}}, 'sample_id': 'census_1f1c5c14-5949-4c81-b28e-b272e271b672_570'}</code> | <code>Stromal cell of ovary, specifically Stroma-2, from a human adult female individual, in S phase of the cell cycle.</code> | <code>Neuron cell type from a 50-year-old male human thalamic complex, specifically from the ventral anterior nucleus of thalamus within the lateral nuclear complex.</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cZdKEMQFMKGHc6E/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GDgf9MfckNmk2Bf/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GWrtoRASdZAWdPa/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/FAiRMKztdjLYG23/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TDTo6seSi6qrGTq/download'}}, 'sample_id': 'census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_19663'}</code> |
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cZdKEMQFMKGHc6E/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GDgf9MfckNmk2Bf/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GWrtoRASdZAWdPa/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/FAiRMKztdjLYG23/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TDTo6seSi6qrGTq/download'}}, 'sample_id': 'census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_34872'}</code> | <code>CD8-positive, alpha-beta T cell sample from a 52-year old Asian female with managed systemic lupus erythematosus (SLE).</code> | <code>Mucosal invariant T cell derived from the spleen of a female in her seventies.</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cZdKEMQFMKGHc6E/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GDgf9MfckNmk2Bf/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GWrtoRASdZAWdPa/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/FAiRMKztdjLYG23/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TDTo6seSi6qrGTq/download'}}, 'sample_id': 'census_74cff64f-9da9-4b2a-9b3b-8a04a1598040_4145'}</code> |
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cZdKEMQFMKGHc6E/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GDgf9MfckNmk2Bf/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GWrtoRASdZAWdPa/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/FAiRMKztdjLYG23/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TDTo6seSi6qrGTq/download'}}, 'sample_id': 'census_74cff64f-9da9-4b2a-9b3b-8a04a1598040_7321'}</code> | <code>Hofbauer cell derived from the decidua basalis tissue of a female individual at 8 post conception week (8_PCW). The sample is a nucleus.</code> | <code>Regulatory T cell derived from a lymph node of a male individual with advanced non-small cell lung cancer (NSCLC), stage IV, who has a history of smoking.</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cZdKEMQFMKGHc6E/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GDgf9MfckNmk2Bf/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/GWrtoRASdZAWdPa/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/FAiRMKztdjLYG23/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TDTo6seSi6qrGTq/download'}}, 'sample_id': 'census_5a73f63f-18a2-49b5-b431-2c469c41a41b_163'}</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"
}
```
#### geo_70k_multiplets_natural_language_annotation
* Dataset: [geo_70k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/geo_70k_multiplets_natural_language_annotation) at [449eb79](https://huggingface.co/datasets/jo-mengr/geo_70k_multiplets_natural_language_annotation/tree/449eb79e41b05af4d3e32900144411963f626f8c)
* Size: 63,000 training samples
* Columns: <code>anndata_ref</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code>
* Approximate statistics based on the first 1000 samples:
| | anndata_ref | positive | negative_1 | negative_2 |
|:--------|:-------------------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------|
| type | dict | string | string | dict |
| details | <ul><li></li></ul> | <ul><li>min: 21 characters</li><li>mean: 139.4 characters</li><li>max: 696 characters</li></ul> | <ul><li>min: 23 characters</li><li>mean: 142.09 characters</li><li>max: 705 characters</li></ul> | <ul><li></li></ul> |
* Samples:
| anndata_ref | positive | negative_1 | negative_2 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/mwyWK7cTL3j5ydA/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Tg4TMSg8gDtxJ5x/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/QjSE4s5ZHamjwfi/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/rYEATQXRJsx42Qr/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cWgZaKPJLsgb5Zo/download'}}, 'sample_id': 'SRX3111576'}</code> | <code>198Z\_MSCB-067 sample contains primary cells that are neuronal progenitors from patient type WB\_1.</code> | <code>31-year-old female Caucasian with ntm disease provided a whole blood sample on July 11, 2016. The baseline FEVPP was 89.74 and FVCpp was 129.41.</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/mwyWK7cTL3j5ydA/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Tg4TMSg8gDtxJ5x/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/QjSE4s5ZHamjwfi/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/rYEATQXRJsx42Qr/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cWgZaKPJLsgb5Zo/download'}}, 'sample_id': 'SRX6591734'}</code> |
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/mwyWK7cTL3j5ydA/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Tg4TMSg8gDtxJ5x/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/QjSE4s5ZHamjwfi/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/rYEATQXRJsx42Qr/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cWgZaKPJLsgb5Zo/download'}}, 'sample_id': 'SRX7834244'}</code> | <code>CD8+ T cells from a healthy skin sample, labeled C4, from plate rep1, well E6, sequencing batch b7, which passed QC, and clustered as 2\_Resid.</code> | <code>6-week-old (PCW6) neuronal epithelium tissue from donor HSB325, cultured using C1-72 chip.</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/mwyWK7cTL3j5ydA/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Tg4TMSg8gDtxJ5x/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/QjSE4s5ZHamjwfi/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/rYEATQXRJsx42Qr/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cWgZaKPJLsgb5Zo/download'}}, 'sample_id': 'SRX2440281'}</code> |
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/mwyWK7cTL3j5ydA/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Tg4TMSg8gDtxJ5x/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/QjSE4s5ZHamjwfi/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/rYEATQXRJsx42Qr/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cWgZaKPJLsgb5Zo/download'}}, 'sample_id': 'SRX3112138'}</code> | <code>201Z\_MSCB-083 is a sample of primary neuronal progenitor cells from patient MD1 with no reported treatment.</code> | <code>48-hour sample from HPV-negative UPCI:SCC131 cell line, a head and neck squamous cell carcinoma (HNSCC) cell line, that has not been irradiated.</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/mwyWK7cTL3j5ydA/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Tg4TMSg8gDtxJ5x/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/QjSE4s5ZHamjwfi/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/rYEATQXRJsx42Qr/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/cWgZaKPJLsgb5Zo/download'}}, 'sample_id': 'SRX7448263'}</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 Datasets
#### cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation
* Dataset: [cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation) at [a6241c4](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_35k_multiplets_natural_language_annotation/tree/a6241c46b7e108ff9106fd7a1838117096e2c3c6)
* Size: 3,500 evaluation samples
* Columns: <code>anndata_ref</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code>
* Approximate statistics based on the first 1000 samples:
| | anndata_ref | positive | negative_1 | negative_2 |
|:--------|:-------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------|
| type | dict | string | string | dict |
| details | <ul><li></li></ul> | <ul><li>min: 51 characters</li><li>mean: 168.27 characters</li><li>max: 829 characters</li></ul> | <ul><li>min: 57 characters</li><li>mean: 174.27 characters</li><li>max: 804 characters</li></ul> | <ul><li></li></ul> |
* Samples:
| anndata_ref | positive | negative_1 | negative_2 |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Zk4EtWao9WKAQKc/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/LET7EG7xi56RqMd/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/5qjxiEJwwdNHTBX/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/z4TQkdxcP3ynBMn/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/6NZ94ZLkLKYyPcY/download'}}, 'sample_id': 'census_842c6f5d-4a94-4eef-8510-8c792d1124bc_6822'}</code> | <code>Non-classical monocyte cell type, derived from a fresh breast tissue sample of an African American female donor with low breast density, obese BMI, and premenopausal status. The cell was obtained through resection procedure and analyzed using single-cell transcriptomics as part of the Human Breast Cell Atlas (HBCA) study.</code> | <code>Plasma cells derived from gingival tissue of a 39-year-old female.</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Zk4EtWao9WKAQKc/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/LET7EG7xi56RqMd/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/5qjxiEJwwdNHTBX/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/z4TQkdxcP3ynBMn/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/6NZ94ZLkLKYyPcY/download'}}, 'sample_id': 'census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_23461'}</code> |
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Zk4EtWao9WKAQKc/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/LET7EG7xi56RqMd/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/5qjxiEJwwdNHTBX/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/z4TQkdxcP3ynBMn/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/6NZ94ZLkLKYyPcY/download'}}, 'sample_id': 'census_b46237d1-19c6-4af2-9335-9854634bad16_9825'}</code> | <code>Enteric neuron cells derived from the ileum tissue at Carnegie stage 22.</code> | <code>Ciliated cell from the trachea of a 6-12 year-old European male with no SARS-CoV-2 infection, who is a non-smoker and healthy.</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Zk4EtWao9WKAQKc/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/LET7EG7xi56RqMd/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/5qjxiEJwwdNHTBX/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/z4TQkdxcP3ynBMn/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/6NZ94ZLkLKYyPcY/download'}}, 'sample_id': 'census_2872f4b0-b171-46e2-abc6-befcf6de6306_2871'}</code> |
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Zk4EtWao9WKAQKc/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/LET7EG7xi56RqMd/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/5qjxiEJwwdNHTBX/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/z4TQkdxcP3ynBMn/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/6NZ94ZLkLKYyPcY/download'}}, 'sample_id': 'census_d7d7e89c-c93a-422d-8958-9b4a90b69558_4209'}</code> | <code>Activated CD16-positive, CD56-dim natural killer cell taken from a 26-year-old male, activated with CD3, and found to be in G1 phase.</code> | <code>CD8-positive, alpha-beta thymocyte cell type derived from a 74-year-old male human with European self-reported ethnicity, located in the transition zone of the prostate.</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/Zk4EtWao9WKAQKc/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/LET7EG7xi56RqMd/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/5qjxiEJwwdNHTBX/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/z4TQkdxcP3ynBMn/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/6NZ94ZLkLKYyPcY/download'}}, 'sample_id': 'census_535e9336-2d8d-43c3-944d-bcbebe20df8a_18'}</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"
}
```
#### geo_70k_multiplets_natural_language_annotation
* Dataset: [geo_70k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/geo_70k_multiplets_natural_language_annotation) at [449eb79](https://huggingface.co/datasets/jo-mengr/geo_70k_multiplets_natural_language_annotation/tree/449eb79e41b05af4d3e32900144411963f626f8c)
* Size: 7,000 evaluation samples
* Columns: <code>anndata_ref</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code>
* Approximate statistics based on the first 1000 samples:
| | anndata_ref | positive | negative_1 | negative_2 |
|:--------|:-------------------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------|
| type | dict | string | string | dict |
| details | <ul><li></li></ul> | <ul><li>min: 22 characters</li><li>mean: 138.7 characters</li><li>max: 702 characters</li></ul> | <ul><li>min: 22 characters</li><li>mean: 131.79 characters</li><li>max: 702 characters</li></ul> | <ul><li></li></ul> |
* Samples:
| anndata_ref | positive | negative_1 | negative_2 |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kfjX6LkLewqssdN/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kxd2NqJjnMSArf6/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/zqPbdqn5nCgo7rb/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/b7sANypKxGyYQ2J/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TwFF6TWRp9sMxgc/download'}}, 'sample_id': 'SRX16033546'}</code> | <code>A549 lung adenocarcinoma cell line with ectopic expression of TPK1 p.G48C mutation.</code> | <code>3 days after the 4th immunization, blood sample from donor 1033 with low antibody-dependent cellular phagocytosis (ADCP) category.</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kfjX6LkLewqssdN/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kxd2NqJjnMSArf6/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/zqPbdqn5nCgo7rb/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/b7sANypKxGyYQ2J/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TwFF6TWRp9sMxgc/download'}}, 'sample_id': 'SRX10356703'}</code> |
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kfjX6LkLewqssdN/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kxd2NqJjnMSArf6/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/zqPbdqn5nCgo7rb/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/b7sANypKxGyYQ2J/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TwFF6TWRp9sMxgc/download'}}, 'sample_id': 'SRX8241199'}</code> | <code>Human fibroblasts at the D7 time point during reprogramming into induced pluripotent stem cells (iPSCs) or hiPSCs.</code> | <code>CD14+ monocytes from a healthy control participant (ID 2015).</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kfjX6LkLewqssdN/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kxd2NqJjnMSArf6/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/zqPbdqn5nCgo7rb/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/b7sANypKxGyYQ2J/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TwFF6TWRp9sMxgc/download'}}, 'sample_id': 'SRX14140416'}</code> |
| <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kfjX6LkLewqssdN/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kxd2NqJjnMSArf6/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/zqPbdqn5nCgo7rb/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/b7sANypKxGyYQ2J/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TwFF6TWRp9sMxgc/download'}}, 'sample_id': 'SRX17834359'}</code> | <code>Whole blood sample from subject HRV15-017, collected at day 1 in the afternoon.</code> | <code>59 year old male bronchial epithelial cells with 39 pack years of smoking history and imaging cluster 1.</code> | <code>{'file_record': {'dataset_path': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kfjX6LkLewqssdN/download', 'embeddings': {'X_geneformer': 'https://nxc-fredato.imbi.uni-freiburg.de/s/kxd2NqJjnMSArf6/download', 'X_hvg': 'https://nxc-fredato.imbi.uni-freiburg.de/s/zqPbdqn5nCgo7rb/download', 'X_pca': 'https://nxc-fredato.imbi.uni-freiburg.de/s/b7sANypKxGyYQ2J/download', 'X_scvi': 'https://nxc-fredato.imbi.uni-freiburg.de/s/TwFF6TWRp9sMxgc/download'}}, 'sample_id': 'SRX5429074'}</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`: 2e-05
- `num_train_epochs`: 8
- `warmup_ratio`: 0.1
- `fp16`: True
- `dataloader_num_workers`: 1
#### 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`: 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`: 8
- `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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 1
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `eval_use_gather_object`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | cellxgene pseudo bulk 35k multiplets natural language annotation loss | geo 70k multiplets natural language annotation loss | cosine_accuracy |
|:------:|:----:|:-------------:|:---------------------------------------------------------------------:|:---------------------------------------------------:|:---------------:|
| 0.1351 | 100 | - | 19.5545 | 19.6050 | 0.5656 |
| 0.2703 | 200 | 17.2819 | 19.4888 | 17.2415 | 0.7261 |
| 0.4054 | 300 | - | 17.2527 | 14.3099 | 0.7684 |
| 0.5405 | 400 | 13.4122 | 13.1462 | 13.4371 | 0.7976 |
| 0.6757 | 500 | - | 12.6305 | 9.3601 | 0.8474 |
| 0.8108 | 600 | 8.3246 | 11.1233 | 7.6021 | 0.8787 |
| 0.9459 | 700 | - | 8.5871 | 7.6461 | 0.8980 |
| 1.0811 | 800 | 6.1203 | 7.0774 | 7.1605 | 0.9046 |
| 1.2162 | 900 | - | 6.0461 | 6.7694 | 0.9076 |
| 1.3514 | 1000 | 5.1622 | 6.1759 | 6.0741 | 0.9166 |
| 1.4865 | 1100 | - | 6.6497 | 5.3305 | 0.9269 |
| 1.6216 | 1200 | 4.7346 | 7.6330 | 4.9083 | 0.9324 |
| 1.7568 | 1300 | - | 6.5700 | 4.8609 | 0.9349 |
| 1.8919 | 1400 | 4.4577 | 6.9249 | 4.6155 | 0.9401 |
| 2.0270 | 1500 | - | 5.4120 | 5.0721 | 0.9367 |
| 2.1622 | 1600 | 4.2281 | 6.3842 | 4.6481 | 0.9407 |
| 2.2973 | 1700 | - | 5.6970 | 4.9588 | 0.9370 |
| 2.4324 | 1800 | 4.2392 | 6.3306 | 4.6888 | 0.9407 |
| 2.5676 | 1900 | - | 5.3909 | 5.0415 | 0.9364 |
| 2.7027 | 2000 | 4.2237 | 6.0779 | 4.7476 | 0.9394 |
| 2.8378 | 2100 | - | 5.3631 | 5.0280 | 0.9379 |
| 2.9730 | 2200 | 4.2215 | 5.5800 | 4.9418 | 0.9373 |
| 3.1081 | 2300 | - | 6.3898 | 4.6718 | 0.9400 |
| 3.2432 | 2400 | 4.1984 | 4.7118 | 5.4301 | 0.9313 |
| 3.3784 | 2500 | - | 7.2266 | 4.5063 | 0.9419 |
| 3.5135 | 2600 | 4.2538 | 8.1464 | 4.4121 | 0.9426 |
| 3.6486 | 2700 | - | 6.5866 | 4.6253 | 0.9409 |
| 3.7838 | 2800 | 4.2186 | 5.8797 | 4.8671 | 0.9380 |
| 3.9189 | 2900 | - | 5.5591 | 4.9559 | 0.9377 |
| 4.0541 | 3000 | 4.2064 | 6.3420 | 4.7167 | 0.9413 |
| 4.1892 | 3100 | - | 5.9561 | 4.8190 | 0.9387 |
| 4.3243 | 3200 | 4.2248 | 6.3844 | 4.6827 | 0.9410 |
| 4.4595 | 3300 | - | 7.1522 | 4.5193 | 0.9421 |
| 4.5946 | 3400 | 4.2263 | 4.8333 | 5.3410 | 0.9331 |
| 4.7297 | 3500 | - | 4.5820 | 5.5334 | 0.9306 |
| 4.8649 | 3600 | 4.2472 | 6.8254 | 4.5512 | 0.9413 |
| 5.0 | 3700 | - | 6.4904 | 4.6993 | 0.9399 |
| 5.1351 | 3800 | 4.1936 | 4.8578 | 5.3678 | 0.9344 |
| 5.2703 | 3900 | - | 6.4530 | 4.6426 | 0.9413 |
| 5.4054 | 4000 | 4.2345 | 6.6050 | 4.6684 | 0.9409 |
| 5.5405 | 4100 | - | 4.8690 | 5.3172 | 0.9334 |
| 5.6757 | 4200 | 4.2406 | 6.2903 | 4.7100 | 0.9404 |
| 5.8108 | 4300 | - | 6.6273 | 4.6269 | 0.9419 |
| 5.9459 | 4400 | 4.2227 | 5.4572 | 5.0365 | 0.9370 |
| 6.0811 | 4500 | - | 5.0242 | 5.2568 | 0.9341 |
| 6.2162 | 4600 | 4.1997 | 4.7279 | 5.5242 | 0.9316 |
| 6.3514 | 4700 | - | 5.1846 | 5.2246 | 0.9339 |
| 6.4865 | 4800 | 4.2361 | 5.8601 | 4.8249 | 0.9381 |
| 6.6216 | 4900 | - | 6.9398 | 4.5848 | 0.9423 |
| 6.7568 | 5000 | 4.2273 | 6.2977 | 4.6921 | 0.9406 |
| 6.8919 | 5100 | - | 6.9737 | 4.5439 | 0.9421 |
| 7.0270 | 5200 | 4.2052 | 5.3900 | 5.0873 | 0.9370 |
| 7.1622 | 5300 | - | 6.3929 | 4.6474 | 0.9406 |
| 7.2973 | 5400 | 4.2416 | 5.6994 | 4.9590 | 0.9371 |
| 7.4324 | 5500 | - | 6.3184 | 4.6922 | 0.9407 |
| 7.5676 | 5600 | 4.2311 | 5.3932 | 5.0403 | 0.9363 |
| 7.7027 | 5700 | - | 6.0781 | 4.7480 | 0.9394 |
| 7.8378 | 5800 | 4.229 | 5.3664 | 5.0291 | 0.9380 |
| 7.9730 | 5900 | - | 5.5803 | 4.9391 | 0.9371 |
### Framework Versions
- Python: 3.10.10
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.43.4
- PyTorch: 2.6.0+cu124
- Accelerate: 0.33.0
- Datasets: 2.14.4
- Tokenizers: 0.19.1
## 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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