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---
language:
- code
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3150
- loss:MultipleNegativesRankingLoss
- loss:ContrastiveLoss
widget:
- source_sentence: '{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download",
"embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download",
"X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download",
"X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download",
"X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}},
"sample_id": "census_acae7679-d077-461c-b857-ee6ccfeb267f_357"}'
sentences:
- Memory B cell derived from a 5-year-old human individual, with IGH + IGL, IgA1
isotype, IGHJ3*01, IGHV3-30*18, IGLC3, IGLV1-44, IGLJ3, obstructive sleep apnea,
and recurrent tonsillitis.
- Neuron cell type from the hippocampal formation, specifically from the Head of
hippocampus (HiH) - Uncal CA1 dissection, in a 50-year-old male individual, with
a supercluster term of Miscellaneous.
- Prostate gland microvascular endothelial cell derived from a 74-year-old male
of European ethnicity, specifically located in the transition zone of the prostate.
- source_sentence: '{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download",
"embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download",
"X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download",
"X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download",
"X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}},
"sample_id": "census_27d91086-cfe3-4e33-9282-bd1246e5ce8e_128"}'
sentences:
- Sample is an oligodendrocyte cell from a 29-year-old male human, with European
self-reported ethnicity, specifically located in the thalamic complex.
- Neuron cell type from a 50-year-old male cerebral cortex, specifically from the
Long insular gyri (LIG) and Dysgranular insular cortex - Idg region, classified
as Deep-layer corticothalamic and 6b.
- Fibroblast cells from the thalamic complex, specifically from the medial nuclear
complex of thalamus (MNC), mediodorsal nucleus of thalamus + reuniens nucleus
(medioventral nucleus) of thalamus (MD + Re) in a 50-year-old male.
- source_sentence: '{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download",
"embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download",
"X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download",
"X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download",
"X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}},
"sample_id": "census_5af90777-6760-4003-9dba-8f945fec6fdf_3563"}'
sentences:
- Memory B cell from a 3-year-old male human with recurrent tonsillitis, expressing
IgG3 isotype, IGLC2, and IGLV2-23-IGLJ2 antibody.
- Macrophage cells from the kidney tissue of a female individual in her sixties,
specifically SPP1+ tumor-associated macrophages (TAMs), originating from a tumor
sample.
- Fibroblast cells from the hypothalamus tissue, specifically from the mammillary
region of HTH (HTHma) and mammillary nucleus (MN), of a 29-year-old male.
- source_sentence: '{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download",
"embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download",
"X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download",
"X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download",
"X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}},
"sample_id": "census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_30680"}'
sentences:
- Ependymal cell derived from the spinal cord tissue of a 50-year-old male human
donor.
- Progenitor cells derived from blood tissue of a 58-year old female with managed
systemic lupus erythematosus (SLE). The cells are of European ethnicity and were
obtained from peripheral blood mononuclear cell suspension.
- Cell sample from the cortex of kidney, taken from a 46-year-old male with kidney
cancer, identified as an alternatively activated macrophage.
- source_sentence: '{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download",
"embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download",
"X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download",
"X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download",
"X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}},
"sample_id": "census_e84f2780-51e8-4cfa-8aa0-13bbfef677c7_184"}'
sentences:
- A 46-year old female's liver sample, specifically conventional dendritic cell
type 1 (cDC1s) enriched in CD45+ cell suspension, with no reported liver-related
diseases.
- Sample is an ON-bipolar cell derived from the peripheral region of the retina
of a 60-year-old male with European self-reported ethnicity, mapped to GENCODE
24 reference annotation.
- Alpha-beta T cell from the thoracic lymph node of a female individual in her seventies,
identified as T_CD4/CD8 subtype, with predicted labels as Double-negative thymocytes,
and majority voting as Regulatory T cells.
datasets:
- jo-mengr/geo_7k_cellxgene_3_5k_multiplets
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer
results:
- task:
type: triplet
name: Triplet
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 0.4942857027053833
name: Cosine Accuracy
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 0.900952380952381
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8415515422821045
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.8594377510040161
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7717130184173584
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8085642317380353
name: Cosine Precision
- type: cosine_recall
value: 0.9171428571428571
name: Cosine Recall
- type: cosine_ap
value: 0.8751864028703273
name: Cosine Ap
- type: cosine_mcc
value: 0.7860489453464287
name: Cosine Mcc
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained on the [geo_7k_cellxgene_3_5k_multiplets](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets) dataset. 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 Dataset:**
- [geo_7k_cellxgene_3_5k_multiplets](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets)
- **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-geo7k-cellxgene3.5k-pairs-cell_type")
# Run inference
sentences = [
'{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}}, "sample_id": "census_e84f2780-51e8-4cfa-8aa0-13bbfef677c7_184"}',
"A 46-year old female's liver sample, specifically conventional dendritic cell type 1 (cDC1s) enriched in CD45+ cell suspension, with no reported liver-related diseases.",
'Sample is an ON-bipolar cell derived from the peripheral region of the retina of a 60-year-old male with European self-reported ethnicity, mapped to GENCODE 24 reference annotation.',
]
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** | **0.4943** |
#### Binary Classification
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:--------------------------|:-----------|
| cosine_accuracy | 0.901 |
| cosine_accuracy_threshold | 0.8416 |
| cosine_f1 | 0.8594 |
| cosine_f1_threshold | 0.7717 |
| cosine_precision | 0.8086 |
| cosine_recall | 0.9171 |
| **cosine_ap** | **0.8752** |
| cosine_mcc | 0.786 |
<!--
## 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
#### geo_7k_cellxgene_3_5k_multiplets
* Dataset: [geo_7k_cellxgene_3_5k_multiplets](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets) at [d5af8a2](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets/tree/d5af8a2ef144a95afa06f7294be2686f7a610e50)
* Size: 3,150 training samples
* Columns: <code>anndata_ref</code>, <code>caption</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | anndata_ref | caption | label |
|:--------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 510 characters</li><li>mean: 512.71 characters</li><li>max: 514 characters</li></ul> | <ul><li>min: 43 characters</li><li>mean: 162.51 characters</li><li>max: 1070 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.33</li><li>max: 1.0</li></ul> |
* Samples:
| anndata_ref | caption | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/DCW3zXGDx6DWY7i/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbjeimYBdjefbpg/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/mggGyqZE6892DWz/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/Rt4wXwEPifBT2nX/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/dmkHbFpkJLLqHPx/download"}}, "sample_id": "census_a37f857c-779f-464e-9310-3db43a1811e7_2741"}</code> | <code>Sample is a macrophage cell type derived from the ileal epithelium tissue of a female human in her fourth decade.</code> | <code>1.0</code> |
| <code>{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/DCW3zXGDx6DWY7i/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbjeimYBdjefbpg/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/mggGyqZE6892DWz/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/Rt4wXwEPifBT2nX/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/dmkHbFpkJLLqHPx/download"}}, "sample_id": "census_a37f857c-779f-464e-9310-3db43a1811e7_2741"}</code> | <code>Erythrocyte cells at the mid erythroid stage, derived from bone marrow of a male human fetus at 15 weeks post-fertilization.</code> | <code>0.0</code> |
| <code>{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/DCW3zXGDx6DWY7i/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbjeimYBdjefbpg/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/mggGyqZE6892DWz/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/Rt4wXwEPifBT2nX/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/dmkHbFpkJLLqHPx/download"}}, "sample_id": "census_a37f857c-779f-464e-9310-3db43a1811e7_2741"}</code> | <code>Native cell from the spleen of a 15th week post-fertilization human female, identified as DOUBLET_IMMUNE_FIBROBLAST.</code> | <code>0.0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Evaluation Dataset
#### geo_7k_cellxgene_3_5k_multiplets
* Dataset: [geo_7k_cellxgene_3_5k_multiplets](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets) at [d5af8a2](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets/tree/d5af8a2ef144a95afa06f7294be2686f7a610e50)
* Size: 350 evaluation samples
* Columns: <code>anndata_ref</code>, <code>caption</code>, and <code>label</code>
* Approximate statistics based on the first 350 samples:
| | anndata_ref | caption | label |
|:--------|:--------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 510 characters</li><li>mean: 512.77 characters</li><li>max: 514 characters</li></ul> | <ul><li>min: 50 characters</li><li>mean: 159.74 characters</li><li>max: 924 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.33</li><li>max: 1.0</li></ul> |
* Samples:
| anndata_ref | caption | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbaoL4ydTqmYwP9/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/X8EFSis4S5ecdse/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/DGxs2PkPeDF2RGm/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/bm3N8RCWePiyJKz/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/8FGZG6EzMeBYxjX/download"}}, "sample_id": "census_b46237d1-19c6-4af2-9335-9854634bad16_7973"}</code> | <code>Sample contains stem cells (LGR5 stem) derived from the duodeno-jejunal junction of a human fetus at Carnegie stage 23.</code> | <code>1.0</code> |
| <code>{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbaoL4ydTqmYwP9/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/X8EFSis4S5ecdse/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/DGxs2PkPeDF2RGm/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/bm3N8RCWePiyJKz/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/8FGZG6EzMeBYxjX/download"}}, "sample_id": "census_b46237d1-19c6-4af2-9335-9854634bad16_7973"}</code> | <code>A 46-year old female's liver sample, specifically conventional dendritic cell type 1 (cDC1s) enriched in CD45+ cell suspension, with no reported liver-related diseases.</code> | <code>0.0</code> |
| <code>{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbaoL4ydTqmYwP9/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/X8EFSis4S5ecdse/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/DGxs2PkPeDF2RGm/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/bm3N8RCWePiyJKz/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/8FGZG6EzMeBYxjX/download"}}, "sample_id": "census_b46237d1-19c6-4af2-9335-9854634bad16_7973"}</code> | <code>A CD16-negative, CD56-bright natural killer cell sample taken from the spleen of a male in his sixth decade.</code> | <code>0.0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
#### 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`: 16
- `per_device_eval_batch_size`: 16
- `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`: False
- `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`: 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 | Validation Loss | cosine_accuracy | cosine_ap |
|:------:|:----:|:-------------:|:---------------:|:---------------:|:---------:|
| -1 | -1 | - | - | 0.5029 | - |
| 0.5076 | 100 | 6.008 | 9.9084 | 0.5057 | - |
| 1.0152 | 200 | 4.7386 | 10.5698 | 0.4943 | - |
| 0.5076 | 100 | 4.3879 | 11.5229 | 0.4943 | - |
| 1.0152 | 200 | 4.1962 | 11.7110 | 0.5 | - |
| 1.5228 | 300 | 4.2736 | 12.5341 | 0.4971 | - |
| 2.0305 | 400 | 4.1793 | 13.1011 | 0.4943 | - |
| -1 | -1 | - | - | - | 0.3408 |
| 0.1692 | 100 | 0.1614 | 0.3496 | - | 0.3389 |
| 0.3384 | 200 | 0.1641 | 0.3579 | - | 0.3390 |
| 0.5076 | 300 | 0.1652 | 0.3592 | - | 0.3396 |
| 0.6768 | 400 | 0.1672 | 0.3696 | - | 0.3413 |
| 0.8460 | 500 | 0.1579 | 0.3591 | - | 0.3417 |
| 1.0152 | 600 | 0.1722 | 0.2388 | - | 0.3457 |
| 1.1844 | 700 | 0.1553 | 0.3597 | - | 0.3866 |
| 1.3536 | 800 | 0.1029 | 0.0675 | - | 0.6485 |
| 1.5228 | 900 | 0.059 | 0.0464 | - | 0.7094 |
| 1.6920 | 1000 | 0.0446 | 0.0357 | - | 0.7133 |
| 1.8613 | 1100 | 0.035 | 0.0286 | - | 0.7571 |
| 2.0305 | 1200 | 0.0304 | 0.0226 | - | 0.8048 |
| 2.1997 | 1300 | 0.0258 | 0.0293 | - | 0.7571 |
| 2.3689 | 1400 | 0.0226 | 0.0179 | - | 0.8204 |
| 2.5381 | 1500 | 0.0207 | 0.0160 | - | 0.8292 |
| 2.7073 | 1600 | 0.0198 | 0.0166 | - | 0.8152 |
| 2.8765 | 1700 | 0.0215 | 0.0157 | - | 0.8430 |
| 3.0457 | 1800 | 0.0183 | 0.0161 | - | 0.8544 |
| 3.2149 | 1900 | 0.0163 | 0.0138 | - | 0.8651 |
| 3.3841 | 2000 | 0.0163 | 0.0142 | - | 0.8696 |
| 3.5533 | 2100 | 0.0159 | 0.0129 | - | 0.8719 |
| 3.7225 | 2200 | 0.015 | 0.0129 | - | 0.8773 |
| 3.8917 | 2300 | 0.0157 | 0.0127 | - | 0.8752 |
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.43.4
- PyTorch: 2.6.0
- 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",
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
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