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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:4247 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
|
- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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widget: |
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- source_sentence: The Opa1 protein localizes to the mitochondria.Opa1 is found normally |
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in the mitochondrial intermembrane space. |
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sentences: |
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- Which is the cellular localization of the protein Opa1? |
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- Which are the genes responsible for Dyskeratosis Congenita? |
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- List blood marker for Non-Hodgkin lymphoma. |
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- source_sentence: CorrSite identifies potential allosteric ligand-binding sites based |
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on motion correlation analyses between cavities.We find that CARDS captures allosteric |
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communication between the two cAMP-Binding Domains (CBDs)Overall, it is demonstrated |
|
that the communication pathways could be multiple and intrinsically disposed, |
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and the MC path generation approach provides an effective tool for the prediction |
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of key residues that mediate the allosteric communication in an ensemble of pathways |
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and functionally plausible residuesWe utilized a data set of 24 known allosteric |
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sites from 23 monomer proteins to calculate the correlations between potential |
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ligand-binding sites and corresponding orthosteric sites using a Gaussian network |
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model (GNM)Here, we introduce the Correlation of All Rotameric and Dynamical States |
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(CARDS) framework for quantifying correlations between both the structure and |
|
disorder of different regions of a proteinWe present a novel method, "MutInf", |
|
to identify statistically significant correlated motions from equilibrium molecular |
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dynamics simulationsCorrSite identifies potential allosteric ligand-binding sites |
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based on motion correlation analyses between cavities.Here, a Monte Carlo (MC) |
|
path generation approach is proposed and implemented to define likely allosteric |
|
pathways through generating an ensemble of maximum probability paths.Here, a Monte |
|
Carlo (MC) path generation approach is proposed and implemented to define likely |
|
allosteric pathways through generating an ensemble of maximum probability paths. |
|
Overall, it is demonstrated that the communication pathways could be multiple |
|
and intrinsically disposed, and the MC path generation approach provides an effective |
|
tool for the prediction of key residues that mediate the allosteric communication |
|
in an ensemble of pathways and functionally plausible residues We utilized a data |
|
set of 24 known allosteric sites from 23 monomer proteins to calculate the correlations |
|
between potential ligand-binding sites and corresponding orthosteric sites using |
|
a Gaussian network model (GNM)A Monte Carlo (MC) path generation approach is proposed |
|
and implemented to define likely allosteric pathways through generating an ensemble |
|
of maximum probability paths. A novel method, "MutInf", to identify statistically |
|
significant correlated motions from equilibrium molecular dynamics simulations. |
|
CorrSite identifies potential alloster-binding sites based on motion correlation |
|
analyses between cavities. The Correlation of All Rotameric and Dynamical States |
|
(CARDS) framework for quantifying correlations between both the structure and |
|
disorder of different regions of a proteinComputational tools for predicting allosteric |
|
pathways in proteins include MCPath, MutInf, pySCA, CorrSite, and CARDS. |
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sentences: |
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- Computational tools for predicting allosteric pathways in proteins |
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- What is PANTHER-PSEP? |
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- What illness is transmitted by the Lone Star Tick, Amblyomma americanum? |
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- source_sentence: "Dopaminergic drugs should be given in patients with BMS. \nCatuama\ |
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\ reduces the symptoms of BMS and may be a novel therapeutic strategy for the\ |
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\ treatment of this disease.\nCapsaicin, alpha-lipoic acid (ALA), and clonazepam\ |
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\ were those that showed more reduction in symptoms of BMS.\nTreatment with placebos\ |
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\ produced a response that was 72% as large as the response to active drugs" |
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sentences: |
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- What is the cyberknife used for? |
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- Which compounds exist that are thyroid hormone analogs? |
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- Which are the drugs utilized for the burning mouth syndrome? |
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- source_sentence: Tinea is a superficial fungal infections of the skin. |
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sentences: |
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- Which molecule is targeted by a monoclonal antibody Mepolizumab? |
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- What disease is tinea ? |
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- Which algorithm is used for detection of long repeat expansions? |
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- source_sentence: Basset is an open source package which applies CNNs to learn the |
|
functional activity of DNA sequences from genomics data. Basset was trained on |
|
a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, |
|
and demonstrated greater predictive accuracy than previous methods. Basset predictions |
|
for the change in accessibility between variant alleles were far greater for Genome-wide |
|
association study (GWAS) SNPs that are likely to be causal relative to nearby |
|
SNPs in linkage disequilibrium with them. With Basset, a researcher can perform |
|
a single sequencing assay in their cell type of interest and simultaneously learn |
|
that cell's chromatin accessibility code and annotate every mutation in the genome |
|
with its influence on present accessibility and latent potential for accessibility. |
|
Thus, Basset offers a powerful computational approach to annotate and interpret |
|
the noncoding genome. |
|
sentences: |
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- Givosiran is used for treatment of which disease? |
|
- Describe the applicability of Basset in the context of deep learning |
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- What is the causative agent of the "Panama disease" affecting bananas? |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: BGE base BioASQ Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8432203389830508 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9427966101694916 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.961864406779661 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9788135593220338 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8432203389830508 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3142655367231638 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19237288135593222 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0978813559322034 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8432203389830508 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9427966101694916 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.961864406779661 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9788135593220338 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9167805960832026 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8963327280064567 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8971987609787653 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8538135593220338 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9427966101694916 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.961864406779661 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9745762711864406 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8538135593220338 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3142655367231638 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19237288135593222 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09745762711864407 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8538135593220338 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9427966101694916 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.961864406779661 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9745762711864406 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9198462326957965 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9016772598870054 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9026755533837086 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8453389830508474 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9385593220338984 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9555084745762712 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9745762711864406 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8453389830508474 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3128531073446327 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19110169491525425 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09745762711864407 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8453389830508474 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9385593220338984 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9555084745762712 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9745762711864406 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.914207272128957 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8944528517621736 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8952712251263324 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
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type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8220338983050848 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9279661016949152 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9449152542372882 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9703389830508474 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8220338983050848 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3093220338983051 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.18898305084745767 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09703389830508474 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8220338983050848 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9279661016949152 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9449152542372882 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9703389830508474 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.901534580728345 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8789800242130752 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8801051507894794 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base BioASQ Matryoshka |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
- **Language:** en |
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- **License:** apache-2.0 |
|
|
|
### 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): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## 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("pavanmantha/bge-base-en-bioembed768") |
|
# Run inference |
|
sentences = [ |
|
"Basset is an open source package which applies CNNs to learn the functional activity of DNA sequences from genomics data. Basset was trained on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrated greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.", |
|
'Describe the applicability of Basset in the context of deep learning', |
|
'What is the causative agent of the "Panama disease" affecting bananas?', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# 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 |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_768` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.8432 | |
|
| cosine_accuracy@3 | 0.9428 | |
|
| cosine_accuracy@5 | 0.9619 | |
|
| cosine_accuracy@10 | 0.9788 | |
|
| cosine_precision@1 | 0.8432 | |
|
| cosine_precision@3 | 0.3143 | |
|
| cosine_precision@5 | 0.1924 | |
|
| cosine_precision@10 | 0.0979 | |
|
| cosine_recall@1 | 0.8432 | |
|
| cosine_recall@3 | 0.9428 | |
|
| cosine_recall@5 | 0.9619 | |
|
| cosine_recall@10 | 0.9788 | |
|
| cosine_ndcg@10 | 0.9168 | |
|
| cosine_mrr@10 | 0.8963 | |
|
| **cosine_map@100** | **0.8972** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_512` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.8538 | |
|
| cosine_accuracy@3 | 0.9428 | |
|
| cosine_accuracy@5 | 0.9619 | |
|
| cosine_accuracy@10 | 0.9746 | |
|
| cosine_precision@1 | 0.8538 | |
|
| cosine_precision@3 | 0.3143 | |
|
| cosine_precision@5 | 0.1924 | |
|
| cosine_precision@10 | 0.0975 | |
|
| cosine_recall@1 | 0.8538 | |
|
| cosine_recall@3 | 0.9428 | |
|
| cosine_recall@5 | 0.9619 | |
|
| cosine_recall@10 | 0.9746 | |
|
| cosine_ndcg@10 | 0.9198 | |
|
| cosine_mrr@10 | 0.9017 | |
|
| **cosine_map@100** | **0.9027** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.8453 | |
|
| cosine_accuracy@3 | 0.9386 | |
|
| cosine_accuracy@5 | 0.9555 | |
|
| cosine_accuracy@10 | 0.9746 | |
|
| cosine_precision@1 | 0.8453 | |
|
| cosine_precision@3 | 0.3129 | |
|
| cosine_precision@5 | 0.1911 | |
|
| cosine_precision@10 | 0.0975 | |
|
| cosine_recall@1 | 0.8453 | |
|
| cosine_recall@3 | 0.9386 | |
|
| cosine_recall@5 | 0.9555 | |
|
| cosine_recall@10 | 0.9746 | |
|
| cosine_ndcg@10 | 0.9142 | |
|
| cosine_mrr@10 | 0.8945 | |
|
| **cosine_map@100** | **0.8953** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_128` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.822 | |
|
| cosine_accuracy@3 | 0.928 | |
|
| cosine_accuracy@5 | 0.9449 | |
|
| cosine_accuracy@10 | 0.9703 | |
|
| cosine_precision@1 | 0.822 | |
|
| cosine_precision@3 | 0.3093 | |
|
| cosine_precision@5 | 0.189 | |
|
| cosine_precision@10 | 0.097 | |
|
| cosine_recall@1 | 0.822 | |
|
| cosine_recall@3 | 0.928 | |
|
| cosine_recall@5 | 0.9449 | |
|
| cosine_recall@10 | 0.9703 | |
|
| cosine_ndcg@10 | 0.9015 | |
|
| cosine_mrr@10 | 0.879 | |
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| **cosine_map@100** | **0.8801** | |
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### Recommendations |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 4,247 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | positive | anchor | |
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|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 102.44 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.78 tokens</li><li>max: 44 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------| |
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| <code>Restless legs syndrome (RLS), also known as Willis-Ekbom disease (WED), is a common movement disorder characterized by an uncontrollable urge to move because of uncomfortable, sometimes painful sensations in the legs with a diurnal variation and a release with movement.</code> | <code>Willis-Ekbom disease is also known as?</code> | |
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| <code>Report the outcomes of laser in situ keratomileusis (LASIK) for high myopia correction after long-term follow-up['Report the outcomes of laser in situ keratomileusis (LASIK) for high myopia correction after long-term follow-up.']Laser in situ keratomileusis is also known as LASIKLaser in situ keratomileusis (LASIK)</code> | <code>What is another name for keratomileusis?</code> | |
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| <code>CellMaps is an HTML5 open-source web tool that allows displaying, editing, exploring and analyzing biological networks as well as integrating metadata into them.CellMaps is an HTML5 open-source web tool that allows displaying, editing, exploring and analyzing biological networks as well as integrating metadata into them. CellMaps can easily be integrated in any web page by using an available JavaScript API. Computations and analyses are remotely executed in high-end servers, and all the functionalities are available through RESTful web services. CellMaps is an HTML5 open-source web tool that allows displaying, editing, exploring and analyzing biological networks as well as integrating metadata into them. Computations and analyses are remotely executed in high-end servers, and all the functionalities are available through RESTful web services. CellMaps can easily be integrated in any web page by using an available JavaScript API. CellMaps is an HTML5 open-source web tool that allows displaying, editing, exploring and analyzing biological networks as well as integrating metadata into them. Computations and analyses are remotely executed in high-end servers, and all the functionalities are available through RESTful web services. CellMaps can easily be integrated in any web page by using an available JavaScript API.CellMaps is an HTML5 open-source web tool that allows displaying, editing, exploring and analyzing biological networks as well as integrating metadata into them. CellMaps is an HTML5 open-source web tool that allows displaying, editing, exploring and analyzing biological networks as well as integrating metadata into them. CellMaps can easily be integrated in any web page by using an available JavaScript API. Computations and analyses are remotely executed in high-end servers, and all the functionalities are available through RESTful web services.</code> | <code>What is CellMaps?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 10 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `tf32`: False |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 10 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: False |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 | |
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|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| |
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| 0.9624 | 8 | - | 0.8560 | 0.8821 | 0.8904 | 0.8876 | |
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| 1.2030 | 10 | 1.2833 | - | - | - | - | |
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| 1.9248 | 16 | - | 0.8655 | 0.8808 | 0.8909 | 0.8889 | |
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| 2.4060 | 20 | 0.4785 | - | - | - | - | |
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| 2.8872 | 24 | - | 0.8720 | 0.8875 | 0.8893 | 0.8921 | |
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| 3.6090 | 30 | 0.2417 | - | - | - | - | |
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| 3.9699 | 33 | - | 0.8751 | 0.8924 | 0.8955 | 0.8960 | |
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| 4.8120 | 40 | 0.1607 | - | - | - | - | |
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| 4.9323 | 41 | - | 0.8799 | 0.8932 | 0.8964 | 0.8952 | |
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| 5.8947 | 49 | - | 0.8785 | 0.8944 | 0.9009 | 0.8982 | |
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| 6.0150 | 50 | 0.1152 | - | - | - | - | |
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| **6.9774** | **58** | **-** | **0.8803** | **0.8947** | **0.9018** | **0.8975** | |
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| 7.2180 | 60 | 0.0924 | - | - | - | - | |
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| 7.9398 | 66 | - | 0.8802 | 0.8956 | 0.9016 | 0.8973 | |
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| 8.4211 | 70 | 0.0832 | - | - | - | - | |
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| 8.9023 | 74 | - | 0.8801 | 0.8956 | 0.9027 | 0.8972 | |
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| 9.6241 | 80 | 0.074 | 0.8801 | 0.8953 | 0.9027 | 0.8972 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.13 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.1.2 |
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- Accelerate: 0.31.0 |
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- Datasets: 2.19.2 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MatryoshkaLoss |
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```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
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year={2024}, |
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eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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