Model: Saideepthi55/sentencetransformer-ft
Author: Saideepthi55
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, mpnet, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:117502, loss:MultipleNegativesRankingLoss, en, dataset:SwastikN/sxc_med_llm_chemical_gen, arxiv:1908.10084, arxiv:1705.00652, base_model:microsoft/mpnet-base, base_model:finetune:microsoft/mpnet-base, license:apache-2.0, model-index, endpoints_compatible, region:us
Base models (finetune): id: microsoft/mpnet-base
Card metadata: base_model: microsoft/mpnet-base
datasets: SwastikN/sxc_med_llm_chemical_gen
language: en
library_name: sentence-transformers
license: apache-2.0
metrics: cosine_accuracy, dot_accuracy, manhattan_accuracy, euclidean_accuracy, max_accuracy
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:117502, loss:MultipleNegativesRankingLoss
widget: source_sentence: Help me make the molecule CC(=O)OC[C@H](OC(C)=O)C(=O)N1CCCC[C@H]1C1CCN(C(=O)c2cc3ccccc3n2C)CC1 with the same hydrogen bond donors. The output molecule should be similar to the input molecule. Please inform me of the number of hydrogen bond donor(s) of the optimized molecule.
sentences: Your requirements guided the optimization, resulting in the molecule "CC(=O)OC(CCl)C(Cc1cccs1)[C@H](OC(C)=O)C(=O)N1CCCC[C@H]1C1CCN(C(=O)c2cc3ccccc3n2C)CC1" with an approximate hydrogen bond donor(s) of 0., Given a molecule expressed in SMILES string, help me optimize it according to my requirements., Help me adapt a molecular structure denoted in SMILES string based on my preferences., source_sentence: How can we modify the molecule CCC(CC)=C(CC)c1ccccc1OC(=O)OC(N=[N+]=[N-])c1ccccc1 to decrease its blood-brain barrier penetration (BBBP) value while keeping it similar to the input molecule? Please inform me of the BBBP value of the optimized molecule.
sentences: Describe a technology used for measuring people's emotional responses., I've successfully optimized the molecule according to your needs, resulting in "CCOC(=O)c1ccccc1OC(=O)OC(N=[N+]=[N-])c1ccccc1" with an approximate BBBP value of 0.71., Given a molecule expressed in SMILES string, help me optimize it according to my requirements., source_sentence: How can we modify the molecule C/C(=C/C(=O)N1CC[C@H](CC(CCCCCC(CO)C(=O)O)NC(=O)OC(C)(C)C)[C@H]1c1cccnc1)C(=O)O to increase its blood-brain barrier penetration (BBBP) value while keeping it similar to the input molecule?
sentences: Given a molecule expressed in SMILES string, help me optimize it according to my requirements., Aid me in refining a molecular structure written in SMILES notation based on my criteria., Taking your requirements into account, I've optimized the molecule to "C/C(=C/C(=O)N1CC[C@H](CNC(=O)[C@H](CO)NC(=O)OC(C)(C)C)[C@H]1c1cccnc1)C(=O)O"., source_sentence: Support me in transforming the molecule [SMILES] by incorporating the same hydrogen bond acceptors and maintaining its resemblance to the original molecule.
sentences: Taking your requirements into account, I've optimized the molecule to "CCOc1cccc(C2c3c(oc4ccc(C)cc4c3=O)C(=O)N2CCN(CC)CC)c1"., Help me adapt a molecular structure denoted in SMILES string based on my preferences., Help me adapt a molecular structure denoted in SMILES string based on my preferences., source_sentence: With a molecule represented by the SMILES string CNNNCC(=O)N[C@H](C)C[C@@H](C)NCc1ccc2c(c1)CCC2, propose adjustments that can increase its logP value while keeping the output molecule structurally related to the input molecule.
sentences: Aid me in refining a molecular structure written in SMILES notation based on my criteria., Given a molecule expressed in SMILES string, help me optimize it according to my requirements., In line with your criteria, I've optimized the molecule and present it as "C[C@H](C[C@@H](C)NC(=O)COC(C)(C)C)NCc1ccc2c(c1)CCC2".
model-index: name: MPNet base trained on AllNLI triplets
results: task: type: triplet
name: Triplet
dataset: name: all nli dev
type: all-nli-dev
metrics: type: cosine_accuracy
value: 0.6562222222222223
name: Cosine Accuracy
verified: no, type: dot_accuracy
value: 0.5342222222222223
name: Dot Accuracy
verified: no, type: manhattan_accuracy
value: 0.7075555555555556
name: Manhattan Accuracy
verified: no, type: euclidean_accuracy
value: 0.6584444444444445
name: Euclidean Accuracy
verified: no, type: max_accuracy
value: 0.7075555555555556
name: Max Accuracy
verified: no, type: cosine_accuracy
value: 0.9804444444444445
name: Cosine Accuracy
verified: no, type: dot_accuracy
value: 0.01888888888888889
name: Dot Accuracy
verified: no, type: manhattan_accuracy
value: 0.9811111111111112
name: Manhattan Accuracy
verified: no, type: euclidean_accuracy
value: 0.9802222222222222
name: Euclidean Accuracy
verified: no, type: max_accuracy
value: 0.9811111111111112
name: Max Accuracy
verified: no
Likes: 0 | All-time downloads: 14
Created: 2024-09-14 15:37:49