Dr. Jorge Abreu Vicente
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update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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- science
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- medical
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- biomedical
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- biocuration
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- sourcedata
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datasets:
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- source_data_nlp
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widget:
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- text: "XPT of siRNA treated [MASK] cells after 48 hours of knockdown. Treated cells were fed with the indicated amounts of C8L peptid conjugated to iron oxide beads via a disulfide bond. The cells were then exposed to RF33. 70-Luc Reporter [MASK] T cells overnight. Error bars show SD of >3 replicate wells. * p<0.05 for siRNA vs control [MASK] using two-way ANOVA. Representative plot of 3 independent experiments."
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- text: "The [MASK] intensity along the line across a lipid droplet in (A) was measured by ImageJ.The lipid droplet localization of [MASK]-[MASK], represented by two peaks, is clearly visible in fat cells from ppl > [MASK] larvae , but it is lost in fat cells from ppl > [MASK] larvae with [MASK] RNAi or overexpression of [MASK]/[MASK]. More than 30 lipid droplets of each genotype were measured. One typical image curve is shown for each genotype."
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metrics:
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- precision
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- recall
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metrics:
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- name: Precision
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type: precision
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value: 0.
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- name: Recall
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type: recall
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value: 0.
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- name: F1
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type: f1
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value: 0.
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language:
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- en
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pipeline_tag: token-classification
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on the source_data_nlp dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy Score: 0.9950
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
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| 0.
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### Framework versions
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- Transformers 4.20.0
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- Pytorch 1.11.0a0+bfe5ad2
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- Datasets 1.17.0
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- Tokenizers 0.12.1
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- source_data_nlp
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metrics:
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- precision
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- recall
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metrics:
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- name: Precision
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type: precision
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value: 0.9227577212638568
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- name: Recall
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type: recall
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value: 0.9288143683990692
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- name: F1
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type: f1
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value: 0.9257761389318425
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on the source_data_nlp dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0136
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- Accuracy Score: 0.9950
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- Precision: 0.9228
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- Recall: 0.9288
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- F1: 0.9258
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
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| 0.014 | 1.0 | 1569 | 0.0136 | 0.9950 | 0.9228 | 0.9288 | 0.9258 |
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### Framework versions
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- Transformers 4.20.0
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- Pytorch 1.11.0a0+bfe5ad2
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- Datasets 1.17.0
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- Tokenizers 0.12.1
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