README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - source_data_nlp
widget:
  - text: >-
      Confocal images of Bmm-GFP (green) in 3rd instar larval fat bodies of
      different genotypes. DAPI (blue) stains nuclei. Scale bar represents 25
      µm. (A) Knocking down CSN2 or overexpressing RDH/CG2064 in animals with
      DGAT1 overexpression (ppl>DGAT1) decreases the level and lipid droplet
      localization of Bmm-GFP.
  - text: >-
      The GFP intensity along the line across a lipid droplet in (A) was
      measured by ImageJ.The lipid droplet localization of Bmm-GFP, represented
      by two peaks, is clearly visible in fat cells from ppl > DGAT1 larvae ,
      but it is lost in fat cells from ppl > DGAT1 larvae with CSN2 RNAi or
      overexpression of RDH/CG2064. More than 30 lipid droplets of each genotype
      were measured. One typical image curve is shown for each genotype.
  - text: >-
      XPT of siRNA treated DC3. 2R 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 CD8 T cells overnight. Error bars show SD of >3 replicate
      wells. * p<0.05 for siRNA vs control I-Ab using two-way ANOVA.
      Representative plot of 3 independent experiments.
metrics:
  - precision
  - recall
  - f1
  - name: sd-ner-v2
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: source_data_nlp
          type: source_data_nlp
          args: NER
        metrics:
          - name: Precision
            type: precision
            value: 0.8030010681183889
          - name: Recall
            type: recall
            value: 0.837754771918473
          - name: F1
            type: f1
            value: 0.8200098518700961

sd-ner-v2

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract on the source_data_nlp dataset. It achieves the following results on the evaluation set:

• Loss: 0.1551
• Accuracy Score: 0.9513
• Precision: 0.8030
• Recall: 0.8378
• F1: 0.8200

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 64
• eval_batch_size: 256
• seed: 42
• optimizer: Adafactor
• lr_scheduler_type: linear
• num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy Score	Precision	Recall	F1
0.1082	1.0	785	0.1550	0.9493	0.7826	0.8402	0.8104
0.073	2.0	1570	0.1551	0.9513	0.8030	0.8378	0.8200

Framework versions

• Transformers 4.20.0
• Pytorch 1.11.0a0+bfe5ad2
• Datasets 1.17.0
• Tokenizers 0.12.1