readme: ---
  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
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # sd-ner-v2
  
  This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/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
  