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--- |
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license: mit |
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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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widget: |
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- 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." |
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- 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." |
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- 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." |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- name: sd-ner-v2 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: source_data_nlp |
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type: source_data_nlp |
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args: NER |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8030010681183889 |
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- name: Recall |
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type: recall |
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value: 0.837754771918473 |
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- name: F1 |
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type: f1 |
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value: 0.8200098518700961 |
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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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should probably proofread and complete it, then remove this comment. --> |
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# sd-ner-v2 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1551 |
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- Accuracy Score: 0.9513 |
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- Precision: 0.8030 |
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- Recall: 0.8378 |
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- F1: 0.8200 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 256 |
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- seed: 42 |
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- optimizer: Adafactor |
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- lr_scheduler_type: linear |
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- num_epochs: 2.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:| |
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| 0.1082 | 1.0 | 785 | 0.1550 | 0.9493 | 0.7826 | 0.8402 | 0.8104 | |
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| 0.073 | 2.0 | 1570 | 0.1551 | 0.9513 | 0.8030 | 0.8378 | 0.8200 | |
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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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