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judithrosell/JNLPBA_PubMedBERT_NER

sourceHugging Facemitupdated 3y agoView on Hugging Face
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JNLPBAPubMedBERTNER

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.1450
  • —Seqeval classification report: precision recall f1-score support

DNA 0.75 0.83 0.79 955 RNA 0.80 0.83 0.82 1144 cellline 0.76 0.79 0.78 5330 celltype 0.86 0.91 0.88 2518 protein 0.87 0.85 0.86 926

micro avg 0.80 0.83 0.81 10873 macro avg 0.81 0.84 0.82 10873 weighted avg 0.80 0.83 0.81 10873

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: 2e-05
  • —trainbatchsize: 16
  • —evalbatchsize: 16
  • —seed: 42
  • —gradientaccumulationsteps: 2
  • —totaltrainbatch_size: 32
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —num_epochs: 3

Training results

Training LossEpochStepValidation LossSeqeval classification report
0.27261.05820.1526precision recall f1-score support

DNA 0.73 0.82 0.77 955 RNA 0.79 0.82 0.81 1144 cellline 0.75 0.78 0.76 5330 celltype 0.86 0.86 0.86 2518 protein 0.86 0.84 0.85 926

micro avg 0.79 0.81 0.80 10873 macro avg 0.80 0.82 0.81 10873 weighted avg 0.79 0.81 0.80 10873 | | 0.145 | 2.0 | 1164 | 0.1473 | precision recall f1-score support

DNA 0.73 0.82 0.77 955 RNA 0.85 0.78 0.81 1144 cellline 0.77 0.78 0.78 5330 celltype 0.85 0.92 0.88 2518 protein 0.88 0.83 0.85 926

micro avg 0.80 0.82 0.81 10873 macro avg 0.81 0.83 0.82 10873 weighted avg 0.80 0.82 0.81 10873 | | 0.1276 | 3.0 | 1746 | 0.1450 | precision recall f1-score support

DNA 0.75 0.83 0.79 955 RNA 0.80 0.83 0.82 1144 cellline 0.76 0.79 0.78 5330 celltype 0.86 0.91 0.88 2518 protein 0.87 0.85 0.86 926

micro avg 0.80 0.83 0.81 10873 macro avg 0.81 0.84 0.82 10873 weighted avg 0.80 0.83 0.81 10873 |

Framework versions

  • —Transformers 4.35.2
  • —Pytorch 2.1.0+cu118
  • —Datasets 2.15.0
  • —Tokenizers 0.15.0