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judithrosell/JNLPBA_SciBERT_NER

sourceHugging Faceupdated 3y agoView on Hugging Face
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Model Card

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JNLPBASciBERTNER

This model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.1472
  • —Seqeval classification report: precision recall f1-score support

DNA 0.83 0.89 0.86 2106 RNA 0.88 0.89 0.88 3516 cellline 0.74 0.80 0.77 526 celltype 0.78 0.83 0.80 1475 protein 0.98 0.97 0.98 37428

micro avg 0.96 0.96 0.96 45051 macro avg 0.84 0.87 0.86 45051 weighted avg 0.96 0.96 0.96 45051

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
0.2331.05820.1513precision recall f1-score support

DNA 0.82 0.89 0.85 2106 RNA 0.87 0.89 0.88 3516 cellline 0.72 0.79 0.76 526 celltype 0.79 0.78 0.79 1475 protein 0.98 0.97 0.98 37428

micro avg 0.95 0.95 0.95 45051 macro avg 0.84 0.87 0.85 45051 weighted avg 0.95 0.95 0.95 45051 | | 0.138 | 2.0 | 1164 | 0.1486 | precision recall f1-score support

DNA 0.85 0.85 0.85 2106 RNA 0.89 0.87 0.88 3516 cellline 0.71 0.80 0.75 526 celltype 0.77 0.82 0.79 1475 protein 0.98 0.97 0.98 37428

micro avg 0.96 0.95 0.95 45051 macro avg 0.84 0.86 0.85 45051 weighted avg 0.96 0.95 0.96 45051 | | 0.1191 | 3.0 | 1746 | 0.1472 | precision recall f1-score support

DNA 0.83 0.89 0.86 2106 RNA 0.88 0.89 0.88 3516 cellline 0.74 0.80 0.77 526 celltype 0.78 0.83 0.80 1475 protein 0.98 0.97 0.98 37428

micro avg 0.96 0.96 0.96 45051 macro avg 0.84 0.87 0.86 45051 weighted avg 0.96 0.96 0.96 45051 |

Framework versions

  • —Transformers 4.35.2
  • —Pytorch 2.1.0+cu121
  • —Datasets 2.15.0
  • —Tokenizers 0.15.0