judithrosell/JNLPBA_SciBERT_NER
<!-- 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. -->
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
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
