metadata

license: apache-2.0
base_model: Dr-BERT/DrBERT-7GB
tags:
  - generated_from_trainer
datasets:
  - quaero
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: drbert-7gb-finedtuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: quaero
          type: quaero
          config: medline
          split: validation
          args: medline
        metrics:
          - name: Precision
            type: precision
            value: 0.5055292259083728
          - name: Recall
            type: recall
            value: 0.5696484201157098
          - name: F1
            type: f1
            value: 0.5356769198577107
          - name: Accuracy
            type: accuracy
            value: 0.8004338394793926

drbert-7gb-finedtuned-ner

This model is a fine-tuned version of Dr-BERT/DrBERT-7GB on the quaero dataset. It achieves the following results on the evaluation set:

Loss: 1.2330
Precision: 0.5055
Recall: 0.5696
F1: 0.5357
Accuracy: 0.8004

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	105	0.7430	0.4129	0.4775	0.4428	0.7671
No log	2.0	210	0.6968	0.4888	0.5042	0.4964	0.7888
No log	3.0	315	0.8218	0.5059	0.5323	0.5188	0.7952
No log	4.0	420	0.9307	0.4869	0.5563	0.5193	0.7913
0.4134	5.0	525	0.9970	0.4688	0.5581	0.5095	0.7870
0.4134	6.0	630	1.0503	0.4992	0.5541	0.5252	0.7930
0.4134	7.0	735	1.1364	0.5034	0.5607	0.5305	0.7994
0.4134	8.0	840	1.1994	0.4865	0.5701	0.5250	0.7937
0.4134	9.0	945	1.2287	0.4948	0.5683	0.5290	0.7982
0.028	10.0	1050	1.2330	0.5055	0.5696	0.5357	0.8004

Framework versions

Transformers 4.38.1
Pytorch 2.1.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2