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jantrienes/roberta-large-question-classifier

sourceHugging Facemitupdated 2y agoView on Hugging Face

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roberta-large-question-classifier

This model classifies questions according to the question-type ontology defined in following paper: Controllable Open-ended Question Generation with A New Question Type Ontology (Cao & Wang, ACL-IJCNLP 2021). It is a fine-tuned roberta-large on the open_question_type dataset. It achieves the following results on the test set:

              precision    recall  f1-score   support
       cause       0.91      0.93      0.92        91
  comparison       0.62      0.83      0.71        30
     concept       0.85      0.65      0.74        54
 consequence       0.80      0.73      0.76        11
 disjunction       0.80      0.78      0.79        36
     example       0.83      0.85      0.84       139
      extent       0.82      0.94      0.87        48
  judgmental       0.68      0.56      0.62        94
  procedural       0.86      0.88      0.87        85
verification       0.79      0.86      0.83        72
    accuracy                           0.81       660
   macro avg       0.80      0.80      0.80       660
weighted avg       0.81      0.81      0.81       660

Training procedure

Script: https://gist.github.com/jantrienes/329479bdad6b2a239cfcea83b9159a8a

Training hyperparameters

The following hyperparameters were used during training:

—learning_rate: 2e-05
—trainbatchsize: 16
—evalbatchsize: 512
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_ratio: 0.1
—num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	F1
1.9467	1.0	233	1.3099	0.4050
0.6381	2.0	466	0.5586	0.7785
0.628	3.0	699	0.6419	0.7831
0.4487	4.0	932	0.5770	0.8094
0.3319	5.0	1165	0.7713	0.7953
0.2095	6.0	1398	0.8799	0.8018
0.1355	7.0	1631	1.0646	0.7961
0.0956	8.0	1864	1.2175	0.7999
0.0687	9.0	2097	1.3647	0.7892
0.0371	10.0	2330	1.3809	0.7987
0.0303	11.0	2563	1.3591	0.8123
0.0263	12.0	2796	1.5317	0.8100
0.0144	13.0	3029	1.5726	0.7959
0.0436	14.0	3262	1.6160	0.7988
0.0048	15.0	3495	1.6826	0.7957
0.0001	16.0	3728	1.6913	0.7957
0.0001	17.0	3961	1.7076	0.7995
0.0034	18.0	4194	1.8018	0.7960
0.0228	19.0	4427	1.7457	0.7916
0.0083	20.0	4660	1.9279	0.7869
0.0001	21.0	4893	1.8367	0.7915
0.0003	22.0	5126	1.8620	0.7842
0.0002	23.0	5359	1.9192	0.7828
0.0	24.0	5592	1.9081	0.7927
0.0003	25.0	5825	1.9822	0.7813
0.0059	26.0	6058	1.8737	0.7954
0.0	27.0	6291	1.8793	0.7929
0.0	28.0	6524	1.8905	0.7940
0.0	29.0	6757	1.8971	0.7940
0.0002	30.0	6990	1.9002	0.7954

Framework versions

—Transformers 4.33.2
—Pytorch 2.1.0+cu118
—Datasets 2.14.5
—Tokenizers 0.13.3