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---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
datasets:
- launch/open_question_type
metrics:
- f1
model-index:
- name: roberta-large-question-classifier
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: launch/open_question_type
type: launch/open_question_type
config: default
split: validation
args: default
metrics:
- name: F1 (macro avg.)
type: f1
value: 0.8123190611646329
- task:
name: Text Classification
type: text-classification
dataset:
name: launch/open_question_type
type: launch/open_question_type
config: default
split: test
args: default
metrics:
- name: F1 (macro avg.)
type: f1
value: 0.8
widget:
- text: When two bacteria exchange genetic information, what is the process called?
language:
- en
arxiv: 2107.00152
---
# 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](https://aclanthology.org/2021.acl-long.502/) (Cao & Wang, ACL-IJCNLP 2021).
It is a fine-tuned [roberta-large](https://huggingface.co./roberta-large) on the [open_question_type](https://huggingface.co./datasets/launch/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
- train_batch_size: 16
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_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 |