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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
language:
- en
---
# roberta-large-question-classifier
This model is a fine-tuned version of [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
### 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 |