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---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: plus_model
results: []
---
<!-- 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. -->
# plus_model
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2227
- Eader: {'precision': 0.6527777777777778, 'recall': 0.6308724832214765, 'f1': 0.6416382252559727, 'number': 149}
- Nswer: {'precision': 0.7374551971326165, 'recall': 0.7481818181818182, 'f1': 0.7427797833935018, 'number': 1100}
- Uestion: {'precision': 0.7554833468724614, 'recall': 0.7604251839738349, 'f1': 0.7579462102689487, 'number': 1223}
- Overall Precision: 0.7415
- Overall Recall: 0.7472
- Overall F1: 0.7443
- Overall Accuracy: 0.8544
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Eader | Nswer | Uestion | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.1843 | 0.78 | 200 | 0.2061 | {'precision': 0.6296296296296297, 'recall': 0.3422818791946309, 'f1': 0.4434782608695652, 'number': 149} | {'precision': 0.6472906403940887, 'recall': 0.5972727272727273, 'f1': 0.6212765957446809, 'number': 1100} | {'precision': 0.7054794520547946, 'recall': 0.6737530662305805, 'f1': 0.6892513592639062, 'number': 1223} | 0.6767 | 0.6197 | 0.6470 | 0.7748 |
| 0.1356 | 1.56 | 400 | 0.1477 | {'precision': 0.5701754385964912, 'recall': 0.436241610738255, 'f1': 0.49429657794676807, 'number': 149} | {'precision': 0.6569148936170213, 'recall': 0.6736363636363636, 'f1': 0.6651705565529622, 'number': 1100} | {'precision': 0.6554054054054054, 'recall': 0.713818479149632, 'f1': 0.6833659491193737, 'number': 1223} | 0.6523 | 0.6792 | 0.6655 | 0.8189 |
| 0.102 | 2.33 | 600 | 0.1666 | {'precision': 0.38974358974358975, 'recall': 0.5100671140939598, 'f1': 0.4418604651162791, 'number': 149} | {'precision': 0.6898444647758463, 'recall': 0.6854545454545454, 'f1': 0.687642498860009, 'number': 1100} | {'precision': 0.7024661893396977, 'recall': 0.7219950940310711, 'f1': 0.7120967741935483, 'number': 1223} | 0.6731 | 0.6930 | 0.6829 | 0.8246 |
| 0.0836 | 3.11 | 800 | 0.1592 | {'precision': 0.6307692307692307, 'recall': 0.5503355704697986, 'f1': 0.5878136200716846, 'number': 149} | {'precision': 0.6595012897678418, 'recall': 0.6972727272727273, 'f1': 0.6778612461334512, 'number': 1100} | {'precision': 0.7373653686826843, 'recall': 0.7277187244480785, 'f1': 0.7325102880658437, 'number': 1223} | 0.6956 | 0.7035 | 0.6995 | 0.8436 |
| 0.0657 | 3.89 | 1000 | 0.1658 | {'precision': 0.5869565217391305, 'recall': 0.5436241610738255, 'f1': 0.5644599303135888, 'number': 149} | {'precision': 0.7464788732394366, 'recall': 0.7227272727272728, 'f1': 0.7344110854503464, 'number': 1100} | {'precision': 0.7090620031796503, 'recall': 0.7293540474243663, 'f1': 0.7190648931882304, 'number': 1223} | 0.7184 | 0.7152 | 0.7168 | 0.8457 |
| 0.0462 | 4.67 | 1200 | 0.1855 | {'precision': 0.656, 'recall': 0.5503355704697986, 'f1': 0.5985401459854014, 'number': 149} | {'precision': 0.6961038961038961, 'recall': 0.730909090909091, 'f1': 0.7130820399113083, 'number': 1100} | {'precision': 0.7286392405063291, 'recall': 0.7530662305805397, 'f1': 0.7406513872135101, 'number': 1223} | 0.7103 | 0.7310 | 0.7205 | 0.8427 |
| 0.0441 | 5.45 | 1400 | 0.1721 | {'precision': 0.6538461538461539, 'recall': 0.5704697986577181, 'f1': 0.6093189964157705, 'number': 149} | {'precision': 0.7275179856115108, 'recall': 0.7354545454545455, 'f1': 0.7314647377938518, 'number': 1100} | {'precision': 0.7504065040650406, 'recall': 0.7547015535568274, 'f1': 0.7525479005299633, 'number': 1223} | 0.7350 | 0.7350 | 0.7350 | 0.8555 |
| 0.0347 | 6.23 | 1600 | 0.2052 | {'precision': 0.6312056737588653, 'recall': 0.5973154362416108, 'f1': 0.6137931034482759, 'number': 149} | {'precision': 0.715929203539823, 'recall': 0.7354545454545455, 'f1': 0.7255605381165919, 'number': 1100} | {'precision': 0.7475728155339806, 'recall': 0.7555192150449714, 'f1': 0.7515250101667346, 'number': 1223} | 0.7268 | 0.7371 | 0.7319 | 0.8545 |
| 0.0294 | 7.0 | 1800 | 0.2374 | {'precision': 0.6190476190476191, 'recall': 0.610738255033557, 'f1': 0.6148648648648649, 'number': 149} | {'precision': 0.7442075996292864, 'recall': 0.73, 'f1': 0.7370353373106929, 'number': 1100} | {'precision': 0.7593671940049959, 'recall': 0.7457072771872445, 'f1': 0.7524752475247525, 'number': 1223} | 0.7441 | 0.7306 | 0.7373 | 0.8401 |
| 0.0239 | 7.78 | 2000 | 0.2227 | {'precision': 0.647887323943662, 'recall': 0.6174496644295302, 'f1': 0.6323024054982819, 'number': 149} | {'precision': 0.7139061116031886, 'recall': 0.7327272727272728, 'f1': 0.7231942575145804, 'number': 1100} | {'precision': 0.7662229617304492, 'recall': 0.7530662305805397, 'f1': 0.7595876288659793, 'number': 1223} | 0.7355 | 0.7358 | 0.7357 | 0.8472 |
| 0.0204 | 8.56 | 2200 | 0.2263 | {'precision': 0.6375838926174496, 'recall': 0.6375838926174496, 'f1': 0.6375838926174496, 'number': 149} | {'precision': 0.7371737173717372, 'recall': 0.7445454545454545, 'f1': 0.7408412483039349, 'number': 1100} | {'precision': 0.7540453074433657, 'recall': 0.7620605069501226, 'f1': 0.7580317202114681, 'number': 1223} | 0.7396 | 0.7468 | 0.7432 | 0.8539 |
| 0.0184 | 9.34 | 2400 | 0.2227 | {'precision': 0.6527777777777778, 'recall': 0.6308724832214765, 'f1': 0.6416382252559727, 'number': 149} | {'precision': 0.7374551971326165, 'recall': 0.7481818181818182, 'f1': 0.7427797833935018, 'number': 1100} | {'precision': 0.7554833468724614, 'recall': 0.7604251839738349, 'f1': 0.7579462102689487, 'number': 1223} | 0.7415 | 0.7472 | 0.7443 | 0.8544 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0.dev20230810
- Datasets 2.14.4
- Tokenizers 0.14.1
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