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license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: F_Roberta_classifier2
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. -->
# F_Roberta_classifier2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1317
- Accuracy: 0.9751
- F1: 0.9751
- Precision: 0.9751
- Recall: 0.9751
- C Report: precision recall f1-score support
0 0.97 0.98 0.98 1467
1 0.98 0.97 0.98 1466
accuracy 0.98 2933
macro avg 0.98 0.98 0.98 2933
weighted avg 0.98 0.98 0.98 2933
- C Matrix: None
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | C Report | C Matrix |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
| 0.1626 | 1.0 | 614 | 0.0936 | 0.9707 | 0.9707 | 0.9707 | 0.9707 | precision recall f1-score support
0 0.97 0.97 0.97 1467
1 0.97 0.97 0.97 1466
accuracy 0.97 2933
macro avg 0.97 0.97 0.97 2933
weighted avg 0.97 0.97 0.97 2933
| None |
| 0.0827 | 2.0 | 1228 | 0.0794 | 0.9731 | 0.9731 | 0.9731 | 0.9731 | precision recall f1-score support
0 0.96 0.98 0.97 1467
1 0.98 0.96 0.97 1466
accuracy 0.97 2933
macro avg 0.97 0.97 0.97 2933
weighted avg 0.97 0.97 0.97 2933
| None |
| 0.0525 | 3.0 | 1842 | 0.1003 | 0.9737 | 0.9737 | 0.9737 | 0.9737 | precision recall f1-score support
0 0.97 0.98 0.97 1467
1 0.98 0.97 0.97 1466
accuracy 0.97 2933
macro avg 0.97 0.97 0.97 2933
weighted avg 0.97 0.97 0.97 2933
| None |
| 0.0329 | 4.0 | 2456 | 0.1184 | 0.9751 | 0.9751 | 0.9751 | 0.9751 | precision recall f1-score support
0 0.98 0.97 0.98 1467
1 0.97 0.98 0.98 1466
accuracy 0.98 2933
macro avg 0.98 0.98 0.98 2933
weighted avg 0.98 0.98 0.98 2933
| None |
| 0.0179 | 5.0 | 3070 | 0.1317 | 0.9751 | 0.9751 | 0.9751 | 0.9751 | precision recall f1-score support
0 0.97 0.98 0.98 1467
1 0.98 0.97 0.98 1466
accuracy 0.98 2933
macro avg 0.98 0.98 0.98 2933
weighted avg 0.98 0.98 0.98 2933
| None |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1
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