File size: 2,694 Bytes
ce7f9b5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: PHR_Suicide_Prediction_Roberta_Cleaned_Light
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. -->
# PHR_Suicide_Prediction_Roberta_Cleaned_Light
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0553
- Accuracy: 0.9869
- Recall: 0.9846
- Precision: 0.9904
- F1: 0.9875
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1928 | 0.05 | 500 | 0.2289 | 0.9340 | 0.9062 | 0.9660 | 0.9352 |
| 0.0833 | 0.1 | 1000 | 0.1120 | 0.9752 | 0.9637 | 0.9888 | 0.9761 |
| 0.0366 | 0.16 | 1500 | 0.1165 | 0.9753 | 0.9613 | 0.9915 | 0.9762 |
| 0.071 | 0.21 | 2000 | 0.0973 | 0.9709 | 0.9502 | 0.9940 | 0.9716 |
| 0.0465 | 0.26 | 2500 | 0.0680 | 0.9829 | 0.9979 | 0.9703 | 0.9839 |
| 0.0387 | 0.31 | 3000 | 0.1583 | 0.9705 | 0.9490 | 0.9945 | 0.9712 |
| 0.1061 | 0.37 | 3500 | 0.0685 | 0.9848 | 0.9802 | 0.9907 | 0.9854 |
| 0.0593 | 0.42 | 4000 | 0.0550 | 0.9872 | 0.9947 | 0.9813 | 0.9879 |
| 0.0382 | 0.47 | 4500 | 0.0551 | 0.9871 | 0.9912 | 0.9842 | 0.9877 |
| 0.0831 | 0.52 | 5000 | 0.0502 | 0.9840 | 0.9768 | 0.9927 | 0.9847 |
| 0.0376 | 0.58 | 5500 | 0.0654 | 0.9865 | 0.9852 | 0.9889 | 0.9871 |
| 0.0634 | 0.63 | 6000 | 0.0422 | 0.9877 | 0.9897 | 0.9870 | 0.9883 |
| 0.0235 | 0.68 | 6500 | 0.0553 | 0.9869 | 0.9846 | 0.9904 | 0.9875 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
|