|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: DEREXP |
|
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. --> |
|
|
|
# DEREXP |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 3.5797 |
|
- Mse: 3.5797 |
|
- Mae: 1.4414 |
|
- R2: 0.3526 |
|
- Accuracy: 0.2268 |
|
|
|
## 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: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 1 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:| |
|
| 14.19 | 0.08 | 500 | 4.6174 | 4.6174 | 1.6744 | 0.1649 | 0.198 | |
|
| 4.527 | 0.16 | 1000 | 3.9019 | 3.9019 | 1.5164 | 0.2943 | 0.2192 | |
|
| 4.3036 | 0.24 | 1500 | 5.3501 | 5.3501 | 1.8130 | 0.0324 | 0.1736 | |
|
| 4.0923 | 0.32 | 2000 | 3.8948 | 3.8948 | 1.5150 | 0.2956 | 0.2142 | |
|
| 4.0042 | 0.4 | 2500 | 3.7648 | 3.7648 | 1.4905 | 0.3191 | 0.2162 | |
|
| 3.8685 | 0.48 | 3000 | 3.7741 | 3.7741 | 1.4908 | 0.3174 | 0.2152 | |
|
| 3.8928 | 0.56 | 3500 | 3.7122 | 3.7122 | 1.4738 | 0.3286 | 0.214 | |
|
| 3.8193 | 0.64 | 4000 | 3.7020 | 3.7020 | 1.4727 | 0.3304 | 0.2182 | |
|
| 3.6929 | 0.72 | 4500 | 3.6419 | 3.6419 | 1.4575 | 0.3413 | 0.2266 | |
|
| 3.7974 | 0.8 | 5000 | 3.6995 | 3.6995 | 1.4656 | 0.3309 | 0.2202 | |
|
| 3.7752 | 0.88 | 5500 | 3.6344 | 3.6344 | 1.4559 | 0.3427 | 0.2276 | |
|
| 3.6254 | 0.96 | 6000 | 3.5797 | 3.5797 | 1.4414 | 0.3526 | 0.2268 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.12.0+cu113 |
|
- Datasets 2.3.2 |
|
- Tokenizers 0.12.1 |
|
|
