|
--- |
|
license: apache-2.0 |
|
base_model: google/electra-base-discriminator |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- precision |
|
- recall |
|
model-index: |
|
- name: electra-base-discriminator_bert-base-uncased |
|
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. --> |
|
|
|
# electra-base-discriminator_bert-base-uncased |
|
|
|
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5398 |
|
- Accuracy: 0.8705 |
|
- F1: 0.8691 |
|
- Precision: 0.8729 |
|
- Recall: 0.8705 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 64 |
|
- eval_batch_size: 64 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 1000 |
|
- num_epochs: 25 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
|
| 0.9217 | 1.0 | 91 | 0.8648 | 0.6459 | 0.5998 | 0.6333 | 0.6459 | |
|
| 0.5726 | 2.0 | 182 | 0.5369 | 0.8066 | 0.8064 | 0.8238 | 0.8066 | |
|
| 0.3522 | 3.0 | 273 | 0.4095 | 0.8440 | 0.8415 | 0.8477 | 0.8440 | |
|
| 0.2589 | 4.0 | 364 | 0.5367 | 0.8097 | 0.8069 | 0.8258 | 0.8097 | |
|
| 0.2718 | 5.0 | 455 | 0.4216 | 0.8612 | 0.8621 | 0.8670 | 0.8612 | |
|
| 0.164 | 6.0 | 546 | 0.5346 | 0.8612 | 0.8602 | 0.8616 | 0.8612 | |
|
| 0.1075 | 7.0 | 637 | 0.5398 | 0.8705 | 0.8691 | 0.8729 | 0.8705 | |
|
| 0.1461 | 8.0 | 728 | 0.6163 | 0.8362 | 0.8368 | 0.8442 | 0.8362 | |
|
| 0.132 | 9.0 | 819 | 0.4933 | 0.8674 | 0.8675 | 0.8701 | 0.8674 | |
|
| 0.1359 | 10.0 | 910 | 0.7141 | 0.8424 | 0.8416 | 0.8489 | 0.8424 | |
|
| 0.0971 | 11.0 | 1001 | 0.5662 | 0.8596 | 0.8578 | 0.8623 | 0.8596 | |
|
| 0.1148 | 12.0 | 1092 | 0.5685 | 0.8612 | 0.8609 | 0.8610 | 0.8612 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.0 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.1.0 |
|
- Tokenizers 0.15.1 |
|
|
