|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: sercetexam9/afro-xlmr-base-vmw-MICRO |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: afro-xlmr-base-vmw-MICRO-finetuned-augmentation-LUNAR-TAPT-macro |
|
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. --> |
|
|
|
# afro-xlmr-base-vmw-MICRO-finetuned-augmentation-LUNAR-TAPT-macro |
|
|
|
This model is a fine-tuned version of [sercetexam9/afro-xlmr-base-vmw-MICRO](https://huggingface.co/sercetexam9/afro-xlmr-base-vmw-MICRO) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2140 |
|
- F1: 0.3055 |
|
- Roc Auc: 0.6225 |
|
- Accuracy: 0.6820 |
|
|
|
## 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: cosine |
|
- lr_scheduler_warmup_steps: 100 |
|
- num_epochs: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
|
| 0.0842 | 1.0 | 169 | 0.2140 | 0.3055 | 0.6225 | 0.6820 | |
|
| 0.0753 | 2.0 | 338 | 0.2155 | 0.2880 | 0.6016 | 0.7028 | |
|
| 0.0629 | 3.0 | 507 | 0.2321 | 0.2955 | 0.6138 | 0.6657 | |
|
| 0.0805 | 4.0 | 676 | 0.2392 | 0.2833 | 0.6075 | 0.6627 | |
|
| 0.0577 | 5.0 | 845 | 0.2320 | 0.2968 | 0.6155 | 0.6880 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.45.1 |
|
- Pytorch 2.4.0 |
|
- Datasets 3.0.1 |
|
- Tokenizers 0.20.0 |
|
|