|
--- |
|
license: apache-2.0 |
|
base_model: distilbert/distilbert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: distilbert_base_uncased_twitter |
|
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. --> |
|
|
|
# distilbert_base_uncased_twitter |
|
|
|
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4864 |
|
- Accuracy: 0.7665 |
|
- F1 Macro: 0.7130 |
|
- F1 Micro: 0.7665 |
|
|
|
## 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: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 2 |
|
- total_train_batch_size: 64 |
|
- total_eval_batch_size: 64 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 3.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:| |
|
| 0.4671 | 0.37 | 50 | 0.4990 | 0.7665 | 0.7214 | 0.7665 | |
|
| 0.4724 | 0.74 | 100 | 0.4864 | 0.7665 | 0.7130 | 0.7665 | |
|
| 0.4569 | 1.1 | 150 | 0.4924 | 0.7619 | 0.7171 | 0.7619 | |
|
| 0.4577 | 1.47 | 200 | 0.4881 | 0.7675 | 0.7357 | 0.7675 | |
|
| 0.4438 | 1.84 | 250 | 0.4902 | 0.7638 | 0.7222 | 0.7638 | |
|
| 0.405 | 2.21 | 300 | 0.4901 | 0.7647 | 0.7211 | 0.7647 | |
|
| 0.4308 | 2.57 | 350 | 0.4900 | 0.7693 | 0.7326 | 0.7693 | |
|
| 0.3584 | 2.94 | 400 | 0.4931 | 0.7675 | 0.7288 | 0.7675 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.0.dev0 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
