|
---
|
|
license: apache-2.0
|
|
base_model: distilbert/distilbert-base-multilingual-cased
|
|
tags:
|
|
- generated_from_trainer
|
|
metrics:
|
|
- precision
|
|
- recall
|
|
- f1
|
|
- accuracy
|
|
model-index:
|
|
- name: NEW_trained_slovak
|
|
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. -->
|
|
|
|
# NEW_trained_slovak
|
|
|
|
This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the None dataset.
|
|
It achieves the following results on the evaluation set:
|
|
- Loss: 0.1299
|
|
- Precision: 0.6938
|
|
- Recall: 0.7634
|
|
- F1: 0.7269
|
|
- Accuracy: 0.9709
|
|
|
|
## 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: 3e-05
|
|
- train_batch_size: 8
|
|
- eval_batch_size: 8
|
|
- seed: 42
|
|
- gradient_accumulation_steps: 2
|
|
- total_train_batch_size: 16
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
|
- lr_scheduler_type: linear
|
|
- num_epochs: 4
|
|
|
|
### Training results
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
|
| 0.0808 | 1.0 | 530 | 0.1178 | 0.6792 | 0.75 | 0.7128 | 0.9686 |
|
|
| 0.0249 | 2.0 | 1061 | 0.1144 | 0.6708 | 0.7654 | 0.7150 | 0.9687 |
|
|
| 0.0122 | 3.0 | 1591 | 0.1206 | 0.6905 | 0.7741 | 0.7299 | 0.9708 |
|
|
| 0.0058 | 4.0 | 2120 | 0.1299 | 0.6938 | 0.7634 | 0.7269 | 0.9709 |
|
|
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.38.2
|
|
- Pytorch 2.1.2+cu118
|
|
- Datasets 2.18.0
|
|
- Tokenizers 0.15.2
|
|
|