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---
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-base-DIALOCONAN-WIKI-CLS
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. -->
# deberta-base-DIALOCONAN-WIKI-CLS
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3866
- Precision: 0.6323
- Recall: 0.6344
- F1: 0.6333
- Accuracy: 0.9484
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3835 | 1.0 | 2500 | 0.4185 | 0.6829 | 0.6859 | 0.6832 | 0.9097 |
| 0.2718 | 2.0 | 5000 | 0.3822 | 0.7011 | 0.7016 | 0.7011 | 0.9329 |
| 0.1602 | 3.0 | 7500 | 0.3330 | 0.6302 | 0.6321 | 0.6311 | 0.9451 |
| 0.1018 | 4.0 | 10000 | 0.3639 | 0.6332 | 0.6351 | 0.6340 | 0.9496 |
| 0.0508 | 5.0 | 12500 | 0.3866 | 0.6323 | 0.6344 | 0.6333 | 0.9484 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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