---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ntc-scv-distilbert-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. -->

# ntc-scv-distilbert-base-uncased

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3672
- Accuracy: 0.8458

## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 118  | 0.4574          | 0.7902   |
| No log        | 2.0   | 236  | 0.4322          | 0.8045   |
| No log        | 3.0   | 354  | 0.4200          | 0.8121   |
| No log        | 4.0   | 472  | 0.3952          | 0.8271   |
| 0.4318        | 5.0   | 590  | 0.3981          | 0.8312   |
| 0.4318        | 6.0   | 708  | 0.3887          | 0.8343   |
| 0.4318        | 7.0   | 826  | 0.4038          | 0.8316   |
| 0.4318        | 8.0   | 944  | 0.4085          | 0.8333   |
| 0.3168        | 9.0   | 1062 | 0.4098          | 0.835    |
| 0.3168        | 10.0  | 1180 | 0.4104          | 0.8362   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0