---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: finetuned-DSCS24-mitre-distilbert-base-uncased-fill-mask
  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. -->

# finetuned-DSCS24-mitre-distilbert-base-uncased-fill-mask

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0279

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7512        | 1.0   | 65   | 2.4057          |
| 2.3941        | 2.0   | 130  | 2.2818          |
| 2.2615        | 3.0   | 195  | 2.2630          |
| 2.2113        | 4.0   | 260  | 2.1359          |
| 2.1435        | 5.0   | 325  | 2.1022          |
| 2.0719        | 6.0   | 390  | 2.0463          |
| 2.0483        | 7.0   | 455  | 2.0830          |
| 2.0175        | 8.0   | 520  | 1.9946          |
| 1.9778        | 9.0   | 585  | 2.0038          |
| 1.9831        | 10.0  | 650  | 1.9502          |
| 1.8909        | 11.0  | 715  | 1.9914          |
| 1.9602        | 12.0  | 780  | 2.0588          |
| 1.9309        | 13.0  | 845  | 2.0038          |
| 1.9112        | 14.0  | 910  | 1.9957          |
| 1.9197        | 15.0  | 975  | 2.0338          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1