---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-pulse
  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. -->

# distilhubert-finetuned-pulse

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6143
- Accuracy: 0.7143

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6972        | 1.0   | 31   | 0.6880          | 0.7143   |
| 0.703         | 2.0   | 62   | 0.6044          | 0.7143   |
| 0.6737        | 3.0   | 93   | 0.6217          | 0.7143   |
| 0.6756        | 4.0   | 124  | 0.6400          | 0.7143   |
| 0.6557        | 5.0   | 155  | 0.6213          | 0.7143   |
| 0.6778        | 6.0   | 186  | 0.6109          | 0.7143   |
| 0.6884        | 7.0   | 217  | 0.6415          | 0.7143   |
| 0.6364        | 8.0   | 248  | 0.6205          | 0.7143   |
| 0.6506        | 9.0   | 279  | 0.6171          | 0.7143   |
| 0.675         | 10.0  | 310  | 0.6139          | 0.7143   |
| 0.7018        | 11.0  | 341  | 0.6145          | 0.7143   |
| 0.6766        | 12.0  | 372  | 0.6099          | 0.7143   |
| 0.6493        | 13.0  | 403  | 0.6131          | 0.7143   |
| 0.6482        | 14.0  | 434  | 0.6138          | 0.7143   |
| 0.8036        | 15.0  | 465  | 0.6143          | 0.7143   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0