---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased-finetuned-sst-2-english
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english_08112024T110433
  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. -->

# distilbert-base-uncased-finetuned-sst-2-english_08112024T110433

This model is a fine-tuned version of [distilbert/distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4561
- F1: 0.8591
- Learning Rate: 0.0

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 600
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | F1     | Rate   |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| No log        | 0.9942  | 86   | 1.8025          | 0.1776 | 0.0000 |
| No log        | 2.0     | 173  | 1.7296          | 0.1902 | 0.0000 |
| No log        | 2.9942  | 259  | 1.6400          | 0.3649 | 0.0000 |
| No log        | 4.0     | 346  | 1.4873          | 0.4157 | 0.0000 |
| No log        | 4.9942  | 432  | 1.2812          | 0.5119 | 0.0000 |
| 1.5846        | 6.0     | 519  | 1.0792          | 0.5668 | 0.0000 |
| 1.5846        | 6.9942  | 605  | 0.9450          | 0.6226 | 1e-05  |
| 1.5846        | 8.0     | 692  | 0.8238          | 0.6836 | 0.0000 |
| 1.5846        | 8.9942  | 778  | 0.7274          | 0.7374 | 0.0000 |
| 1.5846        | 10.0    | 865  | 0.6411          | 0.7734 | 0.0000 |
| 1.5846        | 10.9942 | 951  | 0.6020          | 0.7822 | 0.0000 |
| 0.7489        | 12.0    | 1038 | 0.5470          | 0.8157 | 0.0000 |
| 0.7489        | 12.9942 | 1124 | 0.5161          | 0.8299 | 0.0000 |
| 0.7489        | 14.0    | 1211 | 0.4952          | 0.8454 | 0.0000 |
| 0.7489        | 14.9942 | 1297 | 0.4768          | 0.8518 | 0.0000 |
| 0.7489        | 16.0    | 1384 | 0.4664          | 0.8546 | 0.0000 |
| 0.7489        | 16.9942 | 1470 | 0.4665          | 0.8565 | 0.0000 |
| 0.3329        | 18.0    | 1557 | 0.4561          | 0.8591 | 5e-07  |
| 0.3329        | 18.9942 | 1643 | 0.4578          | 0.8585 | 1e-07  |
| 0.3329        | 19.8844 | 1720 | 0.4579          | 0.8584 | 0.0    |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.19.1