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---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_base_uncased_amazon
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_amazon
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9130
- Accuracy: 0.7576
- F1 Macro: 0.6904
- F1 Micro: 0.7576
## 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: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 2.6322 | 0.26 | 50 | 2.5191 | 0.4750 | 0.3209 | 0.4750 |
| 1.9044 | 0.53 | 100 | 1.8323 | 0.6014 | 0.4626 | 0.6014 |
| 1.5127 | 0.79 | 150 | 1.4810 | 0.6574 | 0.5154 | 0.6574 |
| 1.2857 | 1.05 | 200 | 1.2679 | 0.6983 | 0.5795 | 0.6983 |
| 1.0669 | 1.32 | 250 | 1.1415 | 0.7306 | 0.6376 | 0.7306 |
| 1.0931 | 1.58 | 300 | 1.0669 | 0.7312 | 0.6333 | 0.7312 |
| 0.9879 | 1.84 | 350 | 1.0102 | 0.7437 | 0.6542 | 0.7437 |
| 0.8936 | 2.11 | 400 | 0.9650 | 0.7444 | 0.6640 | 0.7444 |
| 0.8345 | 2.37 | 450 | 0.9389 | 0.7582 | 0.6900 | 0.7582 |
| 0.7851 | 2.63 | 500 | 0.9208 | 0.7628 | 0.6924 | 0.7628 |
| 0.8439 | 2.89 | 550 | 0.9130 | 0.7576 | 0.6904 | 0.7576 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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