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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-lora-text-classification
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/johanneseboigbe55-octave-analytics/huggingface/runs/ypukib7l)
# distilbert-base-uncased-lora-text-classification
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: 0.8006
- Accuracy: {'accuracy': 0.893}
## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log | 1.0 | 125 | 0.2598 | {'accuracy': 0.896} |
| No log | 2.0 | 250 | 0.3580 | {'accuracy': 0.888} |
| No log | 3.0 | 375 | 0.4035 | {'accuracy': 0.885} |
| 0.2622 | 4.0 | 500 | 0.5133 | {'accuracy': 0.881} |
| 0.2622 | 5.0 | 625 | 0.6146 | {'accuracy': 0.886} |
| 0.2622 | 6.0 | 750 | 0.7576 | {'accuracy': 0.885} |
| 0.2622 | 7.0 | 875 | 0.7499 | {'accuracy': 0.885} |
| 0.045 | 8.0 | 1000 | 0.8082 | {'accuracy': 0.891} |
| 0.045 | 9.0 | 1125 | 0.8045 | {'accuracy': 0.89} |
| 0.045 | 10.0 | 1250 | 0.8006 | {'accuracy': 0.893} |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
|