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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
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. -->
# distilbert-base-uncased-lora-text-classification
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: 1.3834
- Precision: 0.8310
- Recall: 0.8708
- F1 and accuracy: {'accuracy': 0.7877237851662404, 'f1': 0.8504504504504504}
## 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: 4
- eval_batch_size: 4
- 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 | Precision | Recall | F1 and accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log | 1.0 | 391 | 0.5803 | 0.7346 | 0.9705 | {'accuracy': 0.7365728900255755, 'f1': 0.836248012718601} |
| 0.5606 | 2.0 | 782 | 0.5085 | 0.8259 | 0.8229 | {'accuracy': 0.7570332480818415, 'f1': 0.8243992606284658} |
| 0.4687 | 3.0 | 1173 | 0.6925 | 0.8007 | 0.8745 | {'accuracy': 0.7621483375959079, 'f1': 0.8359788359788359} |
| 0.3603 | 4.0 | 1564 | 0.8182 | 0.7955 | 0.9188 | {'accuracy': 0.7800511508951407, 'f1': 0.8527397260273973} |
| 0.3603 | 5.0 | 1955 | 0.8375 | 0.8413 | 0.8413 | {'accuracy': 0.7800511508951407, 'f1': 0.8413284132841329} |
| 0.2736 | 6.0 | 2346 | 1.0186 | 0.8235 | 0.8782 | {'accuracy': 0.7851662404092071, 'f1': 0.8500000000000001} |
| 0.1993 | 7.0 | 2737 | 1.1566 | 0.8224 | 0.9225 | {'accuracy': 0.8081841432225064, 'f1': 0.8695652173913043} |
| 0.1491 | 8.0 | 3128 | 1.2136 | 0.8502 | 0.8376 | {'accuracy': 0.7851662404092071, 'f1': 0.8438661710037174} |
| 0.1224 | 9.0 | 3519 | 1.3815 | 0.8231 | 0.8930 | {'accuracy': 0.7928388746803069, 'f1': 0.8566371681415929} |
| 0.1224 | 10.0 | 3910 | 1.3834 | 0.8310 | 0.8708 | {'accuracy': 0.7877237851662404, 'f1': 0.8504504504504504} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
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