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---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: distilbert-base-uncased
datasets:
- pubmed-summarization
model-index:
- name: distilbert-base-uncased-finetuned-pubmed-lora-trained-tabbas97
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-pubmed-lora-trained-tabbas97
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the pubmed-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9949
## 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: 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2258 | 0.8 | 500 | 2.0382 |
| 2.1769 | 1.6 | 1000 | 1.9865 |
| 2.1476 | 2.4 | 1500 | 2.0094 |
| 2.146 | 3.2 | 2000 | 2.0005 |
| 2.1429 | 4.0 | 2500 | 1.9993 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |