---
library_name: peft
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- base_model:adapter:distilbert/distilbert-base-uncased
- lora
- transformers
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
  results: []
datasets:
- iam-tsr/employ_fdbk
language:
- en
---

<!-- 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. -->

# results

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on [employ feedback dataset](https://huggingface.co/datasets/iam-tsr/employ_fdbk).
It achieves the following results on the evaluation set:
- Loss: 0.1520
- Accuracy: 0.9423
- Precision: 0.9181
- Recall: 0.9284
- F1: 0.9228

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1735        | 1.0   | 172  | 0.1631          | 0.9462   | 0.9249    | 0.9297 | 0.9272 |
| 0.1824        | 2.0   | 344  | 0.1619          | 0.9385   | 0.9108    | 0.9308 | 0.9191 |
| 0.1555        | 3.0   | 516  | 0.1520          | 0.9423   | 0.9181    | 0.9284 | 0.9228 |


### Framework versions

- PEFT 0.18.0
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.3.0
- Tokenizers 0.22.2