---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-uncased-finetuned-resume
  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-resume

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: 0.7572
- Accuracy: 0.6121
- Precision: 0.5993
- Recall: 0.5837
- F1: 0.5817

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.961         | 1.0   | 583  | 0.7683          | 0.6508   | 0.6194    | 0.5758 | 0.5509 |
| 0.7218        | 2.0   | 1166 | 0.7392          | 0.6424   | 0.6484    | 0.5936 | 0.5577 |
| 0.6682        | 3.0   | 1749 | 0.7518          | 0.6358   | 0.5780    | 0.6620 | 0.6089 |
| 0.6262        | 4.0   | 2332 | 0.7457          | 0.6199   | 0.5959    | 0.6417 | 0.5964 |
| 0.59          | 5.0   | 2915 | 0.7572          | 0.6121   | 0.5993    | 0.5837 | 0.5817 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1