---
license: mit
tags:
- generated_from_trainer
base_model: xlm-roberta-base
model-index:
- name: job-listing-filtering-model
  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. -->

# job-listing-filtering-model

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1992

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4639        | 1.55  | 50   | 0.4343          |
| 0.407         | 3.12  | 100  | 0.3589          |
| 0.3459        | 4.68  | 150  | 0.3110          |
| 0.2871        | 6.25  | 200  | 0.2604          |
| 0.1966        | 7.8   | 250  | 0.2004          |
| 0.0994        | 9.37  | 300  | 0.1766          |
| 0.0961        | 10.92 | 350  | 0.2007          |
| 0.0954        | 12.49 | 400  | 0.1716          |
| 0.0498        | 14.06 | 450  | 0.1642          |
| 0.0419        | 15.62 | 500  | 0.1811          |
| 0.0232        | 17.18 | 550  | 0.1872          |
| 0.0146        | 18.74 | 600  | 0.1789          |
| 0.0356        | 20.31 | 650  | 0.1984          |
| 0.0325        | 21.86 | 700  | 0.1845          |
| 0.0381        | 23.43 | 750  | 0.1994          |
| 0.0063        | 24.98 | 800  | 0.1992          |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6