---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: pizza-ner2
  results: []
pipeline_tag: token-classification
widget:
- text: "Can I get a Veggie Supreme Pizza with Pineapple toppings?"
  example_title: "Pizza"
- text: "Can I get a cheese pizza with a coke?"
  example_title: "Drink"
- text: "Can I get a Margherita Pizza, three Chicken Pizza with ham and bacon and five bottles of coke?"
  example_title: "Complex Order"
- text: "What all non-veg pizza options you have ?"
  example_title: "Category Type"
---

<!-- 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. -->

# Pizza NER

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0172
- F1: 0.9879

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0382        | 1.0   | 10468 | 0.0172          | 0.9879 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1