---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: slurp-slot_baseline-xlm_r-en
  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. -->

# slurp-slot_baseline-xlm_r-en

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: 0.3263
- Precision: 0.8145
- Recall: 0.8641
- F1: 0.8386
- Accuracy: 0.9341

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1437        | 1.0   | 720  | 0.5236          | 0.6852    | 0.6623 | 0.6736 | 0.8860   |
| 0.5761        | 2.0   | 1440 | 0.3668          | 0.7348    | 0.7829 | 0.7581 | 0.9119   |
| 0.3087        | 3.0   | 2160 | 0.2996          | 0.7925    | 0.8280 | 0.8099 | 0.9270   |
| 0.2631        | 4.0   | 2880 | 0.2959          | 0.7872    | 0.8487 | 0.8168 | 0.9275   |
| 0.1847        | 5.0   | 3600 | 0.3121          | 0.7929    | 0.8373 | 0.8145 | 0.9290   |
| 0.1518        | 6.0   | 4320 | 0.3117          | 0.8080    | 0.8601 | 0.8332 | 0.9329   |
| 0.1232        | 7.0   | 5040 | 0.3153          | 0.7961    | 0.8490 | 0.8217 | 0.9267   |
| 0.0994        | 8.0   | 5760 | 0.3125          | 0.8105    | 0.8570 | 0.8331 | 0.9332   |
| 0.0968        | 9.0   | 6480 | 0.3242          | 0.8147    | 0.8637 | 0.8385 | 0.9329   |
| 0.0772        | 10.0  | 7200 | 0.3263          | 0.8145    | 0.8641 | 0.8386 | 0.9341   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3