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---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme_en
metrics:
- accuracy
- f1
widget:
- text: "My name is Julia, I study at Imperial College, in London"
example_title: "Example 1"
- text: "My name is Sarah and I live in Paris"
example_title: "Example 2"
- text: "My name is Clara and I live in Berkeley, California"
example_title: "Example 3"
model-index:
- name: XLM-RoBERTa-xtreme-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme_en
type: xtreme_en
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9109484079686702
- name: F1
type: f1
value: 0.7544312444026322
---
<!-- 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. -->
# XLM-RoBERTa-xtreme-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme_en dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2838
- Accuracy: 0.9109
- F1: 0.7544
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6502 | 1.0 | 235 | 0.3328 | 0.8995 | 0.7251 |
| 0.3239 | 2.0 | 470 | 0.2897 | 0.9101 | 0.7473 |
| 0.2644 | 3.0 | 705 | 0.2838 | 0.9109 | 0.7544 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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