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---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme_en_token_drift
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-token-drift
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme_en_token_drift
type: xtreme_en_token_drift
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.908855961405927
- name: F1
type: f1
value: 0.76126567683807
---
<!-- 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-token-drift
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme_en_token_drift dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2802
- Accuracy: 0.9089
- F1: 0.7613
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6398 | 1.0 | 161 | 0.3421 | 0.8973 | 0.7111 |
| 0.3268 | 2.0 | 322 | 0.2846 | 0.9097 | 0.7611 |
| 0.2701 | 3.0 | 483 | 0.2802 | 0.9089 | 0.7613 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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