pytorch tensorboard xlm-roberta token-classification dataset:xtreme_en_token_drift transformers generated_from_trainer license:mit model-index autotrain_compatible
---
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
---

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# 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
