pytorch tensorboard xlm-roberta token-classification dataset:xtreme transformers generated_from_trainer license:mit model-index autotrain_compatible
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: XLM-R-fine-tuned-for-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xtreme
      type: xtreme
      args: PAN-X.en
    metrics:
    - name: F1
      type: f1
      value: 0.8377982238973259
---

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# XLM-R-fine-tuned-for-ner

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5679
- F1: 0.8378

## 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: 8
- eval_batch_size: 8
- 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 | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4202        | 1.0   | 2500  | 0.3449          | 0.7963 |
| 0.2887        | 2.0   | 5000  | 0.2756          | 0.8057 |
| 0.2309        | 3.0   | 7500  | 0.2971          | 0.8040 |
| 0.1832        | 4.0   | 10000 | 0.3319          | 0.8167 |
| 0.1461        | 5.0   | 12500 | 0.3958          | 0.8350 |
| 0.114         | 6.0   | 15000 | 0.4087          | 0.8316 |
| 0.0833        | 7.0   | 17500 | 0.4320          | 0.8361 |
| 0.0614        | 8.0   | 20000 | 0.4885          | 0.8353 |
| 0.039         | 9.0   | 22500 | 0.5408          | 0.8390 |
| 0.0251        | 10.0  | 25000 | 0.5679          | 0.8378 |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
