---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-NER
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xtreme
      type: xtreme
      config: PAN-X.en
      split: validation
      args: PAN-X.en
    metrics:
    - name: Precision
      type: precision
      value: 0.8003614625330182
    - name: Recall
      type: recall
      value: 0.8110735418427726
    - name: F1
      type: f1
      value: 0.8056818976978517
    - name: Accuracy
      type: accuracy
      value: 0.9194332683336213
---

<!-- 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. -->

# roberta-base-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: 0.2935
- Precision: 0.8004
- Recall: 0.8111
- F1: 0.8057
- Accuracy: 0.9194

## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 417  | 0.3359          | 0.7286    | 0.7675 | 0.7476 | 0.8991   |
| 0.4227        | 2.0   | 834  | 0.2951          | 0.7711    | 0.7980 | 0.7843 | 0.9131   |
| 0.2818        | 3.0   | 1251 | 0.2824          | 0.7852    | 0.8076 | 0.7962 | 0.9174   |
| 0.2186        | 4.0   | 1668 | 0.2853          | 0.7934    | 0.8150 | 0.8041 | 0.9193   |
| 0.1801        | 5.0   | 2085 | 0.2935          | 0.8004    | 0.8111 | 0.8057 | 0.9194   |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3