metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - f1
model-index:
  - name: xlm-roberta-base-finetuned-panx-all
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: wikiann
          type: wikiann
          config: en
          split: test
        metrics:
          - type: accuracy
            value: 0.843189280620875
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.8410061269097046
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.8568527450211155
            name: Recall
            verified: true
            verifyToken: >-
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          - type: f1
            value: 0.8488554853827908
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.6632214784622192
            name: loss
            verified: true
            verifyToken: >-
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xlm-roberta-base-finetuned-panx-all

This model is a fine-tuned version of xlm-roberta-base on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the NLP with Transformers book. You can find the full code in the accompanying Github repository.

It achieves the following results on the evaluation set:

Loss: 0.1739
F1: 0.8581

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: 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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.2912	1.0	835	0.1883	0.8238
0.1548	2.0	1670	0.1738	0.8480
0.101	3.0	2505	0.1739	0.8581

Framework versions

Transformers 4.12.0.dev0
Pytorch 1.9.1+cu102
Datasets 1.12.1
Tokenizers 0.10.3