metadata

license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: fine_tuned_XLMROBERTA_cs_wikann
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wikiann
          type: wikiann
          config: default
          split: validation
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.920336
          - name: Recall
            type: recall
            value: 0.934218
          - name: F1
            type: f1
            value: 0.927225
          - name: Accuracy
            type: accuracy
            value: 0.973202

fine_tuned_XLMROBERTA_cs_wikann

This model is a fine-tuned version of FacebookAI/xlm-roberta-large on a czech wikiann dataset. It achieves the following results on the evaluation set:

Loss: 0.1543
Precision: 0.9203
Recall: 0.9342
F1: 0.9272
Accuracy: 0.9732

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: 16
eval_batch_size: 16
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
0.51	0.27	500	0.1995	0.7873	0.8274	0.8069	0.9435
0.2164	0.53	1000	0.2216	0.7743	0.8430	0.8072	0.9407
0.1963	0.8	1500	0.1673	0.8465	0.8849	0.8653	0.9534
0.1478	1.07	2000	0.1612	0.8850	0.9	0.8925	0.9629
0.1316	1.33	2500	0.1508	0.8765	0.9081	0.8920	0.9615
0.1156	1.6	3000	0.1561	0.9028	0.9081	0.9054	0.9656
0.1069	1.87	3500	0.1544	0.9009	0.9091	0.9050	0.9651
0.0925	2.13	4000	0.1724	0.9008	0.9216	0.9111	0.9662
0.0791	2.4	4500	0.1385	0.9096	0.9201	0.9148	0.9705
0.0739	2.67	5000	0.1309	0.9130	0.9254	0.9192	0.9701
0.0732	2.93	5500	0.1593	0.9035	0.9190	0.9112	0.9679
0.0538	3.2	6000	0.1550	0.9193	0.9309	0.9251	0.9722
0.0529	3.47	6500	0.1451	0.9112	0.9330	0.9220	0.9710
0.0521	3.73	7000	0.1510	0.9185	0.9323	0.9253	0.9721
0.0526	4.0	7500	0.1378	0.9173	0.9325	0.9249	0.9727
0.0377	4.27	8000	0.1501	0.9164	0.9344	0.9253	0.9728
0.0382	4.53	8500	0.1541	0.9213	0.9352	0.9282	0.9729
0.0358	4.8	9000	0.1543	0.9203	0.9342	0.9272	0.9732

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0