metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - banking77
metrics:
  - accuracy
model-index:
  - name: xlm-roberta-base-banking77-classification
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: banking77
          type: banking77
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9321428571428572
        widget:
          - text: 'Can I track the card you sent to me? '
            example_title: Card Arrival Example - English
          - text: 'Posso tracciare la carta che mi avete spedito? '
            example_title: Card Arrival Example - Italian
          - text: Can you explain your exchange rate policy to me?
            example_title: Exchange Rate Example - English
          - text: Potete spiegarmi la vostra politica dei tassi di cambio?
            example_title: Exchange Rate Example - Italian
          - text: I can't pay by my credit card
            example_title: Card Not Working Example - English
          - text: Non posso pagare con la mia carta di credito
            example_title: Card Not Working Example - Italian

xlm-roberta-base-banking77-classification

This model is a fine-tuned version of xlm-roberta-base on the banking77 dataset. It achieves the following results on the evaluation set:

Loss: 0.3034
Accuracy: 0.9321
F1 Score: 0.9321

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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score
3.8002	1.0	157	2.7771	0.5159	0.4483
2.4006	2.0	314	1.6937	0.7140	0.6720
1.4633	3.0	471	1.0385	0.8308	0.8153
0.9234	4.0	628	0.7008	0.8789	0.8761
0.6163	5.0	785	0.5029	0.9068	0.9063
0.4282	6.0	942	0.4084	0.9123	0.9125
0.3203	7.0	1099	0.3515	0.9253	0.9253
0.245	8.0	1256	0.3295	0.9227	0.9225
0.1863	9.0	1413	0.3092	0.9269	0.9269
0.1518	10.0	1570	0.2901	0.9338	0.9338
0.1179	11.0	1727	0.2938	0.9318	0.9319
0.0969	12.0	1884	0.2906	0.9328	0.9328
0.0805	13.0	2041	0.2963	0.9295	0.9295
0.063	14.0	2198	0.2998	0.9289	0.9288
0.0554	15.0	2355	0.2933	0.9351	0.9349
0.046	16.0	2512	0.2960	0.9328	0.9326
0.04	17.0	2669	0.3032	0.9318	0.9318
0.035	18.0	2826	0.3061	0.9312	0.9312
0.0317	19.0	2983	0.3030	0.9331	0.9330
0.0315	20.0	3140	0.3034	0.9321	0.9321

Framework versions

Transformers 4.21.1
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1