xlm-roberta-base-finetuned-language-detection-new
This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0436
- Accuracy: 0.9959
Model description
The model used in this task is XLM-RoBERTa, a transformer model with a classification head on top.
Intended uses & limitations
It identifies the language a document is written in and it supports 20 different langauges:
Arabic (ar), Bulgarian (bg), German (de), Modern greek (el), English (en), Spanish (es), French (fr), Hindi (hi), Italian (it), Japanese (ja), Dutch (nl), Polish (pl), Portuguese (pt), Russian (ru), Swahili (sw), Thai (th), Turkish (tr), Urdu (ur), Vietnamese (vi), Chinese (zh)
Training and evaluation data
The model is fine-tuned on the Language Identification dataset, a corpus consists of text from 20 different languages. The dataset is split with 7000 sentences for training, 1000 for validating, and 1000 for testing. The accuracy on the test set is 99.5%.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0493 | 1.0 | 35000 | 0.0407 | 0.9955 |
| 0.018 | 2.0 | 70000 | 0.0436 | 0.9959 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1