papluca/xlm-roberta-base-language-detection
xlm-roberta-base-language-detection
This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset.
Model description
This model is an XLM-RoBERTa transformer model with a classification head on top (i.e. a linear layer on top of the pooled output).
For additional information please refer to the xlm-roberta-base model card or to the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al.
Intended uses & limitations
You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 20 languages:
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), and chinese (zh)
Training and evaluation data
The model was fine-tuned on the Language Identification dataset, which consists of text sequences in 20 languages. The training set contains 70k samples, while the validation and test sets 10k each. The average accuracy on the test set is 99.6% (this matches the average macro/weighted F1-score being the test set perfectly balanced). A more detailed evaluation is provided by the following table.
Language | Precision | Recall | F1-score | support |
---|---|---|---|---|
ar | 0.998 | 0.996 | 0.997 | 500 |
bg | 0.998 | 0.964 | 0.981 | 500 |
de | 0.998 | 0.996 | 0.997 | 500 |
el | 0.996 | 1.000 | 0.998 | 500 |
en | 1.000 | 1.000 | 1.000 | 500 |
es | 0.967 | 1.000 | 0.983 | 500 |
fr | 1.000 | 1.000 | 1.000 | 500 |
hi | 0.994 | 0.992 | 0.993 | 500 |
it | 1.000 | 0.992 | 0.996 | 500 |
ja | 0.996 | 0.996 | 0.996 | 500 |
nl | 1.000 | 1.000 | 1.000 | 500 |
pl | 1.000 | 1.000 | 1.000 | 500 |
pt | 0.988 | 1.000 | 0.994 | 500 |
ru | 1.000 | 0.994 | 0.997 | 500 |
sw | 1.000 | 1.000 | 1.000 | 500 |
th | 1.000 | 0.998 | 0.999 | 500 |
tr | 0.994 | 0.992 | 0.993 | 500 |
ur | 1.000 | 1.000 | 1.000 | 500 |
vi | 0.992 | 1.000 | 0.996 | 500 |
zh | 1.000 | 1.000 | 1.000 | 500 |
Benchmarks
As a baseline to compare xlm-roberta-base-language-detection
against, we have used the Python langid library. Since it comes pre-trained on 97 languages, we have used its .set_languages()
method to constrain the language set to our 20 languages. The average accuracy of langid on the test set is 98.5%. More details are provided by the table below.
Language | Precision | Recall | F1-score | support |
---|---|---|---|---|
ar | 0.990 | 0.970 | 0.980 | 500 |
bg | 0.998 | 0.964 | 0.981 | 500 |
de | 0.992 | 0.944 | 0.967 | 500 |
el | 1.000 | 0.998 | 0.999 | 500 |
en | 1.000 | 1.000 | 1.000 | 500 |
es | 1.000 | 0.968 | 0.984 | 500 |
fr | 0.996 | 1.000 | 0.998 | 500 |
hi | 0.949 | 0.976 | 0.963 | 500 |
it | 0.990 | 0.980 | 0.985 | 500 |
ja | 0.927 | 0.988 | 0.956 | 500 |
nl | 0.980 | 1.000 | 0.990 | 500 |
pl | 0.986 | 0.996 | 0.991 | 500 |
pt | 0.950 | 0.996 | 0.973 | 500 |
ru | 0.996 | 0.974 | 0.985 | 500 |
sw | 1.000 | 1.000 | 1.000 | 500 |
th | 1.000 | 0.996 | 0.998 | 500 |
tr | 0.990 | 0.968 | 0.979 | 500 |
ur | 0.998 | 0.996 | 0.997 | 500 |
vi | 0.971 | 0.990 | 0.980 | 500 |
zh | 1.000 | 1.000 | 1.000 | 500 |
Training procedure
Fine-tuning was done via the Trainer
API. Here is the Colab notebook with the training code.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
The validation results on the valid
split of the Language Identification dataset are summarised here below.
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
0.2492 | 1.0 | 1094 | 0.0149 | 0.9969 | 0.9969 |
0.0101 | 2.0 | 2188 | 0.0103 | 0.9977 | 0.9977 |
In short, it achieves the following results on the validation set:
- Loss: 0.0101
- Accuracy: 0.9977
- F1: 0.9977
Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
数据统计
数据评估
本站Ai导航提供的papluca/xlm-roberta-base-language-detection都来源于网络,不保证外部链接的准确性和完整性,同时,对于该外部链接的指向,不由Ai导航实际控制,在2023年5月15日 下午3:15收录时,该网页上的内容,都属于合规合法,后期网页的内容如出现违规,可以直接联系网站管理员进行删除,Ai导航不承担任何责任。