File size: 7,184 Bytes
6595fed 28e7eae 6595fed 8442e3d 6595fed 8884cad 6595fed 44a4fd0 6595fed f793746 6595fed 714fcf8 6595fed f793746 6595fed 9865598 6595fed a5d0aa7 f793746 6595fed f793746 6595fed f793746 6595fed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
---
language:
- multilingual
- ar
- bg
- de
- el
- en
- es
- fr
- hi
- it
- ja
- nl
- pl
- pt
- ru
- sw
- th
- tr
- ur
- vi
- zh
license: mit
tags:
- generated_from_trainer
datasets: papluca/language-identification
metrics:
- accuracy
- f1
base_model: xlm-roberta-base
model-index:
- name: xlm-roberta-base-language-detection
results: []
---
# xlm-roberta-base-language-detection
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Language Identification](https://huggingface.co/datasets/papluca/language-identification#additional-information) 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](https://huggingface.co/xlm-roberta-base) model card or to the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) 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](https://huggingface.co/datasets/papluca/language-identification#additional-information) 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](https://github.com/saffsd/langid.py) 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 |
## How to get started with the model
The easiest way to use the model is via the high-level `pipeline` API:
```python
from transformers import pipeline
text = [
"Brevity is the soul of wit.",
"Amor, ch'a nullo amato amar perdona."
]
model_ckpt = "papluca/xlm-roberta-base-language-detection"
pipe = pipeline("text-classification", model=model_ckpt)
pipe(text, top_k=1, truncation=True)
```
Or one can proceed with the tokenizer and model separately:
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
text = [
"Brevity is the soul of wit.",
"Amor, ch'a nullo amato amar perdona."
]
model_ckpt = "papluca/xlm-roberta-base-language-detection"
tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
model = AutoModelForSequenceClassification.from_pretrained(model_ckpt)
inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
preds = torch.softmax(logits, dim=-1)
# Map raw predictions to languages
id2lang = model.config.id2label
vals, idxs = torch.max(preds, dim=1)
{id2lang[k.item()]: v.item() for k, v in zip(idxs, vals)}
```
## Training procedure
Fine-tuning was done via the `Trainer` API. Here is the [Colab notebook](https://colab.research.google.com/drive/15LJTckS6gU3RQOmjLqxVNBmbsBdnUEvl?usp=sharing) 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
|