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--- |
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license: cc-by-sa-4.0 |
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language: |
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- multilingual |
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- af |
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- am |
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- ar |
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- as |
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- az |
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- be |
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- bg |
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- bn |
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- br |
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- bs |
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- ca |
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- cs |
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- cy |
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- da |
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- de |
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- el |
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- en |
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- eo |
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- es |
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- et |
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- eu |
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- fa |
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- fi |
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- fr |
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- fy |
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- ga |
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- gd |
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- gl |
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- gu |
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- ha |
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- he |
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- hi |
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- hr |
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- hu |
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- hy |
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- id |
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- is |
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- it |
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- ja |
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- jv |
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- ka |
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- kk |
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- km |
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- kn |
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- ko |
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- ku |
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- ky |
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- la |
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- lo |
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- lt |
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- lv |
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- mg |
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- mk |
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- ml |
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- mn |
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- mr |
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- ms |
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- my |
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- ne |
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- nl |
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- 'no' |
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- om |
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- or |
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- pa |
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- pl |
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- ps |
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- pt |
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- ro |
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- ru |
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- sa |
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- sd |
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- si |
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- sk |
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- sl |
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- so |
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- sq |
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- sr |
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- su |
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- sv |
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- sw |
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- ta |
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- te |
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- th |
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- tl |
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- tr |
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- ug |
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- uk |
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- ur |
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- uz |
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- vi |
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- xh |
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- yi |
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- zh |
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tags: |
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- text-classification |
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- register |
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- web-register |
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- genre |
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--- |
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# Web register classification (multilingual model) |
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A multilingual web register classifier, fine-tuned from [XLM-RoBERTa-large](https://huggingface.co/FacebookAI/xlm-roberta-large). |
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The model is trained with the multilingual CORE corpora across five languages (English, Finnish, French, Swedish, Turkish) to classify documents based on the [CORE taxonomy](https://turkunlp.org/register-annotation-docs/). |
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It can predict labels for the 100 languages covered by XLM-RoBERTa-large. The model achieves state-of-the-art performance in classifying web registers for the trained languages and has strong transfer performance (see Evaluation below). |
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It is designed to support the development of open language models and for linguists analyzing register variation. |
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## Model Details |
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### Model Description |
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- **Developed by:** TurkuNLP |
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- **Funded by:** The Research Council of Finland, Eemil Aaltonen Foundation, University of Turku |
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- **Shared by:** TurkuNLP |
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- **Model type:** Language model |
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- **Language(s) (NLP):** English, Finnish, French, Swedish, Turkish |
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- **License:** apache-2.0 |
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- **Finetuned from model:** FacebookAI/xlm-roberta-large |
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### Model Sources |
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- **Repository:** https://github.com/TurkuNLP/pytorch-registerlabeling |
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- **Paper:** Coming soon! |
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## Register labels and their abbreviations |
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Below is a list of the register labels predicted by the model. Note that some labels are hierarchical; when a sublabel is predicted, its parent label is also predicted. |
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For a more detailed description of the label scheme, see [here](https://turkunlp.org/register-annotation-docs/). |
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The main labels are uppercase. To only include these main labels in the predictions, simply slice the model's output to keep only the uppercase labels. |
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- **MT:** Machine translated or generated |
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- **LY:** Lyrical |
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- **SP:** Spoken |
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- **it:** Interview |
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- **ID:** Interactive discussion |
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- **NA:** Narrative |
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- **ne:** News report |
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- **sr:** Sports report |
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- **nb:** Narrative blog |
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- **HI:** How-to or instructions |
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- **re:** Recipe |
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- **IN:** Informational description |
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- **en:** Encyclopedia article |
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- **ra:** Research article |
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- **dtp:** Description of a thing or person |
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- **fi:** Frequently asked questions |
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- **lt:** Legal terms and conditions |
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- **OP:** Opinion |
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- **rv:** Review |
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- **ob:** Opinion blog |
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- **rs:** Denominational religious blog or sermon |
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- **av:** Advice |
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- **IP:** Informational persuasion |
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- **ds:** Description with intent to sell |
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- **ed:** News & opinion blog or editorial |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import torch |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model_id = "TurkuNLP/multilingual-web-register-classification" |
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# Load model and tokenizer |
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model = AutoModelForSequenceClassification.from_pretrained(model_id).to(device) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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# Text to be categorized |
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text = "A text to be categorized" |
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# Tokenize text |
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inputs = tokenizer([text], return_tensors="pt", padding=True, truncation=True, max_length=512).to(device) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# Apply sigmoid to the logits to get probabilities |
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probabilities = torch.sigmoid(outputs.logits).squeeze() |
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# Determine a threshold for predicting labels |
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threshold = 0.5 |
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predicted_label_indices = (probabilities > threshold).nonzero(as_tuple=True)[0] |
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# Extract readable labels using id2label |
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id2label = model.config.id2label |
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predicted_labels = [id2label[idx.item()] for idx in predicted_label_indices] |
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print("Predicted labels:", predicted_labels) |
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``` |
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## Training Details |
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### Training Data |
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The model was trained using the Multilingual CORE Corpora, which will be published soon. |
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### Training Procedure |
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#### Training Hyperparameters |
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- **Batch size:** 8 |
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- **Epochs:** 21 |
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- **Learning rate:** 0.00005 |
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- **Precision:** bfloat16 (non-mixed precision) |
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- **TF32:** Enabled |
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- **Seed:** 42 |
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- **Max Size:** 512 tokens |
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#### Inference time |
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Average inference time (across 1000 iterations), using a single NVIDIA A100 GPU and a batch size of one is **17 ms** for a single example. Wirh bigger batches, inference can be considerably faster. |
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## Evaluation |
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Micro-averaged F1 scores and optimized prediction thresholds for the five training languages (test set): |
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| Language | F1 (All labels) | F1 (Main labels) | Threshold | |
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| -------- | --------------- | ---------------- | ----------| |
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| English | 0.72 | 0.75 | 0.40 | |
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| Finnish | 0.79 | 0.82 | 0.45 | |
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| French | 0.75 | 0.78 | 0.45 | |
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| Swedish | 0.81 | 0.82 | 0.45 | |
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| Turkish | 0.77 | 0.78 | 0.45 | |
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Micro-averaged F1 scores and optimized prediction thresholds for additional languages (zero-shot): |
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| Language | F1 (All labels) | F1 (Main labels) | Threshold | |
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| ---------- | --------------- | ---------------- | ----------| |
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| Arabic | 0.63 | 0.66 | 0.40 | |
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| Catalan | 0.62 | 0.63 | 0.50 | |
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| Spanish | 0.62 | 0.67 | 0.65 | |
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| Persian | 0.71 | 0.70 | 0.35 | |
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| Hindi | 0.77 | 0.78 | 0.40 | |
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| Indonesian | 0.60 | 0.61 | 0.30 | |
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| Japanese | 0.53 | 0.64 | 0.35 | |
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| Norwegian | 0.65 | 0.70 | 0.65 | |
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| Portuguese | 0.67 | 0.68 | 0.40 | |
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| Urdu | 0.81 | 0.83 | 0.35 | |
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| Chinese | 0.67 | 0.70 | 0.40 | |
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## Technical Specifications |
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### Compute Infrastructure |
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- Mahti supercomputer (CSC - IT Center for Science, Finland) |
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- 1 x NVIDIA A100-SXM4-40GB |
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#### Software |
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- torch 2.2.1 |
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- transformers 4.39.3 |
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## Citation |
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The citation for this work will be available soon. In the meantime, please refer to earlier related work for citation: |
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```bibtex |
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@article{Laippala.etal2022, |
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title = {Register Identification from the Unrestricted Open {{Web}} Using the {{Corpus}} of {{Online Registers}} of {{English}}}, |
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author = {Laippala, Veronika and R{\"o}nnqvist, Samuel and Oinonen, Miika and Kyr{\"o}l{\"a}inen, Aki-Juhani and Salmela, Anna and Biber, Douglas and Egbert, Jesse and Pyysalo, Sampo}, |
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year = {2022}, |
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journal = {Language Resources and Evaluation}, |
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issn = {1574-0218}, |
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doi = {10.1007/s10579-022-09624-1}, |
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url = {https://doi.org/10.1007/s10579-022-09624-1}, |
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} |
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@article{Skantsi_Laippala_2023, |
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title = {Analyzing the unrestricted web: The finnish corpus of online registers}, |
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doi = {10.1017/S0332586523000021}, |
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journal = {Nordic Journal of Linguistics}, |
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author = {Skantsi, Valtteri and Laippala, Veronika}, |
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year = {2023}, |
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pages = {1–31}} |
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``` |
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## Model Card Contact |
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Erik Henriksson, Hugging Face username: erikhenriksson |
