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--- |
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tags: |
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- pytorch_model_hub_mixin |
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- model_hub_mixin |
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license: apache-2.0 |
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--- |
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# nvidia/domain-classifier |
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# Model Overview |
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This is a text classification model to classify documents into one of 26 domain classes: |
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'Adult', 'Arts_and_Entertainment', 'Autos_and_Vehicles', 'Beauty_and_Fitness', 'Books_and_Literature', 'Business_and_Industrial', 'Computers_and_Electronics', 'Finance', 'Food_and_Drink', 'Games', 'Health', 'Hobbies_and_Leisure', 'Home_and_Garden', 'Internet_and_Telecom', 'Jobs_and_Education', 'Law_and_Government', 'News', 'Online_Communities', 'People_and_Society', 'Pets_and_Animals', 'Real_Estate', 'Science', 'Sensitive_Subjects', 'Shopping', 'Sports', 'Travel_and_Transportation' |
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# Model Architecture |
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The model architecture is Deberta V3 Base |
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Context length is 512 tokens |
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# Training (details) |
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## Training data: |
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- 1 million Common Crawl samples, labeled using Google Cloud’s Natural Language API: https://cloud.google.com/natural-language/docs/classifying-text |
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- 500k Wikepedia articles, curated using Wikipedia-API: https://pypi.org/project/Wikipedia-API/ |
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## Training steps: |
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Model was trained in multiple rounds using Wikipedia and Common Crawl data, labeled by a combination of pseudo labels and Google Cloud API. |
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# How To Use This Model |
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## Input |
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The model takes one or several paragraphs of text as input. |
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Example input: |
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``` |
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q Directions |
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1. Mix 2 flours and baking powder together |
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2. Mix water and egg in a separate bowl. Add dry to wet little by little |
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3. Heat frying pan on medium |
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4. Pour batter into pan and then put blueberries on top before flipping |
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5. Top with desired toppings! |
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``` |
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## Output |
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The model outputs one of the 26 domain classes as the predicted domain for each input sample. |
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Example output: |
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``` |
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Food_and_Drink |
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``` |
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# How to use in NeMo Curator |
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The inference code is available on NeMo Curator's GitHub repository. Download the [model.pth](https://huggingface.co/nvidia/domain-classifier/blob/main/model.pth) file and check out this [example notebook](https://github.com/NVIDIA/NeMo-Curator/blob/main/tutorials/distributed_data_classification/distributed_data_classification.ipynb) to get started. |
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# How to use in transformers |
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To use the Domain classifier, use the following code: |
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```python |
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import torch |
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from torch import nn |
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from transformers import AutoModel, AutoTokenizer, AutoConfig |
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from huggingface_hub import PyTorchModelHubMixin |
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class CustomModel(nn.Module, PyTorchModelHubMixin): |
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def __init__(self, config): |
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super(CustomModel, self).__init__() |
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self.model = AutoModel.from_pretrained(config['base_model']) |
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self.dropout = nn.Dropout(config['fc_dropout']) |
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self.fc = nn.Linear(self.model.config.hidden_size, len(config['id2label'])) |
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def forward(self, input_ids, attention_mask): |
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features = self.model(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state |
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dropped = self.dropout(features) |
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outputs = self.fc(dropped) |
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return torch.softmax(outputs[:, 0, :], dim=1) |
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# Setup configuration and model |
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config = AutoConfig.from_pretrained("nvidia/domain-classifier") |
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tokenizer = AutoTokenizer.from_pretrained("nvidia/domain-classifier") |
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model = CustomModel.from_pretrained("nvidia/domain-classifier") |
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# Prepare and process inputs |
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text_samples = ["Sports is a popular domain", "Politics is a popular domain"] |
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inputs = tokenizer(text_samples, return_tensors="pt", padding="longest", truncation=True) |
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outputs = model(inputs['input_ids'], inputs['attention_mask']) |
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# Predict and display results |
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predicted_classes = torch.argmax(outputs, dim=1) |
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predicted_domains = [config.id2label[class_idx.item()] for class_idx in predicted_classes.cpu().numpy()] |
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print(predicted_domains) |
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# ['Sports', 'News'] |
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``` |
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# Evaluation Benchmarks |
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Evaluation Metric: PR-AUC |
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PR-AUC score on evaluation set with 105k samples - 0.9873 |
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PR-AUC score for each domain: |
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| Domain | PR-AUC | |
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|--------------------------|--------| |
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| Adult | 0.999 | |
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| Arts_and_Entertainment | 0.997 | |
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| Autos_and_Vehicles | 0.997 | |
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| Beauty_and_Fitness | 0.997 | |
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| Books_and_Literature | 0.995 | |
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| Business_and_Industrial | 0.982 | |
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| Computers_and_Electronics| 0.992 | |
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| Finance | 0.989 | |
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| Food_and_Drink | 0.998 | |
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| Games | 0.997 | |
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| Health | 0.997 | |
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| Hobbies_and_Leisure | 0.984 | |
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| Home_and_Garden | 0.997 | |
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| Internet_and_Telecom | 0.982 | |
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| Jobs_and_Education | 0.993 | |
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| Law_and_Government | 0.967 | |
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| News | 0.918 | |
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| Online_Communities | 0.983 | |
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| People_and_Society | 0.975 | |
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| Pets_and_Animals | 0.997 | |
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| Real_Estate | 0.997 | |
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| Science | 0.988 | |
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| Sensitive_Subjects | 0.982 | |
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| Shopping | 0.995 | |
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| Sports | 0.995 | |
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| Travel_and_Transportation| 0.996 | |
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| Mean | 0.9873 | |
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# References |
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https://arxiv.org/abs/2111.09543 |
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https://github.com/microsoft/DeBERTa |
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# License |
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License to use this model is covered by the Apache 2.0. By downloading the public and release version of the model, you accept the terms and conditions of the Apache License 2.0. |
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This repository contains the code for the domain classifier model. |
