DeepSite - 🧬 RemixEmpowering low-resource languages through advanced NLP techniques. Our transfer learning models bring high-quality language processing to underserved communities.
# Initialize base model (mBERT)
base_model = AutoModel.from_pretrained('bert-base-multilingual-cased')
# Add task-specific layers
class PolyglotModel(nn.Module):
def __init__(self, base_model, num_languages):
super().__init__()
self.base = base_model
self.classifier = nn.Linear(768, num_languages)
def forward(self, input_ids, attention_mask):
outputs = self.base(input_ids, attention_mask)
pooled = outputs.last_hidden_state[:, 0, :]
return self.classifier(pooled)
# Train with low-resource data
train_model(low_resource_data, epochs=5, lr=2e-5)
We currently support these languages with limited digital resources, helping bridge the digital divide.
~30M speakers
~25M speakers
~16M speakers
~33M speakers
~35M speakers
~30M speakers
Leveraging state-of-the-art techniques to bring NLP to languages with limited digital resources.
We use pretrained multilingual models like mBERT and XLM-R as our foundation, then fine-tune them for specific low-resource languages.
Our progressive unfreezing technique carefully adapts the model layers to preserve valuable multilingual knowledge while specializing for target languages.
We add lightweight task-specific layers on top of the multilingual base, enabling efficient adaptation for various NLP tasks with minimal data.
Experience how transfer learning enables NLP for low-resource languages.
Processing your text with our transfer learning model...
Enter some text and click "Process Text" to see our transfer learning model in action.
This demo simulates how our transfer learning approach works with low-resource languages. While we can't run actual models in the browser, this shows the interface and expected behavior of our system.
Over 3,000 languages are spoken worldwide, yet most NLP research focuses on just a handful of high-resource languages. We're changing that by making NLP accessible to all languages, regardless of available digital resources.
Our transfer learning techniques allow us to achieve state-of-the-art results with as little as 1% of the data typically required for training NLP models from scratch.
Achieve 90% of high-resource language performance with just thousands (not millions) of training examples.
Knowledge from related languages boosts performance on truly low-resource languages.
Our transfer learning approach outperforms traditional methods for low-resource languages:
Average performance across 5 low-resource languages on NER task
Whether you're a researcher, developer, or language community representative, we'd love to hear from you.
contact@polyglotnlp.org
github.com/polyglot-nlp
Read our latest findings