canine c

Introduction

CANINE-C is a pretrained model designed for multilingual language processing without needing explicit tokenization. It was introduced in the paper "CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation." Unlike other models like BERT or RoBERTa, CANINE-C operates at a character level using Unicode code points for input processing.

Architecture

CANINE-C is a transformer-based model that directly processes input at the character level, eliminating the need for tokenization stages like WordPiece or SentencePiece. This model leverages a self-supervised learning approach similar to BERT, using masked language modeling and next sentence prediction for training.

Training

The model was pretrained on a large corpus of multilingual data, specifically from multilingual Wikipedia, covering 104 languages. The training involved masked language modeling with an autoregressive character loss and next sentence prediction to build robust language representations.

Guide: Running Locally

1. Install the Transformers library:

   pip install transformers
   

2. Load the model and tokenizer:

   from transformers import CanineTokenizer, CanineModel
   
   model = CanineModel.from_pretrained('google/canine-c')
   tokenizer = CanineTokenizer.from_pretrained('google/canine-c')
   

3. Prepare inputs:

   inputs = ["Life is like a box of chocolates.", "You never know what you gonna get."]
   encoding = tokenizer(inputs, padding="longest", truncation=True, return_tensors="pt")
   

4. Perform a forward pass:

   outputs = model(**encoding)
   pooled_output = outputs.pooler_output
   sequence_output = outputs.last_hidden_state
   

5. Cloud GPU suggestion: For large-scale tasks or fine-tuning, consider using cloud services like AWS EC2 with GPU instances, Google Cloud Platform, or Azure for efficient processing.

License

The CANINE-C model is released under the Apache-2.0 License.