canine-s-finetuned-sst2
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch 1.10.0 |
| Best Accuracy | 85.78% |
| Training Dataset | GLUE SST2 |
What is canine-s-finetuned-sst2?
This is a fine-tuned version of Google's CANINE-S model specifically optimized for sentiment analysis using the SST2 (Stanford Sentiment Treebank) dataset. The model demonstrates robust performance with an accuracy of 85.78% on the evaluation set, making it particularly effective for text classification tasks.
Implementation Details
The model was trained using the Adam optimizer with carefully tuned hyperparameters (betas=0.9,0.999, epsilon=1e-08) and implements a linear learning rate scheduler. Training was conducted over 5 epochs with a learning rate of 2e-05 and batch sizes of 16 for both training and evaluation.
- Training conducted over 21,050 steps
- Achieved optimal validation loss of 0.5259
- Implemented using Transformers 4.17.0 and Tokenizers 0.11.6
Core Capabilities
- Binary sentiment classification
- Efficient text processing using CANINE architecture
- Consistent performance across evaluation metrics
- Production-ready with TensorBoard support
Frequently Asked Questions
Q: What makes this model unique?
This model leverages the CANINE architecture, which processes text directly at the character level, making it particularly efficient for sentiment analysis tasks without the need for complex tokenization.
Q: What are the recommended use cases?
The model is best suited for sentiment analysis tasks, particularly in scenarios requiring binary classification of text sentiment. It's especially useful in production environments where consistent performance is crucial.
