Instructions to use BVK97/Discord-NFT-Sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BVK97/Discord-NFT-Sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BVK97/Discord-NFT-Sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BVK97/Discord-NFT-Sentiment") model = AutoModelForSequenceClassification.from_pretrained("BVK97/Discord-NFT-Sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Discord Sentiment Analysis - (Context: NFTs)
This is a model derived from Twitter-roBERTa-base model trained on ~10K Discord messages from NFT-based Discord servers and finetuned for sentiment analysis with manually labelled data. The original Twitter-roBERTa-base model can be found here. This model is suitable for English.
- Git Repo: BVK project repository.
Labels: 0 -> Negative; 1 -> Neutral; 2 -> Positive
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