Text Classification
Transformers
PyTorch
TensorBoard
English
roberta
financial-sentiment-analysis
sentiment-analysis
sentence_50agree
Generated from Trainer
sentiment
finance
Eval Results (legacy)
text-embeddings-inference
Instructions to use nickmuchi/distilroberta-finetuned-financial-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickmuchi/distilroberta-finetuned-financial-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nickmuchi/distilroberta-finetuned-financial-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nickmuchi/distilroberta-finetuned-financial-text-classification") model = AutoModelForSequenceClassification.from_pretrained("nickmuchi/distilroberta-finetuned-financial-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- generated_from_trainer
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metrics:
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- f1
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widget:
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- text: "The USD rallied by 10% last night"
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example_title: "Bullish Sentiment"
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- generated_from_trainer
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metrics:
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- f1
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datasets:
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- financial_phrasebank
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- Kaggle Self label
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widget:
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- text: "The USD rallied by 10% last night"
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example_title: "Bullish Sentiment"
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