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
- Xet hash:
- 4ffe21a9072e8a0cc8760d990a0deb7687d6e1a8430d7b6a8846c4a6160ffd2a
- Size of remote file:
- 329 MB
- SHA256:
- 4d8d337e98173d75dc96a3035981aa909d0a7b0824ec1c27b04aee2f62c7cecc
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