Text Classification
Transformers
PyTorch
TensorBoard
perceiver
Generated from Trainer
Eval Results (legacy)
Instructions to use oandreae/financial_sentiment_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oandreae/financial_sentiment_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oandreae/financial_sentiment_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oandreae/financial_sentiment_model") model = AutoModelForSequenceClassification.from_pretrained("oandreae/financial_sentiment_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 018b50533f5415b32d1e820cc750f6e04ddf70744725b0b4f1af78e89a2c489d
- Size of remote file:
- 825 MB
- SHA256:
- 3ffc49cdf7fa3c6b37e0f12aaba38f10384f7ffab47dab58b88e68e67c5ad2e4
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