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", device_map="auto")# 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:
- 54c48f4459f145db004e614911f26bb4283452f5a9493e0d6621c71a6358f254
- Size of remote file:
- 2.99 kB
- SHA256:
- c5863bb712e166cc2f8cc31818bb880ab744659b8bece2add13355cbbce0d4bf
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