Instructions to use tim9510019/Financial-Sentiment-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tim9510019/Financial-Sentiment-Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tim9510019/Financial-Sentiment-Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tim9510019/Financial-Sentiment-Analysis") model = AutoModelForSequenceClassification.from_pretrained("tim9510019/Financial-Sentiment-Analysis") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tim9510019/Financial-Sentiment-Analysis")
model = AutoModelForSequenceClassification.from_pretrained("tim9510019/Financial-Sentiment-Analysis")Quick Links
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tim9510019/Financial-Sentiment-Analysis")