Text Classification
Transformers
PyTorch
English
bert
financial-sentiment-analysis
sentiment-analysis
text-embeddings-inference
Instructions to use ahmedrachid/FinancialBERT-Sentiment-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedrachid/FinancialBERT-Sentiment-Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ahmedrachid/FinancialBERT-Sentiment-Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis") model = AutoModelForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis") - Inference
- Notebooks
- Google Colab
- Kaggle
Add evaluation results on the sentences_allagree config and train split of financial_phrasebank
#5
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator π!
Your model has been evaluated on the sentences_allagree config and train split of the financial_phrasebank dataset by @du , using the predictions stored here.
Accept this pull request to see the results displayed on the Hub leaderboard.
Evaluate your model on more datasets here.