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
Request: DOI
#6 opened over 1 year ago
by
stani000
Add evaluation results on the sentences_allagree config and train split of financial_phrasebank
#5 opened over 2 years ago
by
autoevaluator
Does the program support num_labels = 2?
1
#4 opened over 2 years ago
by
ewmiao
Adding `safetensors` variant of this model
#3 opened about 3 years ago
by
SFconvertbot
Accuracy
👍 2
#2 opened almost 4 years ago
by
ehrencrona