Text Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
Sentiment Classification
Finance
Deberta-v2
text-embeddings-inference
Instructions to use RashidNLP/Finance-Sentiment-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RashidNLP/Finance-Sentiment-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RashidNLP/Finance-Sentiment-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RashidNLP/Finance-Sentiment-Classification") model = AutoModelForSequenceClassification.from_pretrained("RashidNLP/Finance-Sentiment-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f733ca4a1cee3941f1dda6df5710cecbd26e5d630faad14970c52281ad751df
- Size of remote file:
- 738 MB
- SHA256:
- bf2a250d8f3c422ab51c6cdb4b2cd7d6e5a12a368ec9e3d0b9b998552ebecbe7
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