Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use rmahade2/fin_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rmahade2/fin_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rmahade2/fin_sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rmahade2/fin_sentiment") model = AutoModelForSequenceClassification.from_pretrained("rmahade2/fin_sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7174424b0153f98552ad00f218c40949c675f4220379a62042478036b2a20ab
- Size of remote file:
- 268 MB
- SHA256:
- feaf719d67def239a70427012479beb31accbf07320cb870986937bde79c4f1f
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