Text Classification
Transformers
PyTorch
TensorFlow
JAX
English
bert
financial-sentiment-analysis
sentiment-analysis
Instructions to use Ziffirpetek/Text-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ziffirpetek/Text-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ziffirpetek/Text-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ziffirpetek/Text-Classification") model = AutoModelForSequenceClassification.from_pretrained("Ziffirpetek/Text-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c62b816cd92351be87f70b4b0895c0a0c63a8cb112ffd07b046517e933e8bfd
- Size of remote file:
- 438 MB
- SHA256:
- e15a7b5738df7f17553399b6d94c6e2ff69c89245d066e8e5d183f5803a554e3
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