Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use rajistics/fin_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajistics/fin_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rajistics/fin_sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rajistics/fin_sentiment") model = AutoModelForSequenceClassification.from_pretrained("rajistics/fin_sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d0db35ad28b0748424051bb333bfcd606ba2c1a09af377025b3cd2a870135da
- Size of remote file:
- 268 MB
- SHA256:
- 44a05f7aeb4468b03182f134f486d21a8dd83902264f7ecd398744ae1275b85e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.