Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use whyabhinay/fin_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whyabhinay/fin_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="whyabhinay/fin_sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("whyabhinay/fin_sentiment") model = AutoModelForSequenceClassification.from_pretrained("whyabhinay/fin_sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f54ba1ed9fdf9af4e2e993c8e329ef4a693400dbd83e1244ea91837e6577a777
- Size of remote file:
- 268 MB
- SHA256:
- dbf57cd32bef5cd4e3d315cf8233be625b76bd2a28c11ff7e7db1a7d426b4543
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.