Text Classification
Transformers
Safetensors
English
roberta
genre
books
multi-label
dataset tools
text-embeddings-inference
Instructions to use BEE-spoke-data/roberta-base-description2genre with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BEE-spoke-data/roberta-base-description2genre with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BEE-spoke-data/roberta-base-description2genre")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/roberta-base-description2genre") model = AutoModelForSequenceClassification.from_pretrained("BEE-spoke-data/roberta-base-description2genre") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e56a8c4b4bad786209a0e46d354ef5fdac94d8b71396ca03d773a9acaa76103f
- Size of remote file:
- 4.6 kB
- SHA256:
- ffcdde489b8cb6656f86cad05fc0b45b1a8c7061c8dc94ef0ae77c80b51974bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.