Text Classification
Transformers
PyTorch
Safetensors
xlm-roberta
TextClassificationPipeline
text-embeddings-inference
Instructions to use ssharoff/genres with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ssharoff/genres with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ssharoff/genres")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ssharoff/genres") model = AutoModelForSequenceClassification.from_pretrained("ssharoff/genres") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f93e0235a4b65b910f60f57299362aa7dca00b444e644941feba52bd88ab09d
- Size of remote file:
- 3.38 kB
- SHA256:
- 721fdc279d323bd54cf3817e32d96d06ca28bfb028f99c992e7b1438d9989c4d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.