Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use dariast/FacebookAI_roberta-large_custom_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dariast/FacebookAI_roberta-large_custom_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dariast/FacebookAI_roberta-large_custom_data")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dariast/FacebookAI_roberta-large_custom_data") model = AutoModelForSequenceClassification.from_pretrained("dariast/FacebookAI_roberta-large_custom_data") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47af31832db3109e9106ec48685525ee50f32f788779dd50979f8a77b2be437a
- Size of remote file:
- 1.42 GB
- SHA256:
- 2771269c4687db40640238af9dd1b47c4ddb635aa65cbb7ad2b03c773ecd781f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.