Text Classification
Transformers
PyTorch
Safetensors
English
roberta
discourse-marker-prediction
discourse-connective-prediction
discourse-connective
discourse-marker
discourse-relation-prediction
pragmatics
discourse
text-embeddings-inference
Instructions to use sileod/roberta-base-discourse-marker-prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sileod/roberta-base-discourse-marker-prediction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sileod/roberta-base-discourse-marker-prediction")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sileod/roberta-base-discourse-marker-prediction") model = AutoModelForSequenceClassification.from_pretrained("sileod/roberta-base-discourse-marker-prediction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8366a10a2277db1fcc713b43ae488da097ffdcab713973aa4ee3cd1d41a2f2ed
- Size of remote file:
- 499 MB
- SHA256:
- 57cc527274cdcfaf49f1366594eb30512c45ecaf2a3a5b800eb82676868408ba
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