Text Classification
Transformers
PyTorch
English
roberta
argument mining
claims
sentence classification
text-embeddings-inference
Instructions to use pheinisch/roberta-base-150T-argumentative-sentence-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pheinisch/roberta-base-150T-argumentative-sentence-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pheinisch/roberta-base-150T-argumentative-sentence-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pheinisch/roberta-base-150T-argumentative-sentence-detector") model = AutoModelForSequenceClassification.from_pretrained("pheinisch/roberta-base-150T-argumentative-sentence-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab5c065110b8cef8b82ff6f6015042223d354acf49bf66002bd56fdfa225a583
- Size of remote file:
- 499 MB
- SHA256:
- 1a0e8114621c4f6d4f0b8ec08d0446f9e5814a66bf6f5fd9edcc2f988dbeba28
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