Text Classification
Transformers
PyTorch
English
roberta
argument mining
claims
sentence classification
text-embeddings-inference
# 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")Quick Links
EXPERIMENTAL roberta-base-150T-argumentative-sentence-detector
(this model might not be the optimal one for accomplishing the task)
- Task: Detects whether a sentence is argumentative (1 - yes/ 0 - not) given the topic and the sentence itself.
- language: English
- dataset: Few-Shot-150T Corpus v1.1 (FS150T-Corpus) fine-tuned roberta-base
Performace on test data (threshold: 0.5)
{'accuracy': 0.7451388888888889,
'f1': 0.6690712353471596,
'precision': 0.733201581027668,
'recall': 0.615257048092869}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pheinisch/roberta-base-150T-argumentative-sentence-detector")