Text Classification
Transformers
PyTorch
English
roberta
text classification
fact checking
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mwong/climatebert-base-f-fever-evidence-related")
model = AutoModelForSequenceClassification.from_pretrained("mwong/climatebert-base-f-fever-evidence-related", device_map="auto")Quick Links
FeverBert-related
FeverBert-related is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 91.23% with test dataset "mwong/fever-evidence-related". Using pretrained ClimateBert-f model, the classifier head is trained on Fever dataset.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mwong/climatebert-base-f-fever-evidence-related")