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---
license: mit
language:
- en
datasets: climatebert/distilroberta-base-climate-f
tags:
- fact-checking
- climate
- text entailment
---
This model fine-tuned ClimateBert on the textual entailment task. Given (claim, evidence) pairs, the model predicts support (entailment), refute (contradict), or not enough info (neutral).
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained("amandakonet/climatebert-fact-checking", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("amandakonet/climatebert-fact-checking", use_auth_token=True)
features = tokenizer(['Beginning in 2005, however, polar ice modestly receded for several years'],
['Polar Discovery "Continued Sea Ice Decline in 2005'],
padding='max_length', truncation=True, return_tensors="pt", max_length=512)
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
``` |