Add pipeline and full example usage

README.md

@@ -12,6 +12,63 @@ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf
 
 The model is fine-tuned for text classification of logical fallacies. There are a total of 14 classes: ad hominem, ad populum, appeal to emotion, circular reasoning, equivocation, fallacy of credibility, fallacy of extension, fallacy of logic, fallacy of relevance, false causality, false dilemma, faulty generalization, intentional, and miscellaneous.
 
+## Example Pipeline
+
+```
+from transformers import pipeline
+
+text = "We know that the earth is flat because it looks and feels flat."
+model_path = "q3fer/distilbert-base-fallacy-classification"
+pipe = pipeline("text-classification", model=model_path, tokenizer=model_path)
+pipe(text)
+```
+
+```
+[{'label': 'circular reasoning', 'score': 0.951125979423523}]
+```
+
+## Full Classification Example
+
+```
+import torch
+from transformers import AutoTokenizer
+from transformers import AutoModelForSequenceClassification
+
+model = AutoModelForSequenceClassification.from_pretrained("q3fer/distilbert-base-fallacy-classification")
+tokenizer = AutoTokenizer.from_pretrained("q3fer/distilbert-base-fallacy-classification")
+
+text = "We know that the earth is flat because it looks and feels flat."
+inputs = tokenizer(text, return_tensors='pt')
+
+with torch.no_grad():
+  logits = model(**inputs)
+  scores = logits[0][0]
+  scores = torch.nn.Softmax(dim=0)(scores)
+
+  _, ranking = torch.topk(scores, k=scores.shape[0])
+  ranking = ranking.tolist()
+
+results = [f"{i+1}) {model.config.id2label[ranking[i]]} {scores[ranking[i]]:.4f}" for i in range(scores.shape[0])]
+print('\n'.join(results))
+```
+
+```
+1) circular reasoning 0.9511
+2) fallacy of logic 0.0154
+3) equivocation 0.0080
+4) fallacy of credibility 0.0069
+5) ad populum 0.0028
+6) fallacy of extension 0.0025
+7) intentional 0.0024
+8) faulty generalization 0.0021
+9) appeal to emotion 0.0021
+10) fallacy of relevance 0.0019
+11) false dilemma 0.0017
+12) ad hominem 0.0013
+13) false causality 0.0012
+14) miscellaneous 0.0004
+```
+
 ## Training and evaluation data
 
 The [Logical Fallacy Dataset](https://github.com/causalNLP/logical-fallacy) is used for training and evaluation.