update readme

README.md

@@ -33,15 +33,14 @@ For more detailed code regarding generating the annotations in Toxic Commons, tr
 
 # How to Use
 
-```
-from transformers import AutoTokenizer
-from celadon.model import MultiHeadDebertaForSequenceClassification
+```py
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-tokenizer = AutoTokenizer.from_pretrained("celadon")
-model = MultiHeadDebertaForSequenceClassification.from_pretrained("celadon")
+model = AutoModelForSequenceClassification.from_pretrained("PleIAs/celadon", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("PleIAs/celadon", trust_remote_code=True)
 model.eval()
 
-sample_text = "This is an example of a normal sentence"
+sample_text = "A very gender inappropriate comment"
 
 inputs = tokenizer(sample_text, return_tensors="pt", padding=True, truncation=True)
 outputs = model(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'])
@@ -53,6 +52,21 @@ predictions = outputs.argmax(dim=-1).squeeze().tolist()
 print(f"Text: {sample_text}")
 for i, category in enumerate(categories):
     print(f"Prediction for Category {category}: {predictions[i]}")
+# Text: A very gender inappropriate comment
+# Prediction for Category Race/Origin: 0
+# Prediction for Category Gender/Sex: 3
+# Prediction for Category Religion: 0
+# Prediction for Category Ability: 0
+# Prediction for Category Violence: 0
+```
+
+you can also use transformers pipelines to get a more streamlined experience
+
+```py
+pipe = pipeline("text-classification", model="PleIAs/celadon", trust_remote_code=True)
+result = pipe("This is an example of a normal sentence")
+print(result)
+# [{'Race/Origin': 0, 'Gender/Sex': 3, 'Religion': 0, 'Ability': 0, 'Violence': 0}]
 ```
 
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