Update README.md

README.md

@@ -65,6 +65,7 @@ tags:
 ---
 
 Find the v1 (TensorFlow) model on [this page](https://github.com/FredZhang7/tfjs-node-tiny/releases/tag/text-classification).
+The license for the v1 model is Apache 2.0
 
 <br>
 
@@ -90,4 +91,41 @@ Training on Toxi Text 3M alone results in a biased model that classifies short t
 <br>
 
 Models tested for v2: roberta, xlm-roberta, bert-small, bert-base-cased/uncased, bert-multilingual-cased/uncased, and alberta-large-v2.
-From these models, I chose bert-multilingual-cased because of its higher resource efficiency and performance than the rest for this particular task.
+Of these, I chose bert-multilingual-cased because it performs better with the same amount of resources as the others for this particular task.
+
+<br>
+
+## PyTorch
+
+```python
+text = "hello world!"
+
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = AutoTokenizer.from_pretrained("FredZhang7/one-for-all-toxicity-v3")
+model = AutoModelForSequenceClassification.from_pretrained("FredZhang7/one-for-all-toxicity-v3").to(device)
+
+encoding = tokenizer.encode_plus(
+    text,
+    add_special_tokens=True,
+    max_length=208,
+    padding="max_length",
+    truncation=True,
+    return_tensors="pt"
+)
+print('device:', device)
+input_ids = encoding["input_ids"].to(device)
+attention_mask = encoding["attention_mask"].to(device)
+
+with torch.no_grad():
+    outputs = model(input_ids, attention_mask=attention_mask)
+    logits = outputs.logits
+    predicted_labels = torch.argmax(logits, dim=1)
+
+print(predicted_labels)
+```
+
+## Attribution
+- If you distribute, remix, adapt, or build upon One-for-all Toxicity v3, please credit "AIstrova Technologies Inc." in your README.md, application description, research, or website.