Update README.md

README.md

@@ -26,12 +26,23 @@ model_name = "nightdessert/WeCheck"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing." # Input for Summarization/ Dialogue / Paraphrase
-hypothesis = "The movie was not good."   # Output for Summarization/ Dialogue / Paraphrase
+hypothesis = "The movie was not good." # Output for Summarization/ Dialogue / Paraphrase
 input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt", truncation_strategy="only_first", max_length=512)
 output = model(input["input_ids"].to(device))['logits'][:,0]  # device = "cuda:0" or "cpu"
 prediction = torch.sigmoid(output).tolist()
-print(prediction)
+print(prediction) #0.884
 ```
+or apply for a batch of samples
+```python
+premise = ["I first thought that I liked the movie, but upon second thought it was actually disappointing."]*3 # Input list for Summarization/ Dialogue / Paraphrase
+hypothesis = ["The movie was not good."]*3 # Output list for Summarization/ Dialogue / Paraphrase
+batch_tokens = tokenizer.batch_encode_plus(list(zip(premise, hypothesis)), padding=True, 
+            truncation=True, max_length=512, return_tensors="pt", truncation_strategy="only_first")
+output = model(batch_tokens["input_ids"].to(device))['logits'][:,0]  # device = "cuda:0" or "cpu"
+prediction = torch.sigmoid(output).tolist()
+print(prediction) #[0.884,0.884,0.884]
+```
+
 
 license: openrail
 pipeline_tag: text-classification