create model card

README.md

@@ -4,10 +4,23 @@ How to use:
 from collections import deque
 from  bs4 import BeautifulSoup
 import requests
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5Tokenizer
+import torch
+
+model_name = 'artemnech/dialoT5-base'
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def generate(text, **kwargs):
+    model.eval()
+    inputs = tokenizer(text, return_tensors='pt').to(model.device)
+    with torch.no_grad():
+        hypotheses = model.generate(**inputs,  **kwargs)
+    return tokenizer.decode(hypotheses[0], skip_special_tokens=True)
 
 def dialog(context):
   
-    keyword = generate('keyword: ' + ' '.join(context), num_beams=2,)[0]
+    keyword = generate('keyword: ' + ' '.join(context), num_beams=2,)
     knowlege = ''
     if keyword != 'no_keywords':
         resp = requests.get(f"https://en.wikipedia.org/wiki/{keyword}")
@@ -17,7 +30,7 @@ def dialog(context):
     answ = generate(f'dialog: ' + knowlege + ' '.join(context), num_beams=3,
                     do_sample=True, temperature=1.1, encoder_no_repeat_ngram_size=5, 
                     no_repeat_ngram_size=5, 
-                    max_new_tokens  = 30)[0]
+                    max_new_tokens  = 30)
     return answ
 
 context =deque([], maxlen=4)