Add pipeline example

README.md

@@ -12,3 +12,48 @@ widget:
 
 Title generator based on Neo-GPT 125M fine-tuned on a dataset of 39k url's title. All urls are selected on the TOP 10 google on a list of Keywords about "Electric car" - "Electric car for sale".
 
+# Pipeline example
+
+import pandas as pd
+```python
+from transformers import AutoModelForMaskedLM
+from transformers import GPT2Tokenizer, TrainingArguments, AutoModelForCausalLM, AutoConfig 
+
+model = AutoModelForCausalLM.from_pretrained('Martian/Neo-GPT-Title-Generation-Electric-Car')
+
+tokenizer = GPT2Tokenizer.from_pretrained('Martian/Neo-GPT-Title-Generation-Electric-Car', bos_token='<|startoftext|>',
+                                          eos_token='<|endoftext|>', pad_token='<|pad|>')
+
+prompt = "<|startoftext|> Electric car"
+
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+
+gen_tokens = model.generate(input_ids, do_sample=True, top_k=100, min_length = 30, max_length=150, top_p=0.90, num_return_sequences=20, skip_special_tokens=True)
+
+list_title_gen = []
+
+for i, sample_output in enumerate(gen_tokens):
+    title = tokenizer.decode(sample_output, skip_special_tokens=True)
+    list_title_gen.append(title)
+    
+for i in list_title_gen:
+    try:
+        list_title_gen[list_title_gen.index(i)] = i.split(' | ')[0]       
+    except:
+        continue
+    try:
+        list_title_gen[list_title_gen.index(i)] = i.split(' - ')[0]
+    except:
+        continue
+    try:
+        list_title_gen[list_title_gen.index(i)] = i.split(' — ')[0]
+    except:
+        continue        
+
+list_title_gen = [sub.replace('�', ' ').replace('\r',' ').replace('\n',' ').replace('\t', ' ').replace('\xa0', '') for sub in list_title_gen]    
+list_title_gen = [sub if sub != '<|startoftext|> Electric car' else '' for sub in list_title_gen]  
+
+for i in list_title_gen:
+    print(i)
+
+```