|
--- |
|
language: |
|
- en |
|
widget: |
|
- text: Tesla range |
|
- text: Nissan Leaf is |
|
- text: Tesla is |
|
- text: The best electric car |
|
--- |
|
|
|
# Neo-GPT-Title-Generation-Electric-Car |
|
|
|
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 |
|
|
|
```python |
|
import pandas as pd |
|
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('\ |
|
',' ').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) |
|
|
|
``` |
|
|
|
# Todo |
|
- Improve the quality of the training sample |
|
- Add more data |
|
|
|
|
