|
--- |
|
language: en |
|
tags: |
|
- en |
|
- english |
|
- gpt2 |
|
- gpt3 |
|
- text-generation |
|
- lm |
|
- nlp |
|
datasets: |
|
- cnn_dailymail |
|
widget: |
|
- text: "Ever noticed how plane seats appear to be getting smaller and smaller? " |
|
|
|
inference: |
|
parameters: |
|
max_length: 120 |
|
do_sample: True |
|
temperature: 0.8 |
|
--- |
|
|
|
# GPT-3 small |
|
|
|
Pretrained GPT-3 small, continuing the development of GPT NEO, with architecture that purposefully mimics that of GPT-3, model was trained on CNN Daily Mail News dataset for text generation. |
|
# How to use the model |
|
|
|
~~~~ |
|
from transformers import GPT2Tokenizer, GPTNeoForCausalLM |
|
|
|
tokenizer = GPT2Tokenizer.from_pretrained('gpt3-small-finetune-cnndaily-news') |
|
model = GPTNeoForCausalLM.from_pretrained('gpt3-small-finetune-cnndaily-news') |
|
|
|
text = "Ever noticed how plane seats appear to be getting smaller and smaller? " |
|
input_ids = tokenizer.encode(text, return_tensors='pt') |
|
max_length = 150 |
|
|
|
sample_outputs = model.generate(input_ids, do_sample=True, max_length=max_length,temperature = 0.8) |
|
|
|
for i, sample_output in enumerate(sample_outputs): |
|
print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist()))) |
|
print('\n---') |
|
~~~~ |
|
|
|
|
|
## Author |
|
` |
|
Phan Minh Toan |
|
` |
