Fix Reward Calculation in `example/2022-12-10-textrl-elon-musk.ipynb`

[Alanhsiu]

In the notebook example/2022-12-10-textrl-elon-musk.ipynb, the reward calculation in the MyRLEnv class should be updated for correct scoring. Specifically, the function get_reward needs modification.

Current Code:

class MyRLEnv(TextRLEnv):
    def get_reward(self, input_item, predicted_list, finish):
        reward = 0
        if finish or len(predicted_list) >= self.env_max_length:
            predicted_text = tokenizer.convert_tokens_to_string(predicted_list[0])
            # sentiment classifier
            reward = sentiment(input_item[0] + predicted_text)[0][0]['score'] * 10
        return reward

The current code concatenates input_item[0] with the predicted text to calculate the sentiment score. However, input_item should be referenced differently to ensure proper reward calculation.

reward = sentiment(input_item['input'] + predicted_text)[0][0]['score'] * 10

[voidful]

feel free to submit a PR to fix this issue~