readme: # Steps to use this model
  This model uses tokenizer 'rinna/japanese-roberta-base'. Therefore, below steps are critical to run the model correctly.
  
  1. Create a local root directory on your system and new python environment.
  2. Install below requirements 
  
  ```
  transformers==4.12.2
  torch==1.10.0
  numpy==1.21.3
  pandas==1.3.4
  sentencepiece==0.1.96
  ```
  3. Go to link: "https://huggingface.co/spaces/shubh2014shiv/Japanese_NLP/tree/main" and download the fine tuned weights "reviewSentiments_jp.pt" in same local root directory.
  4. Rename the downloaded weights as "reviewSentiments_jp.pt"
  5. Use below code in the newly created environment.
  
  ```
  from transformers import T5Tokenizer,BertForSequenceClassification
  import torch
  tokenizer = T5Tokenizer.from_pretrained('rinna/japanese-roberta-base')
  japanese_review_text = "履きやすい。タイムセールで購入しました。見た目以上にカッコいいです。(^^)"
  encoded_data = tokenizer.batch_encode_plus([japanese_review_text ],
                                                     add_special_tokens=True,
                                                     return_attention_mask=True,
                                                     padding=True,
                                                     max_length=200,
                                                     return_tensors='pt',
                                                     truncation=True)
  input_ids = encoded_data['input_ids']
  attention_masks = encoded_data['attention_mask']
  model = BertForSequenceClassification.from_pretrained("shubh2014shiv/jp_review_sentiments_amzn",
                                                                        num_labels=2,
                                                                        output_attentions=False,
                                                                        output_hidden_states=False)
  model.load_state_dict(torch.load('reviewSentiments_jp.pt',map_location=torch.device('cpu')))
  inputs = { 'input_ids': input_ids,
                'attention_mask': attention_masks}
  with torch.no_grad():
    outputs = model(**inputs)
  
  logits = outputs.logits
  logits = logits.detach().cpu().numpy()
  scores = 1 / (1 + np.exp(-1 * logits))
  result = {"TEXT (文章)": jp_review_text,'NEGATIVE (ネガティブ)': scores[0][0], 'POSITIVE (ポジティブ)': scores[0][1]}
  ```
  
  Output:
  
  {'TEXT (文章)': '履きやすい。タイムセールで購入しました。見た目以上にカッコいいです。(^^)', 'NEGATIVE (ネガティブ)': 0.023672901, 'POSITIVE (ポジティブ)': 0.96819043}