Create README.md

README.md

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+# 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 should be as below:
+{'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}