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Nepali
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+ "Sentence: 'यो मोडेल राम्रो छ' - Positive sentiment\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "import os\n",
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+ "import torch\n",
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+ "from transformers import BertForSequenceClassification, BertTokenizer\n",
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+ "\n",
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+ "tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-uncased')\n",
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+ "model = BertForSequenceClassification.from_pretrained('/kaggle/input/my-model/', num_labels=3, ignore_mismatched_sizes=True)\n",
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+ "\n",
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+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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+ "model.to(device)\n",
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+ "\n",
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+ "sentences = ['यो मोडेल राम्रो छ']\n",
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+ "\n",
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+ "for text in sentences:\n",
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+ " inputs = tokenizer.encode_plus(\n",
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+ " text,\n",
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+ " add_special_tokens=True,\n",
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+ " max_length=128,\n",
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+ " padding='max_length',\n",
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+ " return_tensors='pt'\n",
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+ " )\n",
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+ " input_ids = inputs['input_ids'].to(device)\n",
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+ " attention_mask = inputs['attention_mask'].to(device)\n",
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+ "\n",
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+ " model.eval()\n",
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+ " with torch.no_grad():\n",
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+ " inputs = {\n",
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+ " 'input_ids': input_ids,\n",
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+ " 'attention_mask': attention_mask\n",
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+ " }\n",
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+ " \n",
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+ " predicted_labels = torch.argmax(logits, dim=1)\n",
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+ " \n",
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+ " if predicted_labels.item() == 0:\n",
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+ " print(f\"Sentence: '{text}' - Negative sentiment\")\n",
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+ " else:\n",
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+ " print(f\"Sentence: '{text}' - Positive sentiment\")"
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