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\"cardiffnlp/twitter-xlm-roberta-base-sentiment\" # use this to finetune the sentiment classifier\nMAX_TRAINING_EXAMPLES = 1000 # set this to -1 if you want to use the whole training set","metadata":{"id":"r3OxewRLFaK1","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data","metadata":{"id":"XWqZ7LGMFeHV"}},{"cell_type":"markdown","source":"Testing the model with German sentiment data\\\nhttps://github.com/oliverguhr/german-sentiment?tab=readme-ov-file#data-sets","metadata":{"id":"a1A24j8pFhup"}},{"cell_type":"code","source":"test_text = open(\"/kaggle/input/fine-tuning-sentiment-analysis-data/test_text.txt\", encoding='latin-1').read().rstrip('\\n').split('\\n')\n# test_labels = open(\"/kaggle/input/fine-tuning-sentiment-analysis-data/test_labels.txt\", encoding='latin-1').read().rstrip('\\n').split('\\n')","metadata":{"id":"fvM2mCi2yC8c","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with open(\"/kaggle/input/fine-tuning-sentiment-analysis-data/test_labels.txt\", encoding='latin-1') as file:\n    test_labels = [int(line.strip()) for line in file]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tokenizer = AutoTokenizer.from_pretrained(MODEL, use_fast=True)","metadata":{"id":"1IjMOsNSyC8d","outputId":"1d39672c-5f4f-46d3-90c9-050b838179c3","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = AutoModelForSequenceClassification.from_pretrained(MODEL)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_encodings = tokenizer(test_text, max_length=128, truncation=True, padding=True)","metadata":{"id":"Rp0llWQVyC8e","outputId":"99f26c0d-406a-4223-8628-d57a3f8d8eb1","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class MyDataset(torch.utils.data.Dataset):\n    def __init__(self, encodings, labels):\n        self.encodings = encodings\n        self.labels = labels\n\n    def __getitem__(self, idx):\n        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n        item['labels'] = torch.tensor(self.labels[idx])\n        return item\n\n    def __len__(self):\n        return len(self.labels)\n\ntest_dataset = MyDataset(test_encodings, test_labels)","metadata":{"id":"1MzkHFG5yC8f","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False)\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel.to(device)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Use batch processing for faster testing","metadata":{}},{"cell_type":"code","source":"def predict(model, data_loader):\n    model.eval()\n    predictions = []\n    real_labels = []\n    with torch.no_grad():\n        for batch in data_loader:\n            input_ids = batch['input_ids'].to(device)\n            attention_mask = batch['attention_mask'].to(device)\n            labels = batch['labels'].to(device)\n\n            outputs = model(input_ids, attention_mask=attention_mask)\n            logits = outputs.logits\n\n            preds = torch.argmax(logits, dim=1)\n            predictions.extend(preds.cpu().numpy())\n            real_labels.extend(labels.cpu().numpy())\n    return predictions, real_labels","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions, real_labels = predict(model, test_loader)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"report = classification_report(real_labels, predictions, target_names=['Negative', 'Neutral', 'Positive'])\nprint(report)","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}