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Train Df has 23 facts recorded in Dontopedia across 7 references, with 3 live disagreements.
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doc:beam/c0918454-86e0-44f7-85fe-2eb2a8e147e5### Step 3: Data Augmentation 1. **Back-Translation**: Translate your queries to another language and then back to the original language. 2. **Paraphrasing**: Use paraphrasing techniques to generate new variations of your queries. 3. **Syn…
doc:beam/14cf4eab-a053-4cf0-b374-9022e5e69c19model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(df['label'].unique())) tokenizer = AutoTokenizer.from_pretrained(model_name) # Tokenize the data train_encodings = tokenizer(train_df['query'].tolist(), …
doc:beam/974a068f-3f5b-4b96-b53c-9e0c612e3beetest_encodings = tokenize_data(tokenizer, test_df['query']) # Create datasets train_dataset = QueryDataset(train_encodings, train_df['label'].tolist()) test_dataset = QueryDataset(test_encodings, test_df['label'].tolist()) …
doc:beam/a2616d4b-38c9-4c2c-832f-d576e35ce8b4# Split the data into training and testing sets train_df, test_df = train_test_split(df, test_size=0.2, random_state=_) # Define a function to tokenize the data def tokenize_data(tokenizer, texts): return tokenizer(texts.tolist(), trun…
doc:beam/e90baac4-24b6-4abb-89e2-a81f7d246e29accuracy = accuracy_score(test_df['label'], predicted_labels) print(f"Accuracy for {model_name}: {accuracy:.2f}") return accuracy # List of models to experiment with models_to_test = [ "bert-base-uncased", "roberta-bas…
doc:beam/6a684f54-32bd-416e-9981-9346a1a4b9591. **Hyperparameter Search**: Use grid search or random search to find the best hyperparameters. 2. **Learning Rate Scheduling**: Use learning rate schedulers like `ReduceLROnPlateau` or `CosineAnnealingLR`. ### Step 4: Ensemble Methods 1…
doc:beam/b1c13f74-d586-4364-a78a-3777454bef7f"distilbert-base-uncased" ] # Experiment with different models best_accuracy = 0 best_model = None for model_name in models_to_test: accuracy = train_and_evaluate_model(model_name, train_df, test_df) if accuracy > best_accuracy…
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