--- license: mit datasets: - prabinpanta0/Rep00Zon language: - en metrics: - accuracy pipeline_tag: question-answering tags: - general_knowledge - 'Question_Answers' --- # ZenGQ - BERT for Question Answering This is a fine-tuned BERT model for question answering tasks, trained on a custom dataset. ## Model Details - **Model:** BERT-base-uncased - **Task:** Question Answering - **Dataset:** [Rep00Zon](https://huggingface.co/datasets/prabinpanta0/Rep00Zon) ## Usage ### Load the model ```python from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline # Load the tokenizer and model from Hugging Face tokenizer = AutoTokenizer.from_pretrained("prabinpanta0/ZenGQ") model = AutoModelForQuestionAnswering.from_pretrained("prabinpanta0/ZenGQ") # Create a pipeline for question answering qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer) # Define your context and questions contexts = ["Berlin is the capital of Germany.", "Paris is the capital of France.", "Madrid is the capital of Spain."] questions = [ "What is the capital of Germany?", "Which city is the capital of France?", "What is the capital of Spain?" ] # Get answers for context, question in zip(contexts, questions): result = qa_pipeline(question=question, context=context) print(f"Question: {question}") print(f"Answer: {result['answer']}\n") ``` ### Training Details - Epochs: 3 - Training Loss: 2.050335, 1.345047, 1.204442 ### Token ``` text = "Berlin is the capital of Germany. Paris is the capital of France. Madrid is the capital of Spain." tokens = tokenizer.tokenize(text) print(tokens) ``` *Output:* ```['berlin', 'is', 'the', 'capital', 'of', 'germany', '.', 'paris', 'is', 'the', 'capital', 'of', 'france', '.', 'madrid', 'is', 'the', 'capital', 'of', 'spain', '.']``` ### Dataset The model was trained on the [Rep00Zon](https://huggingface.co/datasets/prabinpanta0/Rep00Zon) dataset. ### License This model is licensed under the MIT License.