rbelanec/train_qnli_42_1776331409
The rbelanec/train_qnli_42_1776331409 model is a 1 billion parameter language model fine-tuned from Meta Llama 3.2-1B-Instruct. It is specifically optimized for question-answering tasks, having been trained on the QNLI dataset. This model demonstrates a loss of 0.0583 on its evaluation set, indicating its proficiency in natural language inference for question answering. Its primary strength lies in accurately classifying whether a text snippet contains the answer to a given question.
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Model Overview
The rbelanec/train_qnli_42_1776331409 model is a specialized language model derived from meta-llama/Llama-3.2-1B-Instruct. It has been fine-tuned specifically on the QNLI (Question-answering Natural Language Inference) dataset to enhance its performance on question-answering tasks.
Key Capabilities
- Question Answering (QNLI): The model is designed to determine if a given text passage contains the answer to a specific question, achieving a loss of 0.0583 on its evaluation set.
- Efficient Inference: As a 1 billion parameter model, it offers a balance between performance and computational efficiency, making it suitable for applications requiring faster inference times compared to larger models.
Training Details
The model was trained with a learning rate of 5e-06, a batch size of 8, and for 5 epochs. The training utilized the AdamW optimizer with a cosine learning rate scheduler and a warmup ratio of 0.1. The training process involved processing over 56 million input tokens, with the lowest validation loss recorded at 0.0583 during the first epoch.
Intended Use Cases
This model is particularly well-suited for applications requiring natural language inference for question answering, such as:
- Information Retrieval: Identifying relevant passages that answer user queries.
- Reading Comprehension: Assessing understanding of text by answering questions based on provided content.
- Text Classification: Determining the relationship between a question and a context sentence.