rbelanec/train_mrpc_42_1776331557
The rbelanec/train_mrpc_42_1776331557 model is a 1 billion parameter language model fine-tuned by rbelanec, based on the meta-llama/Llama-3.2-1B-Instruct architecture. It has a context length of 32768 tokens and is specifically optimized for the MRPC (Microsoft Research Paraphrase Corpus) dataset, achieving a validation loss of 0.1084. This model is designed for tasks requiring paraphrase detection and semantic similarity analysis.
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Model Overview
This model, rbelanec/train_mrpc_42_1776331557, is a fine-tuned version of the meta-llama/Llama-3.2-1B-Instruct base model, featuring 1 billion parameters and a 32768-token context length. It has been specifically adapted for the MRPC (Microsoft Research Paraphrase Corpus) dataset, demonstrating a validation loss of 0.1084 during its training.
Key Capabilities
- Paraphrase Detection: Optimized for identifying semantically equivalent sentences.
- Semantic Similarity: Capable of assessing the degree of similarity between two text snippets.
- Small Footprint: At 1 billion parameters, it offers a more efficient solution for specific NLP tasks compared to larger general-purpose models.
Training Details
The model was trained with a learning rate of 5e-06, a batch size of 8, and for 5 epochs using the AdamW optimizer with a cosine learning rate scheduler. The training process involved processing approximately 1.78 million input tokens, achieving its best validation loss early in the training cycle.
Use Cases
This model is particularly suitable for applications requiring:
- Text Deduplication: Identifying and removing redundant information.
- Question Answering Systems: Matching user queries to relevant information by understanding semantic equivalence.
- Information Retrieval: Improving search result relevance through paraphrase recognition.