rbelanec/train_rte_42_1774791065
The rbelanec/train_rte_42_1774791065 model is a 1 billion parameter language model fine-tuned from meta-llama/Llama-3.2-1B-Instruct. It was specifically trained on the RTE (Recognizing Textual Entailment) dataset, indicating an optimization for tasks requiring natural language inference. With a context length of 32768 tokens, this model is designed for understanding and determining the relationship between text pairs, such as entailment, contradiction, or neutrality.
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Overview
This model, rbelanec/train_rte_42_1774791065, is a 1 billion parameter language model derived from meta-llama/Llama-3.2-1B-Instruct. It has been specifically fine-tuned on the RTE (Recognizing Textual Entailment) dataset, which focuses on natural language inference tasks. The training process involved 5 epochs with a learning rate of 5e-05 and a batch size of 8, utilizing the AdamW_Torch optimizer.
Key Capabilities
- Natural Language Inference (NLI): Optimized for tasks that involve determining logical relationships (entailment, contradiction, neutral) between two text snippets.
- Textual Entailment: Directly trained on the RTE dataset, making it suitable for identifying if one sentence can be inferred from another.
Good for
- Applications requiring textual entailment recognition.
- Research and development in natural language inference.
- Use cases where a compact model (1B parameters) is needed for NLI-specific tasks.