rbelanec/train_mnli_42_1773765555
The rbelanec/train_mnli_42_1773765555 is a 1 billion parameter language model fine-tuned from meta-llama/Llama-3.2-1B-Instruct. This model is specifically optimized for Natural Language Inference (NLI) tasks, having been trained on the MNLI dataset. It demonstrates a validation loss of 0.2161, indicating its proficiency in classifying textual entailment relationships. Its primary strength lies in understanding and categorizing logical relationships between sentences.
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Model Overview
The rbelanec/train_mnli_42_1773765555 is a 1 billion parameter language model derived from the meta-llama/Llama-3.2-1B-Instruct architecture. It has been specifically fine-tuned on the MNLI (Multi-Genre Natural Language Inference) dataset to excel at natural language inference tasks.
Key Capabilities
- Natural Language Inference (NLI): The model is specialized in determining the logical relationship between a premise and a hypothesis, classifying them as entailment, contradiction, or neutral.
- Performance: Achieved a validation loss of 0.2161 during training, indicating strong performance on the MNLI evaluation set.
- Training Details:
- Trained for 5 epochs with a learning rate of 5e-05.
- Utilized AdamW optimizer with a cosine learning rate scheduler.
- Processed over 191 million input tokens during its training run.
When to Use This Model
This model is particularly well-suited for applications requiring robust natural language inference capabilities. Consider using it for:
- Textual Entailment Classification: Identifying logical relationships between sentences.
- Fact Verification: Assessing the consistency of claims against evidence.
- Question Answering Systems: Improving the understanding of question-answer relationships.
Limitations
As a specialized model, its primary strength is NLI. For broader generative tasks or other specific NLP applications, its performance may not match models fine-tuned for those particular use cases.