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modernbert-setfit-nli

Text Classification
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metadata
library_name: transformers
tags:
  - generated_from_trainer
license: mit
datasets:
  - SetFit/mnli
language:
  - en
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: modernbert-setfit-nli
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: SetFit/mnli
          type: SetFit/mnli
          args: SetFit/mnli
        metrics:
          - type: precision
            value: 0.8463114754098361
            name: Precision
          - type: recall
            value: 0.8463114754098361
            name: Recall
          - type: f1
            value: 0.8463114754098361
            name: F1
          - type: accuracy
            value: 0.8463114754098361
            name: Accuracy
base_model:
  - answerdotai/ModernBERT-base
pipeline_tag: text-classification

modernbert-setfit-nli

Model Description

This model is a fine-tuned version of answerdotai/ModernBERT-base trained on a subset of the SetFit/mnli dataset. It is trained for natural language inference (NLI) tasks, where the goal is to determine the relationship between two text inputs (e.g., entailment, contradiction, or neutrality).

Intended Uses & Limitations

Intended Uses

  • Natural Language Inference (NLI): Suitable for classifying relationships between pairs of sentences.
  • Text Understanding Tasks: Can be applied to other similar tasks requiring sentence pair classification.

Limitations

  • Dataset-Specific Biases: The model was fine-tuned on 30,000 samples from the SetFit/mnli dataset and may not generalize well to domains significantly different from the training data.
  • Context Length: The tokenizer’s maximum sequence length is 512 tokens. Inputs longer than this will be truncated.
  • Resource Intensive: May require a modern GPU for efficient inference on large datasets.

This model is a starting point for NLI tasks and may need further fine-tuning for domain-specific applications.

Training Details:

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 3

Framework versions

  • Transformers 4.48.0
  • Pytorch 2.5.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.21.0

References