---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# modernbert-setfit-nli

## Model Description

This model is a fine-tuned version of [`answerdotai/ModernBERT-base`](https://huggingface.co/answerdotai/ModernBERT-base) trained on a subset of the [SetFit/mnli](https://huggingface.co/datasets/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

- **GitHub Repository:** The training code is available a my [GitHub repository](https://github.com/sfarrukhm/model_finetune.git).