| --- |
| 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). |
