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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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license: mit |
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datasets: |
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- SetFit/mnli |
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language: |
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- en |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: modernbert-setfit-nli |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: SetFit/mnli |
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type: SetFit/mnli |
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args: SetFit/mnli |
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metrics: |
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- type: precision |
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value: 0.8463114754098361 |
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name: Precision |
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- type: recall |
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value: 0.8463114754098361 |
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name: Recall |
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- type: f1 |
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value: 0.8463114754098361 |
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name: F1 |
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- type: accuracy |
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value: 0.8463114754098361 |
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name: Accuracy |
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base_model: |
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- answerdotai/ModernBERT-base |
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pipeline_tag: text-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# modernbert-setfit-nli |
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## Model Description |
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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). |
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## Intended Uses & Limitations |
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### Intended Uses |
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- **Natural Language Inference (NLI):** Suitable for classifying relationships between pairs of sentences. |
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- **Text Understanding Tasks:** Can be applied to other similar tasks requiring sentence pair classification. |
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### Limitations |
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- **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. |
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- **Context Length:** The tokenizer’s maximum sequence length is 512 tokens. Inputs longer than this will be truncated. |
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- **Resource Intensive:** May require a modern GPU for efficient inference on large datasets. |
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This model is a starting point for NLI tasks and may need further fine-tuning for domain-specific applications. |
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## Training Details: |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Framework versions |
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- Transformers 4.48.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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## References |
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- **GitHub Repository:** The training code is available a my [GitHub repository](https://github.com/sfarrukhm/model_finetune.git). |
