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---
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). |