---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-large
tags:
- generated_from_trainer
model-index:
- name: binary_paragraph
  results: []
---

<!-- 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. -->

# binary_paragraph

This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2458
- Classification Report: {'0': {'precision': 0.930564166150031, 'recall': 0.9428391959798995, 'f1-score': 0.9366614664586583, 'support': 1592.0}, '1': {'precision': 0.6192468619246861, 'recall': 0.5692307692307692, 'f1-score': 0.593186372745491, 'support': 260.0}, 'accuracy': 0.8903887688984882, 'macro avg': {'precision': 0.7749055140373586, 'recall': 0.7560349826053343, 'f1-score': 0.7649239196020747, 'support': 1852.0}, 'weighted avg': {'precision': 0.8868587130730388, 'recall': 0.8903887688984882, 'f1-score': 0.8884414209049739, 'support': 1852.0}}

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 256
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Classification Report                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 25   | 0.2760          | {'0': {'precision': 0.8786867000556483, 'recall': 0.9918341708542714, 'f1-score': 0.9318383003835939, 'support': 1592.0}, '1': {'precision': 0.7636363636363637, 'recall': 0.16153846153846155, 'f1-score': 0.26666666666666666, 'support': 260.0}, 'accuracy': 0.8752699784017278, 'macro avg': {'precision': 0.8211615318460059, 'recall': 0.5766863161963665, 'f1-score': 0.5992524835251303, 'support': 1852.0}, 'weighted avg': {'precision': 0.8625349249643881, 'recall': 0.8752699784017278, 'f1-score': 0.8384556736198784, 'support': 1852.0}} |
| No log        | 2.0   | 50   | 0.2626          | {'0': {'precision': 0.8689240851993446, 'recall': 0.9993718592964824, 'f1-score': 0.9295939234589541, 'support': 1592.0}, '1': {'precision': 0.9523809523809523, 'recall': 0.07692307692307693, 'f1-score': 0.1423487544483986, 'support': 260.0}, 'accuracy': 0.8698704103671706, 'macro avg': {'precision': 0.9106525187901484, 'recall': 0.5381474681097796, 'f1-score': 0.5359713389536763, 'support': 1852.0}, 'weighted avg': {'precision': 0.8806404920390952, 'recall': 0.8698704103671706, 'f1-score': 0.81907354336028, 'support': 1852.0}}    |
| No log        | 3.0   | 75   | 0.2458          | {'0': {'precision': 0.930564166150031, 'recall': 0.9428391959798995, 'f1-score': 0.9366614664586583, 'support': 1592.0}, '1': {'precision': 0.6192468619246861, 'recall': 0.5692307692307692, 'f1-score': 0.593186372745491, 'support': 260.0}, 'accuracy': 0.8903887688984882, 'macro avg': {'precision': 0.7749055140373586, 'recall': 0.7560349826053343, 'f1-score': 0.7649239196020747, 'support': 1852.0}, 'weighted avg': {'precision': 0.8868587130730388, 'recall': 0.8903887688984882, 'f1-score': 0.8884414209049739, 'support': 1852.0}}     |


### Framework versions

- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1