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---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-large
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: ModernBERT-large-llm-router
results: []
datasets:
- DevQuasar/llm_router_dataset-synth
pipeline_tag: text-classification
---
# ModernBERT-large-llm-router
This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on [DevQuasar/llm_router_dataset-synth](https://huggingface.co/datasets/DevQuasar/llm_router_dataset-synth).
It achieves the following results on the test set:
- Loss: 0.0536
- F1: 0.9933
## Model description
See original [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) model card for additional information. This model is intended to classify queries for LLM routing. More advanced queries get labeled 1 for large_llm and simpler queries get 0 for small_llm.
## Training procedure
Annotated training procedure available [in this notebook.](https://colab.research.google.com/drive/1G7oHp_8R4fmOSpjwaNB_T2NUJsmMh4Kw?usp=sharing) Methodology and code credits to [Phillip Schmid](https://huggingface.co/philschmid) from his [Fine-tune classifier with ModernBERT in 2025
](https://www.philschmid.de/fine-tune-modern-bert-in-2025) blog post.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0303 | 1.0 | 479 | 0.0317 | 0.9881 |
| 0.014 | 2.0 | 958 | 0.0374 | 0.9927 |
| 0.0044 | 3.0 | 1437 | 0.0502 | 0.9921 |
| 0.0004 | 4.0 | 1916 | 0.0554 | 0.9927 |
| 0.0003 | 5.0 | 2395 | 0.0536 | 0.9933 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0 |