---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
model-index:
- name: eternis_router_encoder_sft_10Sep
  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. -->

# eternis_router_encoder_sft_10Sep

This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6852
- Complexity Accuracy: 0.772
- Model Accuracy: 0.747
- Overall Accuracy: 0.5793

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Complexity Accuracy | Model Accuracy | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:-------------------:|:--------------:|:----------------:|
| 0.8284        | 0.6857 | 300  | 0.7391          | 0.7275              | 0.7475         | 0.5437           |
| 0.7657        | 1.3703 | 600  | 0.7173          | 0.7408              | 0.7478         | 0.5515           |
| 0.7398        | 2.0549 | 900  | 0.7099          | 0.7502              | 0.748          | 0.5595           |
| 0.7161        | 2.7406 | 1200 | 0.7037          | 0.7578              | 0.748          | 0.5645           |
| 0.7057        | 3.4251 | 1500 | 0.6973          | 0.7635              | 0.7468         | 0.569            |
| 0.7115        | 4.1097 | 1800 | 0.6927          | 0.764               | 0.748          | 0.5705           |
| 0.7214        | 4.7954 | 2100 | 0.6896          | 0.7672              | 0.7482         | 0.5755           |
| 0.7034        | 5.48   | 2400 | 0.6886          | 0.769               | 0.7472         | 0.5777           |
| 0.6935        | 6.1646 | 2700 | 0.6878          | 0.769               | 0.7478         | 0.577            |
| 0.7055        | 6.8503 | 3000 | 0.6867          | 0.7722              | 0.7465         | 0.5787           |
| 0.6983        | 7.5349 | 3300 | 0.6858          | 0.7728              | 0.7465         | 0.5797           |
| 0.7092        | 8.2194 | 3600 | 0.6849          | 0.774               | 0.747          | 0.5803           |
| 0.697         | 8.9051 | 3900 | 0.6851          | 0.7718              | 0.747          | 0.5787           |
| 0.6989        | 9.5897 | 4200 | 0.6852          | 0.772               | 0.747          | 0.5793           |


### Framework versions

- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0