---
library_name: transformers
base_model: ModernBERT-base
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: moe_True_addTokens_False_clipLoss_False_cv_2
  results: []
---

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# moe_True_addTokens_False_clipLoss_False_cv_2

This model is a fine-tuned version of [ModernBERT-base](https://huggingface.co./ModernBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 332.7107
- Model Preparation Time: 0.0049
- F1: 0.0042
- Precision: 0.0025
- Recall: 0.0121
- Threshold: 1.0
- Sim Ratio: 1.0011
- Pos Sim: 1.0
- Neg Sim: 0.9989

## 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: 16
- eval_batch_size: 16
- 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | F1     | Precision | Recall | Threshold | Sim Ratio | Pos Sim | Neg Sim |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:---------:|:------:|:---------:|:---------:|:-------:|:-------:|
| 332.7109      | 0.8   | 5000 | 332.7107        | 0.0049                 | 0.0038 | 0.0023    | 0.0109 | 1.0       | 1.001     | 1.0     | 0.999   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0