---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
datasets:
- param-bharat/scorers-nli
pipeline_tag: text-classification
model-index:
- name: ModernBERT-base-nli-clf
  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. -->

# ModernBERT-base-nli-clf

This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0101
- F1: 0.8717
- Accuracy: 0.8717
- Precision: 0.8717
- Recall: 0.8717

## 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: 0.0003
- train_batch_size: 128
- eval_batch_size: 128
- seed: 2024
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 1024
- total_eval_batch_size: 1024
- 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: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | F1     | Accuracy | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:------:|:--------:|:---------:|:------:|
| No log        | 0      | 0     | 0.0185          | 0.5044 | 0.5297   | 0.5418    | 0.5297 |
| 0.0135        | 0.4999 | 6630  | 0.0150          | 0.7539 | 0.755    | 0.7582    | 0.755  |
| 0.0108        | 0.9998 | 13260 | 0.0108          | 0.8539 | 0.8539   | 0.8540    | 0.8539 |
| 0.0109        | 1.4998 | 19890 | 0.0113          | 0.8492 | 0.8493   | 0.8496    | 0.8493 |
| 0.0103        | 1.9997 | 26520 | 0.0103          | 0.8641 | 0.8641   | 0.8641    | 0.8641 |
| 0.0099        | 2.4996 | 33150 | 0.0109          | 0.8575 | 0.8579   | 0.8630    | 0.8579 |
| 0.0095        | 2.9995 | 39780 | 0.0103          | 0.8686 | 0.8686   | 0.8686    | 0.8686 |
| 0.0092        | 3.4995 | 46410 | 0.0101          | 0.8700 | 0.87     | 0.8700    | 0.87   |
| 0.0094        | 3.9994 | 53040 | 0.0097          | 0.8751 | 0.8751   | 0.8751    | 0.8751 |
| 0.0095        | 4.4993 | 59670 | 0.0105          | 0.8664 | 0.8664   | 0.8664    | 0.8664 |
| 0.0086        | 4.9992 | 66300 | 0.0101          | 0.8717 | 0.8717   | 0.8717    | 0.8717 |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0