metadata

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: []

ModernBERT-base-nli-clf

This model is a fine-tuned version of 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