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Training_Checkpoint

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

eval_loss: 0.0006
eval_accuracy: 1.0
eval_f1: 1.0
eval_runtime: 1.7344
eval_samples_per_second: 576.552
eval_steps_per_second: 36.323
epoch: 10.0
step: 630

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

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Safetensors

Model size

67M params

Tensor type

F32

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Model tree for LalasaMynalli/LalasaMynalli_First_LLM

Base model

distilbert/distilbert-base-uncased

Finetuned

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Evaluation results

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