distilbert-base-uncased-finetuned-NSD – AI Model by PriyankV | AlphaNeural AI

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PriyankV/
distilbert-base-uncased-finetuned-NSD

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token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased-finetuned-NSD

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

Loss: 0.1901
Precision: 0.7716
Recall: 0.8081
F1: 0.7894
Accuracy: 0.9455

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: 114
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: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	62	0.2133	0.6771	0.7336	0.7042	0.9295
No log	2.0	124	0.1959	0.7095	0.7720	0.7395	0.9341
No log	3.0	186	0.1785	0.7370	0.7968	0.7657	0.9406
No log	4.0	248	0.1846	0.7618	0.8014	0.7811	0.9448
No log	5.0	310	0.1901	0.7716	0.8081	0.7894	0.9455

Framework versions

Transformers 4.48.1
Pytorch 2.5.1
Datasets 3.2.0
Tokenizers 0.21.0