distilbert-base-uncased-Distilbert-Model – AI Model by petermutwiri | AlphaNeural AI

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petermutwiri/
distilbert-base-uncased-Distilbert-Model

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased-Distilbert-Model

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7456
F1: 0.7245

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.8394	0.5	500	0.8400	0.6557
0.7944	1.0	1000	0.7827	0.6959
0.7253	1.5	1500	0.7619	0.6763
0.6838	2.01	2000	0.7456	0.7245
0.5503	2.51	2500	0.7754	0.7117
0.5517	3.01	3000	0.7592	0.7199
0.4178	3.51	3500	0.9776	0.7145
0.4225	4.01	4000	1.1820	0.7055
0.3327	4.51	4500	1.3212	0.7038
0.3292	5.02	5000	1.4429	0.7057
0.2563	5.52	5500	1.5018	0.7049
0.2555	6.02	6000	1.4722	0.7025
0.1745	6.52	6500	1.7254	0.7035
0.1813	7.02	7000	1.7894	0.6957
0.1473	7.52	7500	1.9552	0.6947
0.1381	8.02	8000	1.9619	0.7022
0.1003	8.53	8500	2.0648	0.6982
0.0916	9.03	9000	2.1473	0.6945
0.0779	9.53	9500	2.1930	0.6947

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3