bert-base-uncased_text_detection

hmcgovern/bert-base-uncased_text_detection

bert-base-uncased_text_detection is a machine learning model from hmcgovern. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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Public

Updated Sep 1, 2023

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7.1 MB

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Hugging Face

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At a glance

License: apache-2.0
Access: Public
Created: Sep 1, 2023
Updated: Sep 1, 2023
SHA: 68fc6813

Base models

Model card

From the Hugging Face model README

bert-base-uncased_text_detection

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

Loss: 0.0825
Accuracy: 0.9744

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: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3842	1.0	96	0.3456	0.8564
0.123	1.99	192	0.1073	0.9634
0.0869	2.99	288	0.0825	0.9744

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3

License: apache-2.0
Created: Sep 1, 2023
Updated: Sep 1, 2023