bert-finetuned-requirements

RodP/bert-finetuned-requirements

bert-finetuned-requirements is a token classification model from RodP. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Jan 9, 2023
Updated: Jan 15, 2023
SHA: 03988ea2

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Model card

From the Hugging Face model README

bert-finetuned-requirements

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

Loss: 0.0538
Precision: 0.9609
Recall: 0.9609
F1: 0.9609
Accuracy: 0.9822

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: 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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	33	0.1992	0.8903	0.8880	0.8892	0.9476
No log	2.0	66	0.1351	0.9039	0.9062	0.9051	0.9554
No log	3.0	99	0.0949	0.9368	0.9271	0.9319	0.9677
No log	4.0	132	0.0613	0.9556	0.9531	0.9544	0.9788
No log	5.0	165	0.0538	0.9609	0.9609	0.9609	0.9822

Framework versions

Transformers 4.25.1
Pytorch 1.13.1+cu116
Datasets 2.8.0
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Jan 9, 2023
Updated: Jan 15, 2023