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Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Allan O. Ibo, Jr.
Model type: Text Classification
Language(s) (NLP): English/Tagalog
License: Apache License
Finetuned from model [optional]: BERT (Large)

Model Sources [optional]

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Demo [optional]: User Guide

Uses

Direct Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

results_bert-large-uncased

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

Loss: 0.2128
Accuracy: 0.9141
Precision: 0.9182
Recall: 0.9421
F1: 0.9300

Model description

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Intended uses & limitations

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Training and evaluation data

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6415	0.09	50	0.5315	0.7175	0.6981	0.9394	0.8010
0.4007	0.18	100	0.7702	0.7243	0.9892	0.5505	0.7074
0.5158	0.28	150	0.4075	0.8591	0.8904	0.8748	0.8825
0.3934	0.37	200	0.2809	0.8763	0.9354	0.8546	0.8932
0.2691	0.46	250	0.3406	0.8832	0.8837	0.9294	0.9060
0.2814	0.55	300	0.2582	0.8768	0.8512	0.9651	0.9046
0.2735	0.64	350	0.2715	0.8953	0.8708	0.9711	0.9182
0.2411	0.74	400	0.2389	0.9103	0.9242	0.9279	0.9260
0.2371	0.83	450	0.2081	0.9104	0.9212	0.9316	0.9264
0.1974	0.92	500	0.2128	0.9141	0.9182	0.9421	0.9300

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Software

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