stud-fac-eval-bert-large-uncased_v2

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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.

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• Model type: Text Classification
• Language(s) (NLP): English/Tagalog
• License: Apache License
• Finetuned from model [optional]: BERT (Large)

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Training Details

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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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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

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0+cu121
• Datasets 2.17.0
• Tokenizers 0.15.2

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Summary

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Environmental Impact

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

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