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F_Roberta_classifier2

Text Classification
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roberta
Generated from Trainer
text-embeddings-inference
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: F_Roberta_classifier2
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # F_Roberta_classifier2
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+
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+ This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1317
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+ - Accuracy: 0.9751
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+ - F1: 0.9751
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+ - Precision: 0.9751
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+ - Recall: 0.9751
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+ - C Report: precision recall f1-score support
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+
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+ 0 0.97 0.98 0.98 1467
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+ 1 0.98 0.97 0.98 1466
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+
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+ accuracy 0.98 2933
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+ macro avg 0.98 0.98 0.98 2933
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+ weighted avg 0.98 0.98 0.98 2933
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+
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+ - C Matrix: None
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 5
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | C Report | C Matrix |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
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+ | 0.1626 | 1.0 | 614 | 0.0936 | 0.9707 | 0.9707 | 0.9707 | 0.9707 | precision recall f1-score support
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+
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+ 0 0.97 0.97 0.97 1467
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+ 1 0.97 0.97 0.97 1466
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+
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+ accuracy 0.97 2933
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+ macro avg 0.97 0.97 0.97 2933
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+ weighted avg 0.97 0.97 0.97 2933
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+ | None |
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+ | 0.0827 | 2.0 | 1228 | 0.0794 | 0.9731 | 0.9731 | 0.9731 | 0.9731 | precision recall f1-score support
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+
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+ 0 0.96 0.98 0.97 1467
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+ 1 0.98 0.96 0.97 1466
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+
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+ accuracy 0.97 2933
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+ macro avg 0.97 0.97 0.97 2933
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+ weighted avg 0.97 0.97 0.97 2933
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+ | None |
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+ | 0.0525 | 3.0 | 1842 | 0.1003 | 0.9737 | 0.9737 | 0.9737 | 0.9737 | precision recall f1-score support
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+
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+ 0 0.97 0.98 0.97 1467
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+ 1 0.98 0.97 0.97 1466
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+
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+ accuracy 0.97 2933
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+ macro avg 0.97 0.97 0.97 2933
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+ weighted avg 0.97 0.97 0.97 2933
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+ | None |
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+ | 0.0329 | 4.0 | 2456 | 0.1184 | 0.9751 | 0.9751 | 0.9751 | 0.9751 | precision recall f1-score support
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+
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+ 0 0.98 0.97 0.98 1467
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+ 1 0.97 0.98 0.98 1466
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+
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+ accuracy 0.98 2933
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+ macro avg 0.98 0.98 0.98 2933
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+ weighted avg 0.98 0.98 0.98 2933
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+ | None |
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+ | 0.0179 | 5.0 | 3070 | 0.1317 | 0.9751 | 0.9751 | 0.9751 | 0.9751 | precision recall f1-score support
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+
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+ 0 0.97 0.98 0.98 1467
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+ 1 0.98 0.97 0.98 1466
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+
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+ accuracy 0.98 2933
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+ macro avg 0.98 0.98 0.98 2933
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+ weighted avg 0.98 0.98 0.98 2933
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+ | None |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.18.0
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+ - Pytorch 1.11.0+cu113
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+ - Datasets 2.2.0
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+ - Tokenizers 0.12.1