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update model card README.md
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README.md
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---
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license: mit
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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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- precision
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- recall
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- f1
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model-index:
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- name: hasoc19-microsoft-mdeberta-v3-base-targinsult1
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results: []
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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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# hasoc19-microsoft-mdeberta-v3-base-targinsult1
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9030
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- Accuracy: 0.6958
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- Precision: 0.6536
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- Recall: 0.6464
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- F1: 0.6493
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| No log | 1.0 | 263 | 0.5647 | 0.6858 | 0.6528 | 0.6609 | 0.6556 |
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| 0.5745 | 2.0 | 526 | 0.5520 | 0.6987 | 0.6612 | 0.6626 | 0.6619 |
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| 0.5745 | 3.0 | 789 | 0.6162 | 0.7139 | 0.6764 | 0.6733 | 0.6747 |
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| 0.4908 | 4.0 | 1052 | 0.6262 | 0.7063 | 0.6698 | 0.6715 | 0.6706 |
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| 0.4908 | 5.0 | 1315 | 0.6799 | 0.7067 | 0.6661 | 0.6568 | 0.6604 |
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| 0.4069 | 6.0 | 1578 | 0.7646 | 0.7044 | 0.6635 | 0.6550 | 0.6584 |
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| 0.4069 | 7.0 | 1841 | 0.7791 | 0.7029 | 0.6646 | 0.6633 | 0.6639 |
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| 0.338 | 8.0 | 2104 | 0.8646 | 0.7029 | 0.6623 | 0.6554 | 0.6582 |
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| 0.338 | 9.0 | 2367 | 0.9417 | 0.7006 | 0.6556 | 0.6317 | 0.6374 |
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| 0.2847 | 10.0 | 2630 | 0.9030 | 0.6958 | 0.6536 | 0.6464 | 0.6493 |
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### Framework versions
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- Transformers 4.24.0.dev0
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- Pytorch 1.11.0+cu102
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- Datasets 2.6.1
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- Tokenizers 0.13.1
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