| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: mdeberta-hate-final |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # mdeberta-hate-final |
|
|
| This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6223 |
| - Accuracy: 0.7424 |
| - Precision: 0.7410 |
| - Recall: 0.7424 |
| - F1: 0.7363 |
|
|
| ## 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: 1e-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 |
| - num_epochs: 6 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | No log | 1.0 | 296 | 0.5309 | 0.7519 | 0.7685 | 0.7519 | 0.7357 | |
| | 0.5358 | 2.0 | 592 | 0.5228 | 0.7510 | 0.7663 | 0.7510 | 0.7351 | |
| | 0.5358 | 3.0 | 888 | 0.5565 | 0.7510 | 0.7513 | 0.7510 | 0.7438 | |
| | 0.4295 | 4.0 | 1184 | 0.5639 | 0.7481 | 0.7488 | 0.7481 | 0.7403 | |
| | 0.4295 | 5.0 | 1480 | 0.5941 | 0.7510 | 0.7531 | 0.7510 | 0.7423 | |
| | 0.3701 | 6.0 | 1776 | 0.6223 | 0.7424 | 0.7410 | 0.7424 | 0.7363 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.24.0.dev0 |
| - Pytorch 1.11.0+cu102 |
| - Datasets 2.6.1 |
| - Tokenizers 0.13.1 |
|
|
