Model save

README.md

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+---
+base_model: GroNLP/hateBERT
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: hateBERT-hate-offensive-normal-speech-lr-2e-05
+  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. -->
+
+# hateBERT-hate-offensive-normal-speech-lr-2e-05
+
+This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0207
+- Accuracy: 0.9902
+- Weighted f1: 0.9902
+- Weighted recall: 0.9902
+- Weighted precision: 0.9904
+- Micro f1: 0.9902
+- Micro recall: 0.9902
+- Micro precision: 0.9902
+- Macro f1: 0.9901
+- Macro recall: 0.9903
+- Macro precision: 0.9899
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Weighted recall | Weighted precision | Micro f1 | Micro recall | Micro precision | Macro f1 | Macro recall | Macro precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------------:|:--------:|:------------:|:---------------:|:--------:|:------------:|:---------------:|
+| 0.6155        | 1.0   | 153  | 0.0889          | 0.9805   | 0.9805      | 0.9805          | 0.9806             | 0.9805   | 0.9805       | 0.9805          | 0.9801   | 0.9811       | 0.9793          |
+| 0.0665        | 2.0   | 306  | 0.0368          | 0.9870   | 0.9870      | 0.9870          | 0.9870             | 0.9870   | 0.9870       | 0.9870          | 0.9864   | 0.9866       | 0.9864          |
+| 0.0235        | 3.0   | 459  | 0.0264          | 0.9902   | 0.9902      | 0.9902          | 0.9904             | 0.9902   | 0.9902       | 0.9902          | 0.9901   | 0.9903       | 0.9899          |
+| 0.0182        | 4.0   | 612  | 0.0414          | 0.9870   | 0.9870      | 0.9870          | 0.9873             | 0.9870   | 0.9870       | 0.9870          | 0.9865   | 0.9869       | 0.9864          |
+| 0.012         | 5.0   | 765  | 0.0207          | 0.9902   | 0.9902      | 0.9902          | 0.9904             | 0.9902   | 0.9902       | 0.9902          | 0.9901   | 0.9903       | 0.9899          |
+
+
+### Framework versions
+
+- Transformers 4.34.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.6.dev0
+- Tokenizers 0.13.3