End of training

README.md

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+---
+license: apache-2.0
+base_model: bert-large-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: bert-large-uncased-Hate_Offensive_or_Normal_Speech
+  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. -->
+
+# bert-large-uncased-Hate_Offensive_or_Normal_Speech
+
+This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0610
+- Accuracy: 0.9853
+- Weighted f1: 0.9853
+- Weighted recall: 0.9853
+- Weighted precision: 0.9854
+- Micro f1: 0.9853
+- Micro recall: 0.9853
+- Micro precision: 0.9853
+- Macro f1: 0.9851
+- Macro recall: 0.9850
+- Macro precision: 0.9853
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------------:|:--------:|:------------:|:---------------:|:--------:|:------------:|:---------------:|
+| 0.2927        | 1.0   | 153  | 0.1163          | 0.9462   | 0.9469      | 0.9462          | 0.9512             | 0.9462   | 0.9462       | 0.9462          | 0.9429   | 0.9472       | 0.9427          |
+| 0.066         | 2.0   | 306  | 0.1119          | 0.9739   | 0.9739      | 0.9739          | 0.9741             | 0.9739   | 0.9739       | 0.9739          | 0.9729   | 0.9742       | 0.9718          |
+| 0.0267        | 3.0   | 459  | 0.0805          | 0.9821   | 0.9821      | 0.9821          | 0.9825             | 0.9821   | 0.9821       | 0.9821          | 0.9804   | 0.9815       | 0.9796          |
+| 0.0209        | 4.0   | 612  | 0.0610          | 0.9853   | 0.9853      | 0.9853          | 0.9854             | 0.9853   | 0.9853       | 0.9853          | 0.9851   | 0.9850       | 0.9853          |
+| 0.0097        | 5.0   | 765  | 0.0673          | 0.9837   | 0.9836      | 0.9837          | 0.9838             | 0.9837   | 0.9837       | 0.9837          | 0.9832   | 0.9833       | 0.9833          |
+
+
+### Framework versions
+
+- Transformers 4.34.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.6.dev0
+- Tokenizers 0.13.3

all_results.json

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+{
+    "epoch": 5.0,
+    "eval_Macro F1": 0.9850968829506508,
+    "eval_Macro Precision": 0.9852965717423573,
+    "eval_Macro Recall": 0.9850027245943126,
+    "eval_Micro F1": 0.9853181076672104,
+    "eval_Micro Precision": 0.9853181076672104,
+    "eval_Micro Recall": 0.9853181076672104,
+    "eval_Weighted F1": 0.9852885515921976,
+    "eval_Weighted Precision": 0.985358447807592,
+    "eval_Weighted Recall": 0.9853181076672104,
+    "eval_accuracy": 0.9853181076672104,
+    "eval_loss": 0.061004869639873505,
+    "eval_runtime": 8.2476,
+    "eval_samples_per_second": 74.325,
+    "eval_steps_per_second": 4.729
+}

eval_results.json

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+{
+    "epoch": 5.0,
+    "eval_Macro F1": 0.9850968829506508,
+    "eval_Macro Precision": 0.9852965717423573,
+    "eval_Macro Recall": 0.9850027245943126,
+    "eval_Micro F1": 0.9853181076672104,
+    "eval_Micro Precision": 0.9853181076672104,
+    "eval_Micro Recall": 0.9853181076672104,
+    "eval_Weighted F1": 0.9852885515921976,
+    "eval_Weighted Precision": 0.985358447807592,
+    "eval_Weighted Recall": 0.9853181076672104,
+    "eval_accuracy": 0.9853181076672104,
+    "eval_loss": 0.061004869639873505,
+    "eval_runtime": 8.2476,
+    "eval_samples_per_second": 74.325,
+    "eval_steps_per_second": 4.729
+}