Model save

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-normal-speech-lr-1e-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. -->
+
+# bert-large-uncased-hate-offensive-normal-speech-lr-1e-05
+
+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: 0.1013
+- Accuracy: 0.9739
+- Weighted f1: 0.9740
+- Weighted recall: 0.9739
+- Weighted precision: 0.9741
+- Micro f1: 0.9739
+- Micro recall: 0.9739
+- Micro precision: 0.9739
+- Macro f1: 0.9720
+- Macro recall: 0.9719
+- Macro precision: 0.9723
+
+## 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: 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------------:|:--------:|:------------:|:---------------:|:--------:|:------------:|:---------------:|
+| 0.9139        | 1.0   | 153  | 0.4854          | 0.8111   | 0.8086      | 0.8111          | 0.8125             | 0.8111   | 0.8111       | 0.8111          | 0.7947   | 0.7932       | 0.8030          |
+| 0.2278        | 2.0   | 306  | 0.0775          | 0.9674   | 0.9675      | 0.9674          | 0.9683             | 0.9674   | 0.9674       | 0.9674          | 0.9652   | 0.9657       | 0.9655          |
+| 0.0582        | 3.0   | 459  | 0.1007          | 0.9772   | 0.9773      | 0.9772          | 0.9782             | 0.9772   | 0.9772       | 0.9772          | 0.9757   | 0.9754       | 0.9769          |
+| 0.0228        | 4.0   | 612  | 0.0727          | 0.9772   | 0.9772      | 0.9772          | 0.9773             | 0.9772   | 0.9772       | 0.9772          | 0.9756   | 0.9755       | 0.9757          |
+| 0.0159        | 5.0   | 765  | 0.1013          | 0.9739   | 0.9740      | 0.9739          | 0.9741             | 0.9739   | 0.9739       | 0.9739          | 0.9720   | 0.9719       | 0.9723          |
+
+
+### Framework versions
+
+- Transformers 4.34.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.6.dev0
+- Tokenizers 0.13.3

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