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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: BERT_model_new |
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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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# BERT_model_new |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1206 |
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- F1: 0.8301 |
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## Model description |
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train_df = pd.read_csv('/content/drive/My Drive/DATASETS/wiki_toxic/train.csv')\ |
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validation_df = pd.read_csv('/content/drive/My Drive/DATASETS/wiki_toxic/validation.csv')\ |
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#test_df = pd.read_csv('/content/drive/My Drive/wiki_toxic/test.csv')\ |
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frac = 0.9\ |
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#TRAIN\ |
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print(train_df.shape[0]) # get the number of rows in the dataframe\ |
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rows_to_delete = train_df.sample(frac=frac, random_state=1)\ |
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train_df = train_df.drop(rows_to_delete.index)\ |
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print(train_df.shape[0])\ |
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#VALIDATION\ |
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print(validation_df.shape[0]) # get the number of rows in the dataframe\ |
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rows_to_delete = validation_df.sample(frac=frac, random_state=1)\ |
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validation_df = validation_df.drop(rows_to_delete.index)\ |
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print(validation_df.shape[0])\ |
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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: 2e-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: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| No log | 1.0 | 399 | 0.0940 | 0.8273 | |
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| 0.1262 | 2.0 | 798 | 0.1206 | 0.8301 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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