Training complete
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README.md
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---
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license: apache-2.0
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base_model: distilbert-base-uncased
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: bert-goemotions-15epochs-run2
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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-goemotions-15epochs-run2
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1106
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- Accuracy Thresh: 0.9619
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- F1 weighted: {'f1': 0.3982045872720165}
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- F1 macro: {'f1': 0.31538372135978}
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- Accuracy: {'accuracy': 0.4170647653000594}
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- Recall weighted: {'recall': 0.4170647653000594}
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- Recall macro: {'recall': 0.32808068442725}
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## Model description
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More information needed
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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: 15
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy Thresh | F1 weighted | F1 macro | Accuracy | Recall weighted | Recall macro |
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|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:---------------------------:|:---------------------------:|:---------------------------------:|:-------------------------------:|:-------------------------------:|
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| 0.1259 | 1.0 | 5286 | 0.1127 | 0.9617 | {'f1': 0.3890983386624071} | {'f1': 0.3007761920553838} | {'accuracy': 0.41630421865715983} | {'recall': 0.41630421865715983} | {'recall': 0.3213896867494273} |
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| 0.1102 | 2.0 | 10572 | 0.1106 | 0.9619 | {'f1': 0.3982045872720165} | {'f1': 0.31538372135978} | {'accuracy': 0.4170647653000594} | {'recall': 0.4170647653000594} | {'recall': 0.32808068442725} |
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| 0.1052 | 3.0 | 15858 | 0.1107 | 0.9619 | {'f1': 0.3980887152485667} | {'f1': 0.3181332487636058} | {'accuracy': 0.4168983957219251} | {'recall': 0.4168983957219251} | {'recall': 0.32790458379700677} |
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| 0.1008 | 4.0 | 21144 | 0.1117 | 0.9616 | {'f1': 0.39966069827702533} | {'f1': 0.32238147844285014} | {'accuracy': 0.4169459298871064} | {'recall': 0.4169459298871064} | {'recall': 0.3280426755048108} |
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| 0.0968 | 5.0 | 26430 | 0.1138 | 0.9609 | {'f1': 0.39833587917024693} | {'f1': 0.32459673497912495} | {'accuracy': 0.4110516934046346} | {'recall': 0.4110516934046346} | {'recall': 0.3347612567297387} |
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| 0.0934 | 6.0 | 31716 | 0.1158 | 0.9604 | {'f1': 0.3893454681480969} | {'f1': 0.3185494353334119} | {'accuracy': 0.39786096256684494} | {'recall': 0.39786096256684494} | {'recall': 0.3332642189610775} |
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| 0.0902 | 7.0 | 37002 | 0.1188 | 0.9596 | {'f1': 0.3843621716402447} | {'f1': 0.3127316237002698} | {'accuracy': 0.39332144979203804} | {'recall': 0.39332144979203804} | {'recall': 0.32900959748461356} |
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### Framework versions
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- Transformers 4.35.1
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- Pytorch 2.1.0+cu118
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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model.safetensors
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runs/Nov15_02-07-47_89bfac40a02e/events.out.tfevents.1700016043.89bfac40a02e.1456.2
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