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Appearance-classifier

Text Classification
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bert
Generated from Trainer
text-embeddings-inference
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End of training

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  1. README.md +13 -9
  2. model.safetensors +1 -1
  3. runs/Apr06_16-34-35_ca82b86b8eeb/events.out.tfevents.1712421284.ca82b86b8eeb.34.0 +2 -2
README.md CHANGED
@@ -5,6 +5,8 @@ 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: Appearance-classifier
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  results: []
@@ -17,8 +19,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/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: 0.6931
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- - Accuracy: 0.51
 
 
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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- - train_batch_size: 8
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- - eval_batch_size: 8
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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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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.7013 | 1.0 | 2375 | 0.6953 | 0.49 |
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- | 0.6954 | 2.0 | 4750 | 0.6944 | 0.51 |
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- | 0.6961 | 3.0 | 7125 | 0.6931 | 0.51 |
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  ### Framework versions
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
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+ - recall
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+ - f1
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  model-index:
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  - name: Appearance-classifier
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  results: []
 
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  This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/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: 0.5101
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+ - Accuracy: 0.7815
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+ - Recall: 0.7846
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+ - F1: 0.7855
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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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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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|
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+ | 0.509 | 1.0 | 1188 | 0.5271 | 0.7529 | 0.8525 | 0.7787 |
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+ | 0.4275 | 2.0 | 2376 | 0.5044 | 0.7763 | 0.8239 | 0.7898 |
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+ | 0.3211 | 3.0 | 3564 | 0.5101 | 0.7815 | 0.7846 | 0.7855 |
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  ### Framework versions
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