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lengocquangLAB
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Taste-classifier

Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
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End of training

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Files changed (4) hide show
  1. README.md +8 -12
  2. model.safetensors +1 -1
  3. runs/Apr23_13-13-33_ad9f05158312/events.out.tfevents.1713878024.ad9f05158312.34.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -4,9 +4,7 @@ base_model: google-bert/bert-base-uncased
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  tags:
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  - generated_from_trainer
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  metrics:
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- - precision
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- - recall
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- - f1
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  model-index:
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  - name: Taste-classifier
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  results: []
@@ -19,10 +17,8 @@ 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: 0.5559
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- - Precision: 0.8683
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- - Recall: 0.8149
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- - F1: 0.8407
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  ## Model description
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@@ -51,11 +47,11 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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- |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
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- | 0.5082 | 1.0 | 2375 | 0.4966 | 0.8112 | 0.8388 | 0.8247 |
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- | 0.3802 | 2.0 | 4750 | 0.4398 | 0.8495 | 0.8163 | 0.8326 |
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- | 0.3543 | 3.0 | 7125 | 0.5559 | 0.8683 | 0.8149 | 0.8407 |
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  ### Framework versions
 
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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: Taste-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: 0.5014
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+ - Accuracy: 0.896
 
 
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  ## Model description
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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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+ | 0.3257 | 1.0 | 2500 | 0.3579 | 0.8688 |
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+ | 0.2355 | 2.0 | 5000 | 0.3739 | 0.894 |
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+ | 0.11 | 3.0 | 7500 | 0.5014 | 0.896 |
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  ### Framework versions
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