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bsgreenb
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my_awesome_model

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

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  1. README.md +10 -8
  2. model.safetensors +1 -1
  3. runs/Jan30_01-13-27_8b09110722bf/events.out.tfevents.1706577207.8b09110722bf.851.2 +2 -2
  4. runs/Jan30_01-13-27_8b09110722bf/events.out.tfevents.1706578127.8b09110722bf.851.3 +3 -0
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.4173
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- - Accuracy: 0.8465
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- - F1: 0.8134
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- - Precision: 0.8095
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- - Recall: 0.8173
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  ## Model description
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@@ -55,9 +55,11 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 0.4196 | 1.17 | 500 | 0.4173 | 0.8465 | 0.8134 | 0.8095 | 0.8173 |
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- | 0.3043 | 2.33 | 1000 | 0.4959 | 0.8451 | 0.8145 | 0.7994 | 0.8301 |
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- | 0.2367 | 3.5 | 1500 | 0.6724 | 0.8163 | 0.7917 | 0.7389 | 0.8526 |
 
 
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  ### Framework versions
 
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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: 0.4208
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+ - Accuracy: 0.8451
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+ - F1: 0.8196
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+ - Precision: 0.8272
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+ - Recall: 0.8121
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.42 | 1.17 | 500 | 0.4208 | 0.8451 | 0.8196 | 0.8272 | 0.8121 |
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+ | 0.3049 | 2.33 | 1000 | 0.5471 | 0.8241 | 0.8080 | 0.7663 | 0.8545 |
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+ | 0.2221 | 3.5 | 1500 | 0.5101 | 0.8373 | 0.7940 | 0.8787 | 0.7242 |
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+ | 0.1545 | 4.66 | 2000 | 0.6485 | 0.8425 | 0.8101 | 0.8477 | 0.7758 |
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+ | 0.1178 | 5.83 | 2500 | 0.7347 | 0.8281 | 0.8024 | 0.7988 | 0.8061 |
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  ### Framework versions
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