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MENG21
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results_bert-base-uncased

Text Classification
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Safetensors
bert
Generated from Trainer
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End of training

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  1. README.md +22 -8
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -5,6 +5,9 @@ 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: results_bert-base-uncased
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  results: []
@@ -17,8 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3483
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- - Accuracy: 0.9323
 
 
 
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  ## Model description
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@@ -43,15 +49,23 @@ The following hyperparameters were used during training:
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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: 3
 
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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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- | 0.2393 | 1.0 | 533 | 0.1818 | 0.9242 |
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- | 0.106 | 2.0 | 1066 | 0.2154 | 0.9266 |
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- | 0.035 | 3.0 | 1599 | 0.3483 | 0.9323 |
 
 
 
 
 
 
 
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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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+ - precision
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+ - recall
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+ - f1
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  model-index:
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  - name: results_bert-base-uncased
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  results: []
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1844
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+ - Accuracy: 0.9301
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+ - Precision: 0.9366
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+ - Recall: 0.9489
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+ - F1: 0.9427
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  ## Model description
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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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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 1
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.5556 | 0.09 | 50 | 0.6183 | 0.8038 | 0.7584 | 0.9921 | 0.8596 |
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+ | 0.29 | 0.19 | 100 | 0.2173 | 0.9086 | 0.9199 | 0.9301 | 0.9250 |
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+ | 0.2393 | 0.28 | 150 | 0.2499 | 0.8975 | 0.8681 | 0.9796 | 0.9205 |
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+ | 0.2177 | 0.38 | 200 | 0.2091 | 0.9156 | 0.9286 | 0.9324 | 0.9305 |
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+ | 0.2202 | 0.47 | 250 | 0.2516 | 0.9062 | 0.8891 | 0.9654 | 0.9257 |
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+ | 0.2059 | 0.56 | 300 | 0.2358 | 0.9153 | 0.9125 | 0.9514 | 0.9315 |
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+ | 0.2158 | 0.66 | 350 | 0.1828 | 0.9255 | 0.9339 | 0.9437 | 0.9388 |
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+ | 0.1997 | 0.75 | 400 | 0.1901 | 0.9263 | 0.9310 | 0.9486 | 0.9397 |
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+ | 0.1713 | 0.84 | 450 | 0.1830 | 0.9290 | 0.9376 | 0.9457 | 0.9417 |
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+ | 0.1569 | 0.94 | 500 | 0.1844 | 0.9301 | 0.9366 | 0.9489 | 0.9427 |
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  ### Framework versions
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