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MENG21
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results_bert-large-uncased

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Inference Endpoints
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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-large-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-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2574
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- - Accuracy: 0.9341
 
 
 
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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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- | 0.2901 | 1.0 | 533 | 0.2233 | 0.9133 |
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- | 0.1643 | 2.0 | 1066 | 0.2325 | 0.9305 |
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- | 0.0781 | 3.0 | 1599 | 0.2574 | 0.9341 |
 
 
 
 
 
 
 
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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-large-uncased
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  results: []
 
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  This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1901
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+ - Accuracy: 0.9225
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+ - Precision: 0.9173
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+ - Recall: 0.9584
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+ - F1: 0.9374
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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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+ | 0.5079 | 0.09 | 50 | 0.3973 | 0.8712 | 0.8304 | 0.9894 | 0.9030 |
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+ | 0.3187 | 0.19 | 100 | 0.2505 | 0.9014 | 0.8936 | 0.9502 | 0.9211 |
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+ | 0.2907 | 0.28 | 150 | 0.3105 | 0.8875 | 0.8547 | 0.9810 | 0.9135 |
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+ | 0.2443 | 0.38 | 200 | 0.2255 | 0.8968 | 0.9249 | 0.9029 | 0.9138 |
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+ | 0.2412 | 0.47 | 250 | 0.2324 | 0.8963 | 0.8918 | 0.9432 | 0.9168 |
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+ | 0.2238 | 0.56 | 300 | 0.2242 | 0.9070 | 0.8940 | 0.9602 | 0.9259 |
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+ | 0.2336 | 0.66 | 350 | 0.2014 | 0.9183 | 0.9266 | 0.9396 | 0.9330 |
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+ | 0.2264 | 0.75 | 400 | 0.1961 | 0.9196 | 0.9153 | 0.9557 | 0.9350 |
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+ | 0.1893 | 0.84 | 450 | 0.1958 | 0.9215 | 0.9177 | 0.9561 | 0.9365 |
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+ | 0.1796 | 0.94 | 500 | 0.1901 | 0.9225 | 0.9173 | 0.9584 | 0.9374 |
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  ### Framework versions
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