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office-character

Text Classification
Transformers
PyTorch
Safetensors
distilbert
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +20 -15
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -17,8 +17,8 @@ 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 the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.3281
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- - Accuracy: 0.339
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  ## Model description
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@@ -37,29 +37,34 @@ More information needed
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  ### Training hyperparameters
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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: 4
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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: 1.0
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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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- | No log | 0.09 | 100 | 2.5032 | 0.3117 |
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- | No log | 0.18 | 200 | 2.4405 | 0.315 |
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- | No log | 0.27 | 300 | 2.4370 | 0.319 |
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- | No log | 0.36 | 400 | 2.4321 | 0.322 |
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- | 2.5244 | 0.45 | 500 | 2.3951 | 0.3087 |
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- | 2.5244 | 0.54 | 600 | 2.3907 | 0.3207 |
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- | 2.5244 | 0.63 | 700 | 2.3612 | 0.321 |
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- | 2.5244 | 0.72 | 800 | 2.3521 | 0.3293 |
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- | 2.5244 | 0.81 | 900 | 2.3339 | 0.3327 |
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- | 2.417 | 0.9 | 1000 | 2.3318 | 0.3323 |
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- | 2.417 | 0.99 | 1100 | 2.3281 | 0.339 |
 
 
 
 
 
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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 the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.3478
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+ - Accuracy: 0.3247
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 3.3e-06
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  - train_batch_size: 8
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  - eval_batch_size: 4
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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: 3.0
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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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+ | 2.4973 | 0.18 | 200 | 2.4079 | 0.3173 |
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+ | 2.4484 | 0.36 | 400 | 2.4126 | 0.3077 |
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+ | 2.398 | 0.54 | 600 | 2.3910 | 0.314 |
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+ | 2.4151 | 0.72 | 800 | 2.3812 | 0.317 |
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+ | 2.3982 | 0.9 | 1000 | 2.3672 | 0.327 |
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+ | 2.3402 | 1.08 | 1200 | 2.3622 | 0.3263 |
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+ | 2.3362 | 1.26 | 1400 | 2.3591 | 0.3253 |
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+ | 2.3865 | 1.43 | 1600 | 2.3641 | 0.3177 |
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+ | 2.3623 | 1.61 | 1800 | 2.3553 | 0.3253 |
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+ | 2.3528 | 1.79 | 2000 | 2.3576 | 0.3213 |
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+ | 2.3225 | 1.97 | 2200 | 2.3488 | 0.3257 |
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+ | 2.342 | 2.15 | 2400 | 2.3486 | 0.326 |
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+ | 2.3279 | 2.33 | 2600 | 2.3588 | 0.3197 |
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+ | 2.3177 | 2.51 | 2800 | 2.3472 | 0.3217 |
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+ | 2.3509 | 2.69 | 3000 | 2.3483 | 0.3273 |
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+ | 2.325 | 2.87 | 3200 | 2.3478 | 0.3247 |
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  ### Framework versions
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