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Basic-Spam-Smish-Classifier

Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
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Basic-Spam-Smish-Classifier

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  1. README.md +14 -11
  2. model.safetensors +1 -1
README.md CHANGED
@@ -7,19 +7,19 @@ tags:
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  metrics:
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  - accuracy
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  model-index:
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- - name: base_model
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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- # base_model
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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: 0.0677
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- - Accuracy: 0.9970
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  ## Model description
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@@ -39,23 +39,26 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - distributed_type: multi-GPU
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  - num_devices: 4
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- - total_train_batch_size: 64
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- - total_eval_batch_size: 64
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 2
 
 
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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 | 1.0 | 125 | 0.0837 | 0.9945 |
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- | No log | 2.0 | 250 | 0.0677 | 0.9970 |
 
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  ### Framework versions
 
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  metrics:
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  - accuracy
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  model-index:
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+ - name: Basic-Spam-Smish-Classifier
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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+ # Basic-Spam-Smish-Classifier
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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: 0.0760
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+ - Accuracy: 0.9965
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 64
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  - seed: 42
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  - distributed_type: multi-GPU
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  - num_devices: 4
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+ - total_train_batch_size: 128
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+ - total_eval_batch_size: 256
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 0.06
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+ - num_epochs: 3
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+ - mixed_precision_training: Native AMP
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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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+ | 1.2331 | 1.0 | 63 | 0.1649 | 0.9890 |
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+ | 0.1547 | 2.0 | 126 | 0.0875 | 0.9955 |
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+ | 0.0621 | 3.0 | 189 | 0.0760 | 0.9965 |
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  ### Framework versions
model.safetensors CHANGED
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