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sentinel-tinybert-classifier-v1

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README.md ADDED
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+ ---
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+ library_name: transformers
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+ base_model: huawei-noah/TinyBERT_General_4L_312D
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+ tags:
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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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+ model-index:
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+ - name: sentinel-weighted-v1
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+ results: []
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+ ---
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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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+
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+ # sentinel-weighted-v1
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+
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+ This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4042
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+ - F1 Binary: 0.7028
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+ - Accuracy: 0.8167
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+ - Precision: 0.5907
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+ - Recall: 0.8674
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+ - Roc Auc: 0.9064
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-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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+ - optimizer: Use 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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 Binary | Accuracy | Precision | Recall | Roc Auc |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|:---------:|:------:|:-------:|
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+ | 0.3796 | 1.0 | 4319 | 0.4003 | 0.7119 | 0.8290 | 0.6146 | 0.8457 | 0.9038 |
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+ | 0.3024 | 2.0 | 8638 | 0.4042 | 0.7028 | 0.8167 | 0.5907 | 0.8674 | 0.9064 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.57.3
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+ - Pytorch 2.9.1+cpu
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+ - Datasets 4.4.2
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+ - Tokenizers 0.22.1
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+ "BertForSequenceClassification"
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+ "hidden_act": "gelu",
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+ "initializer_range": 0.02,
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+ "intermediate_size": 1200,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 4,
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+ "position_embedding_type": "absolute",
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+ "pre_trained": "",
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+ "problem_type": "single_label_classification",
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+ "structure": [],
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+ "transformers_version": "4.57.3",
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+ "use_cache": true,
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+ }
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