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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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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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model-index: |
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- name: distilbert-base-uncased-finetuned-clinc |
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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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# distilbert-base-uncased-finetuned-clinc |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7761 |
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- Accuracy: 0.9174 |
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## Model description |
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This is an initial example of knowledge-distillation where the student loss is all cross-entropy loss \\(L_{CE}\\) of the ground-truth labels and none of the knowledge-distillation loss \\(L_{KD}\\). |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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The training and evaluation data come straight from the `train` and `validation` splits in the clinc_oos dataset, respectively; and tokenized using the `distilbert-base-uncased` tokenization. |
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## Training procedure |
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Please see page 224 in Chapter 8: Making Transformers Efficient in Production, Natural Language Processing with Transformers, May 2022. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- num_epochs: 5 |
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- alpha: 1.0 |
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- temperature: 2.0 |
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- learning_rate: 2e-05 |
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- train_batch_size: 48 |
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- eval_batch_size: 48 |
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- seed: 8675309 |
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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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### 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 | 318 | 3.2998 | 0.7132 | |
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| 3.7996 | 2.0 | 636 | 1.8739 | 0.8390 | |
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| 3.7996 | 3.0 | 954 | 1.1564 | 0.8903 | |
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| 1.689 | 4.0 | 1272 | 0.8571 | 0.9126 | |
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| 0.9017 | 5.0 | 1590 | 0.7761 | 0.9174 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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