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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- clinc_oos |
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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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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: clinc_oos |
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type: clinc_oos |
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args: plus |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9174193548387096 |
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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 the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/08_model-compression.ipynb). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7773 |
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- Accuracy: 0.9174 |
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## Model description |
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More information needed |
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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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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 48 |
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- eval_batch_size: 48 |
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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: 5 |
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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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| 4.2923 | 1.0 | 318 | 3.2893 | 0.7423 | |
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| 2.6307 | 2.0 | 636 | 1.8837 | 0.8281 | |
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| 1.5483 | 3.0 | 954 | 1.1583 | 0.8968 | |
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| 1.0153 | 4.0 | 1272 | 0.8618 | 0.9094 | |
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| 0.7958 | 5.0 | 1590 | 0.7773 | 0.9174 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.9.1+cu102 |
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- Datasets 1.13.0 |
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- Tokenizers 0.10.3 |
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