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--- |
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license: apache-2.0 |
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base_model: distilbert-base-multilingual-cased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- swiss_law_area_prediction |
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metrics: |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: modello_finetuning1 |
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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: swiss_law_area_prediction |
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type: swiss_law_area_prediction |
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config: main |
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split: validation |
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args: main |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9922018189992046 |
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- name: Recall |
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type: recall |
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value: 0.9901734200771951 |
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- name: F1 |
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type: f1 |
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value: 0.9911413155243709 |
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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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# modello_finetuning1 |
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This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the swiss_law_area_prediction dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0506 |
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- Precision: 0.9922 |
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- Recall: 0.9902 |
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- F1: 0.9911 |
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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: 6e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| |
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| 0.0834 | 0.38 | 500 | 0.1812 | 0.9793 | 0.9677 | 0.9730 | |
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| 0.1029 | 0.76 | 1000 | 0.0973 | 0.9875 | 0.9834 | 0.9854 | |
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| 0.0066 | 1.15 | 1500 | 0.0647 | 0.9864 | 0.9886 | 0.9875 | |
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| 0.0008 | 1.53 | 2000 | 0.0619 | 0.9913 | 0.9893 | 0.9902 | |
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| 0.0003 | 1.91 | 2500 | 0.0506 | 0.9922 | 0.9902 | 0.9911 | |
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### Framework versions |
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- Transformers 4.36.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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