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--- |
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license: apache-2.0 |
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base_model: google/t5-efficient-tiny |
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tags: |
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- generated_from_trainer |
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datasets: |
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- generator |
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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: salt_language_Classification |
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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: generator |
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type: generator |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 1.0 |
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- name: Recall |
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type: recall |
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value: 1.0 |
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- name: F1 |
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type: f1 |
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value: 1.0 |
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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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# salt_language_Classification |
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This model is a fine-tuned version of [google/t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0 |
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- Precision: 1.0 |
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- Recall: 1.0 |
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- F1: 1.0 |
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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: 0.001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 10 |
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- training_steps: 20000 |
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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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| 0.0 | 0.025 | 500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.05 | 1000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.075 | 1500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.1 | 2000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.125 | 2500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.15 | 3000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.175 | 3500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.2 | 4000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.225 | 4500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.25 | 5000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.275 | 5500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.3 | 6000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.325 | 6500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.35 | 7000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.375 | 7500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.4 | 8000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.425 | 8500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.45 | 9000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.475 | 9500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.5 | 10000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.525 | 10500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.55 | 11000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.575 | 11500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.6 | 12000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.625 | 12500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.65 | 13000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.675 | 13500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.7 | 14000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.725 | 14500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.75 | 15000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.775 | 15500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.8 | 16000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.825 | 16500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.85 | 17000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.875 | 17500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.9 | 18000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.925 | 18500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.95 | 19000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 0.975 | 19500 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 1.0 | 20000 | 0.0 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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