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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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- dataset |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: distilbert-base-uncased-finetuned-with-spanish-tweets-clf-cleaned-ds |
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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: dataset |
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type: dataset |
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config: 60-20-20 |
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split: dev |
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args: 60-20-20 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.5556323427781618 |
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- name: F1 |
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type: f1 |
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value: 0.5577964748279268 |
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- name: Precision |
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type: precision |
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value: 0.5682169161320979 |
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- name: Recall |
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type: recall |
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value: 0.5539741666889855 |
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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-with-spanish-tweets-clf-cleaned-ds |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1229 |
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- Accuracy: 0.5556 |
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- F1: 0.5578 |
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- Precision: 0.5682 |
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- Recall: 0.5540 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 32 |
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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: 4.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 1.0683 | 1.0 | 543 | 1.0019 | 0.4997 | 0.4041 | 0.4724 | 0.4488 | |
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| 0.9372 | 2.0 | 1086 | 0.9395 | 0.5425 | 0.5143 | 0.5480 | 0.5123 | |
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| 0.7283 | 3.0 | 1629 | 0.9674 | 0.5632 | 0.5615 | 0.5658 | 0.5587 | |
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| 0.5127 | 4.0 | 2172 | 1.1229 | 0.5556 | 0.5578 | 0.5682 | 0.5540 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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