francisco-perez-sorrosal
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README.md
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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-multilingual-cased-finetuned-with-spanish-tweets-clf
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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: 0.6005528680027643
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- name: F1
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type: f1
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value: 0.5980973383983778
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- name: Precision
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type: precision
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value: 0.6008849518067042
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- name: Recall
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type: recall
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value: 0.5962561389203832
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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-multilingual-cased-finetuned-with-spanish-tweets-clf
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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 dataset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.4692
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- Accuracy: 0.6006
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- F1: 0.5981
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- Precision: 0.6009
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- Recall: 0.5963
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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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| 1.0168 | 1.0 | 543 | 0.9144 | 0.5563 | 0.5012 | 0.5240 | 0.5251 |
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| 0.8197 | 2.0 | 1086 | 0.9133 | 0.5764 | 0.5476 | 0.5815 | 0.5462 |
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| 0.5574 | 3.0 | 1629 | 1.0629 | 0.6151 | 0.6150 | 0.6227 | 0.6112 |
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| 0.3487 | 4.0 | 2172 | 1.4692 | 0.6006 | 0.5981 | 0.6009 | 0.5963 |
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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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