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fine_tuned_rte_croslo

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README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ base_model: EMBEDDIA/crosloengual-bert
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: fine_tuned_rte_croslo
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+ results: []
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+ ---
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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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+
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+ # fine_tuned_rte_croslo
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+
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+ This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7790
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+ - Accuracy: 0.6207
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+ - F1: 0.5951
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-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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+ - training_steps: 400
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
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+ | 0.6951 | 1.7241 | 50 | 0.6869 | 0.5517 | 0.5549 |
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+ | 0.5952 | 3.4483 | 100 | 0.6381 | 0.6207 | 0.5466 |
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+ | 0.4725 | 5.1724 | 150 | 0.6293 | 0.6207 | 0.6090 |
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+ | 0.3055 | 6.8966 | 200 | 0.6905 | 0.6552 | 0.6018 |
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+ | 0.2004 | 8.6207 | 250 | 0.6624 | 0.6897 | 0.6523 |
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+ | 0.1191 | 10.3448 | 300 | 0.7124 | 0.6552 | 0.6236 |
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+ | 0.0661 | 12.0690 | 350 | 0.7694 | 0.6552 | 0.6236 |
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+ | 0.048 | 13.7931 | 400 | 0.7790 | 0.6207 | 0.5951 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.0
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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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+ {
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+ "_name_or_path": "EMBEDDIA/crosloengual-bert",
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+ "BertForSequenceClassification"
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+ ],
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+ "problem_type": "single_label_classification",
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+ "torch_dtype": "float32",
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