Fine-tuning completed
Browse files
README.md
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---
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license: apache-2.0
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base_model: PlanTL-GOB-ES/roberta-base-bne
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: RE_NegREF_NSD_Nubes_Training_Test_dataset_RoBERTa_base_bne_fine_tuned
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results: []
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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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# RE_NegREF_NSD_Nubes_Training_Test_dataset_RoBERTa_base_bne_fine_tuned
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This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3480
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- Negref Precision: 0.5338
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- Negref Recall: 0.5565
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- Negref F1: 0.5449
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- Neg Precision: 0.9552
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- Neg Recall: 0.9593
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- Neg F1: 0.9573
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- Nsco Precision: 0.8862
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- Nsco Recall: 0.9070
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- Nsco F1: 0.8964
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- Precision: 0.8428
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- Recall: 0.8591
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- F1: 0.8509
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- Accuracy: 0.9569
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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: 2e-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: 12
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Negref Precision | Negref Recall | Negref F1 | Neg Precision | Neg Recall | Neg F1 | Nsco Precision | Nsco Recall | Nsco F1 | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
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| 0.0092 | 1.0 | 1729 | 0.3395 | 0.5044 | 0.5051 | 0.5048 | 0.9546 | 0.9600 | 0.9573 | 0.8681 | 0.8911 | 0.8794 | 0.8323 | 0.8430 | 0.8376 | 0.9552 |
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| 0.0096 | 2.0 | 3458 | 0.2674 | 0.4694 | 0.5962 | 0.5252 | 0.9483 | 0.9663 | 0.9572 | 0.8717 | 0.8888 | 0.8801 | 0.8070 | 0.8629 | 0.8340 | 0.9545 |
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| 0.0069 | 3.0 | 5187 | 0.2922 | 0.5054 | 0.5477 | 0.5257 | 0.9488 | 0.9628 | 0.9557 | 0.8740 | 0.8865 | 0.8802 | 0.8275 | 0.8509 | 0.8390 | 0.9559 |
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| 0.0066 | 4.0 | 6916 | 0.2958 | 0.5371 | 0.5639 | 0.5501 | 0.9533 | 0.9614 | 0.9573 | 0.8714 | 0.8971 | 0.8841 | 0.8368 | 0.8576 | 0.8471 | 0.9559 |
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| 0.0042 | 5.0 | 8645 | 0.2978 | 0.5475 | 0.5419 | 0.5446 | 0.9584 | 0.9551 | 0.9567 | 0.8850 | 0.8964 | 0.8906 | 0.8491 | 0.8503 | 0.8497 | 0.9558 |
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| 0.0072 | 6.0 | 10374 | 0.2875 | 0.5068 | 0.5477 | 0.5265 | 0.9547 | 0.9614 | 0.9580 | 0.8791 | 0.8911 | 0.8850 | 0.8319 | 0.8521 | 0.8419 | 0.9561 |
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| 0.0045 | 7.0 | 12103 | 0.3203 | 0.5551 | 0.5624 | 0.5587 | 0.9561 | 0.9628 | 0.9594 | 0.8744 | 0.9002 | 0.8871 | 0.8448 | 0.8591 | 0.8519 | 0.9567 |
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| 0.0019 | 8.0 | 13832 | 0.3412 | 0.5339 | 0.5433 | 0.5386 | 0.9565 | 0.9579 | 0.9572 | 0.8921 | 0.9009 | 0.8965 | 0.8468 | 0.8535 | 0.8502 | 0.9560 |
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| 0.0013 | 9.0 | 15561 | 0.3270 | 0.5033 | 0.5551 | 0.5279 | 0.9533 | 0.9600 | 0.9566 | 0.8900 | 0.9062 | 0.8981 | 0.8335 | 0.8588 | 0.8459 | 0.9574 |
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| 0.0009 | 10.0 | 17290 | 0.3257 | 0.5285 | 0.5580 | 0.5429 | 0.9513 | 0.9593 | 0.9552 | 0.8826 | 0.9039 | 0.8931 | 0.8381 | 0.8582 | 0.8480 | 0.9567 |
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| 0.0007 | 11.0 | 19019 | 0.3303 | 0.5299 | 0.5595 | 0.5443 | 0.9586 | 0.9586 | 0.9586 | 0.8859 | 0.9047 | 0.8952 | 0.8423 | 0.8585 | 0.8503 | 0.9574 |
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| 0.0 | 12.0 | 20748 | 0.3480 | 0.5338 | 0.5565 | 0.5449 | 0.9552 | 0.9593 | 0.9573 | 0.8862 | 0.9070 | 0.8964 | 0.8428 | 0.8591 | 0.8509 | 0.9569 |
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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