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NeRUBioS_RoBERTa_base_bne_Training_Development

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Fine-tuning completed

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  1. README.md +92 -0
  2. model.safetensors +1 -1
  3. runs/Apr13_16-56-21_79653374f968/events.out.tfevents.1713027382.79653374f968.1015.1 +2 -2
README.md ADDED
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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: NeRUBioS_RoBERTa_base_bne_Training_Development
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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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+ # NeRUBioS_RoBERTa_base_bne_Training_Development
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+
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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: 0.3499
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+ - Negref Precision: 0.5449
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+ - Negref Recall: 0.5380
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+ - Negref F1: 0.5414
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+ - Neg Precision: 0.9559
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+ - Neg Recall: 0.9694
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+ - Neg F1: 0.9626
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+ - Nsco Precision: 0.8730
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+ - Nsco Recall: 0.9062
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+ - Nsco F1: 0.8893
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+ - Unc Precision: 0.8315
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+ - Unc Recall: 0.8764
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+ - Unc F1: 0.8534
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+ - Usco Precision: 0.6608
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+ - Usco Recall: 0.7383
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+ - Usco F1: 0.6974
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+ - Precision: 0.8205
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+ - Recall: 0.8453
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+ - F1: 0.8327
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+ - Accuracy: 0.9526
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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: 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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+
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+ ### Training results
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+
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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 | Unc Precision | Unc Recall | Unc F1 | Usco Precision | Usco Recall | Usco F1 | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
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+ | 0.1898 | 1.0 | 1729 | 0.1783 | 0.4516 | 0.5316 | 0.4884 | 0.9351 | 0.9596 | 0.9472 | 0.8079 | 0.8539 | 0.8303 | 0.8193 | 0.7529 | 0.7847 | 0.5816 | 0.6406 | 0.6097 | 0.7596 | 0.8041 | 0.7813 | 0.9452 |
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+ | 0.1163 | 2.0 | 3458 | 0.1724 | 0.4906 | 0.5527 | 0.5198 | 0.9274 | 0.9760 | 0.9511 | 0.8252 | 0.9026 | 0.8622 | 0.8263 | 0.8263 | 0.8263 | 0.5662 | 0.6680 | 0.6129 | 0.7721 | 0.8376 | 0.8036 | 0.9485 |
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+ | 0.0621 | 3.0 | 5187 | 0.1946 | 0.5139 | 0.5063 | 0.5101 | 0.9524 | 0.9618 | 0.9571 | 0.8542 | 0.8836 | 0.8687 | 0.8071 | 0.8726 | 0.8386 | 0.6034 | 0.6836 | 0.6410 | 0.7999 | 0.8249 | 0.8122 | 0.9480 |
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+ | 0.0378 | 4.0 | 6916 | 0.2279 | 0.4923 | 0.5401 | 0.5151 | 0.9450 | 0.9749 | 0.9597 | 0.8568 | 0.8884 | 0.8723 | 0.8259 | 0.8610 | 0.8431 | 0.6179 | 0.6758 | 0.6455 | 0.7940 | 0.8347 | 0.8138 | 0.9490 |
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+ | 0.0192 | 5.0 | 8645 | 0.2495 | 0.5227 | 0.5338 | 0.5282 | 0.9541 | 0.9760 | 0.9649 | 0.8256 | 0.8884 | 0.8558 | 0.8071 | 0.8726 | 0.8386 | 0.6049 | 0.6758 | 0.6384 | 0.7929 | 0.8351 | 0.8135 | 0.9508 |
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+ | 0.0134 | 6.0 | 10374 | 0.2764 | 0.5199 | 0.5232 | 0.5216 | 0.9568 | 0.9672 | 0.9620 | 0.8687 | 0.8955 | 0.8819 | 0.8277 | 0.8533 | 0.8403 | 0.6389 | 0.7188 | 0.6765 | 0.8114 | 0.8347 | 0.8229 | 0.9514 |
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+ | 0.0068 | 7.0 | 12103 | 0.2876 | 0.4880 | 0.5169 | 0.5020 | 0.9470 | 0.9760 | 0.9613 | 0.8593 | 0.8919 | 0.8753 | 0.8494 | 0.8494 | 0.8494 | 0.6456 | 0.7188 | 0.6802 | 0.8010 | 0.8351 | 0.8177 | 0.9508 |
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+ | 0.0059 | 8.0 | 13832 | 0.2886 | 0.4991 | 0.5591 | 0.5274 | 0.9488 | 0.9705 | 0.9595 | 0.8601 | 0.8907 | 0.8751 | 0.8231 | 0.8803 | 0.8507 | 0.6528 | 0.7344 | 0.6912 | 0.7986 | 0.8446 | 0.8209 | 0.9516 |
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+ | 0.0029 | 9.0 | 15561 | 0.3290 | 0.5408 | 0.4895 | 0.5138 | 0.9529 | 0.9716 | 0.9622 | 0.8653 | 0.9002 | 0.8824 | 0.8218 | 0.8726 | 0.8464 | 0.6090 | 0.7422 | 0.6690 | 0.8125 | 0.8358 | 0.8240 | 0.9505 |
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+ | 0.0009 | 10.0 | 17290 | 0.3582 | 0.5438 | 0.5105 | 0.5267 | 0.9519 | 0.9716 | 0.9616 | 0.8757 | 0.9038 | 0.8895 | 0.8218 | 0.8726 | 0.8464 | 0.6737 | 0.75 | 0.7098 | 0.8227 | 0.8413 | 0.8319 | 0.9506 |
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+ | 0.0012 | 11.0 | 19019 | 0.3516 | 0.5139 | 0.5443 | 0.5287 | 0.9539 | 0.9705 | 0.9621 | 0.8834 | 0.9086 | 0.8958 | 0.8291 | 0.8803 | 0.8539 | 0.6761 | 0.75 | 0.7111 | 0.8157 | 0.8489 | 0.8320 | 0.9526 |
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+ | 0.0005 | 12.0 | 20748 | 0.3499 | 0.5449 | 0.5380 | 0.5414 | 0.9559 | 0.9694 | 0.9626 | 0.8730 | 0.9062 | 0.8893 | 0.8315 | 0.8764 | 0.8534 | 0.6608 | 0.7383 | 0.6974 | 0.8205 | 0.8453 | 0.8327 | 0.9526 |
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+
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+
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+ ### Framework versions
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+
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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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