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NeRUBioS_xlm_RoBERTa_base_Training_Development

Token Classification
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Safetensors
xlm-roberta
Generated from Trainer
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Fine-tuning completed

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+ ---
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+ license: mit
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+ base_model: FacebookAI/xlm-roberta-base
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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_xlm_RoBERTa_base_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_xlm_RoBERTa_base_Training_Development
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+
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+ This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3324
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+ - Negref Precision: 0.5672
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+ - Negref Recall: 0.5696
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+ - Negref F1: 0.5684
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+ - Neg Precision: 0.9480
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+ - Neg Recall: 0.9760
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+ - Neg F1: 0.9618
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+ - Nsco Precision: 0.8685
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+ - Nsco Recall: 0.9097
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+ - Nsco F1: 0.8886
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+ - Unc Precision: 0.8419
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+ - Unc Recall: 0.8842
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+ - Unc F1: 0.8625
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+ - Usco Precision: 0.6429
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+ - Usco Recall: 0.7383
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+ - Usco F1: 0.6873
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+ - Precision: 0.8190
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+ - Recall: 0.8548
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+ - F1: 0.8365
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+ - Accuracy: 0.9520
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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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+ | 0.2316 | 1.0 | 1729 | 0.2023 | 0.4328 | 0.4958 | 0.4621 | 0.8759 | 0.9629 | 0.9173 | 0.7259 | 0.8397 | 0.7786 | 0.7059 | 0.8340 | 0.7646 | 0.4408 | 0.6836 | 0.5360 | 0.6864 | 0.8063 | 0.7415 | 0.9364 |
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+ | 0.1596 | 2.0 | 3458 | 0.1756 | 0.4771 | 0.5274 | 0.5010 | 0.9252 | 0.9727 | 0.9484 | 0.8113 | 0.8836 | 0.8459 | 0.8036 | 0.8687 | 0.8349 | 0.5615 | 0.6953 | 0.6213 | 0.7624 | 0.8329 | 0.7961 | 0.9480 |
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+ | 0.1197 | 3.0 | 5187 | 0.1735 | 0.5214 | 0.5401 | 0.5306 | 0.9436 | 0.9672 | 0.9553 | 0.8449 | 0.8800 | 0.8621 | 0.8094 | 0.8687 | 0.8380 | 0.5705 | 0.6953 | 0.6268 | 0.7891 | 0.8322 | 0.8101 | 0.9510 |
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+ | 0.1006 | 4.0 | 6916 | 0.2003 | 0.5324 | 0.5717 | 0.5514 | 0.9365 | 0.9814 | 0.9584 | 0.8510 | 0.8955 | 0.8727 | 0.7965 | 0.8764 | 0.8346 | 0.5755 | 0.7148 | 0.6376 | 0.7890 | 0.8497 | 0.8182 | 0.9508 |
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+ | 0.0706 | 5.0 | 8645 | 0.2077 | 0.5434 | 0.5675 | 0.5552 | 0.9497 | 0.9683 | 0.9589 | 0.8821 | 0.9062 | 0.8940 | 0.8285 | 0.8764 | 0.8518 | 0.6013 | 0.7188 | 0.6548 | 0.8107 | 0.8482 | 0.8290 | 0.9531 |
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+ | 0.0514 | 6.0 | 10374 | 0.2554 | 0.5282 | 0.5527 | 0.5402 | 0.9281 | 0.9716 | 0.9493 | 0.8476 | 0.9050 | 0.8754 | 0.8433 | 0.8726 | 0.8577 | 0.6131 | 0.7305 | 0.6667 | 0.7950 | 0.8471 | 0.8202 | 0.9496 |
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+ | 0.039 | 7.0 | 12103 | 0.2547 | 0.5306 | 0.5675 | 0.5484 | 0.9508 | 0.9705 | 0.9606 | 0.8672 | 0.9074 | 0.8868 | 0.8582 | 0.9112 | 0.8839 | 0.6609 | 0.7461 | 0.7009 | 0.8136 | 0.8551 | 0.8339 | 0.9525 |
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+ | 0.0273 | 8.0 | 13832 | 0.2796 | 0.5447 | 0.5401 | 0.5424 | 0.9459 | 0.9738 | 0.9597 | 0.8615 | 0.9086 | 0.8844 | 0.8088 | 0.8494 | 0.8286 | 0.6575 | 0.75 | 0.7007 | 0.8115 | 0.8464 | 0.8286 | 0.9497 |
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+ | 0.0214 | 9.0 | 15561 | 0.3079 | 0.5429 | 0.5738 | 0.5579 | 0.9391 | 0.9771 | 0.9577 | 0.8707 | 0.9121 | 0.8910 | 0.8448 | 0.9035 | 0.8731 | 0.6084 | 0.7344 | 0.6655 | 0.8066 | 0.8580 | 0.8315 | 0.9514 |
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+ | 0.0136 | 10.0 | 17290 | 0.3172 | 0.5524 | 0.5781 | 0.5649 | 0.9459 | 0.9738 | 0.9597 | 0.8848 | 0.9121 | 0.8982 | 0.8476 | 0.8803 | 0.8636 | 0.6519 | 0.7461 | 0.6958 | 0.8201 | 0.8566 | 0.8380 | 0.9520 |
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+ | 0.0098 | 11.0 | 19019 | 0.3312 | 0.5729 | 0.5717 | 0.5723 | 0.9501 | 0.9771 | 0.9634 | 0.8753 | 0.9086 | 0.8916 | 0.8476 | 0.8803 | 0.8636 | 0.6574 | 0.7422 | 0.6972 | 0.8251 | 0.8551 | 0.8398 | 0.9516 |
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+ | 0.008 | 12.0 | 20748 | 0.3324 | 0.5672 | 0.5696 | 0.5684 | 0.9480 | 0.9760 | 0.9618 | 0.8685 | 0.9097 | 0.8886 | 0.8419 | 0.8842 | 0.8625 | 0.6429 | 0.7383 | 0.6873 | 0.8190 | 0.8548 | 0.8365 | 0.9520 |
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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