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---
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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: roberta-gest-pred-seqeval-partialmatch
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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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# roberta-gest-pred-seqeval-partialmatch
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This model is a fine-tuned version of [xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/xlm-roberta-large-finetuned-conll03-english) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9911
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- Precision: 0.8401
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- Recall: 0.8401
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- F1: 0.8401
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- Accuracy: 0.8347
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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: 16
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- eval_batch_size: 16
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 1.7547 | 1.0 | 147 | 0.9698 | 0.7634 | 0.7634 | 0.7634 | 0.7426 |
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| 0.8657 | 2.0 | 294 | 0.6433 | 0.8300 | 0.8300 | 0.8300 | 0.8127 |
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| 0.5629 | 3.0 | 441 | 0.6167 | 0.8383 | 0.8383 | 0.8383 | 0.8270 |
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| 0.3763 | 4.0 | 588 | 0.6022 | 0.8335 | 0.8335 | 0.8335 | 0.8228 |
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| 0.2507 | 5.0 | 735 | 0.7089 | 0.8424 | 0.8424 | 0.8424 | 0.8317 |
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| 0.165 | 6.0 | 882 | 0.7194 | 0.8650 | 0.8650 | 0.8650 | 0.8537 |
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| 0.1224 | 7.0 | 1029 | 0.8469 | 0.8532 | 0.8532 | 0.8532 | 0.8478 |
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| 0.0759 | 8.0 | 1176 | 0.9850 | 0.8365 | 0.8365 | 0.8365 | 0.8288 |
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| 0.0548 | 9.0 | 1323 | 0.9934 | 0.8490 | 0.8490 | 0.8490 | 0.8424 |
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| 0.0418 | 10.0 | 1470 | 0.9911 | 0.8401 | 0.8401 | 0.8401 | 0.8347 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.13.1+cu116
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- Datasets 2.10.1
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- Tokenizers 0.13.2
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