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--- |
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language: |
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- en |
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license: mit |
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base_model: roberta-base |
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tags: |
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- pytorch |
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- RobertaForTokenClassification |
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- named-entity-recognition |
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- roberta-base |
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- generated_from_trainer |
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metrics: |
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- recall |
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- precision |
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- f1 |
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- accuracy |
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model-index: |
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- name: roberta-base-ontonotes |
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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-base-ontonotes |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tner/ontonotes5 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0695 |
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- Recall: 0.9227 |
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- Precision: 0.9013 |
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- F1: 0.9118 |
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- Accuracy: 0.9820 |
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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: 8e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 160 |
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- seed: 75241309 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- training_steps: 6000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| |
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| 0.1305 | 0.31 | 600 | 0.1169 | 0.8550 | 0.8139 | 0.8340 | 0.9681 | |
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| 0.118 | 0.63 | 1200 | 0.0925 | 0.8769 | 0.8592 | 0.8680 | 0.9750 | |
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| 0.0937 | 0.94 | 1800 | 0.0874 | 0.8939 | 0.8609 | 0.8771 | 0.9764 | |
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| 0.0698 | 1.25 | 2400 | 0.0821 | 0.9066 | 0.8775 | 0.8918 | 0.9784 | |
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| 0.0663 | 1.56 | 3000 | 0.0827 | 0.9124 | 0.8764 | 0.8940 | 0.9789 | |
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| 0.0624 | 1.88 | 3600 | 0.0732 | 0.9179 | 0.8868 | 0.9021 | 0.9804 | |
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| 0.0364 | 2.19 | 4200 | 0.0750 | 0.9204 | 0.8968 | 0.9085 | 0.9816 | |
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| 0.0429 | 2.5 | 4800 | 0.0699 | 0.9198 | 0.9031 | 0.9114 | 0.9818 | |
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| 0.0323 | 2.82 | 5400 | 0.0697 | 0.9227 | 0.9008 | 0.9116 | 0.9819 | |
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| 0.0334 | 3.13 | 6000 | 0.0695 | 0.9227 | 0.9013 | 0.9118 | 0.9820 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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