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--- |
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license: mit |
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base_model: xlm-roberta-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- uner_ser_set |
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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: uner_ser_set |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: uner_ser_set |
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type: uner_ser_set |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9338624338624338 |
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- name: Recall |
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type: recall |
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value: 0.9489247311827957 |
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- name: F1 |
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type: f1 |
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value: 0.9413333333333335 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9930792962561494 |
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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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# uner_ser_set |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the uner_ser_set dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0440 |
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- Precision: 0.9339 |
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- Recall: 0.9489 |
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- F1: 0.9413 |
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- Accuracy: 0.9931 |
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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: 3e-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: 5.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 1.10.1+cu113 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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