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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conllpp |
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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-large-md-conllpp |
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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: conllpp |
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type: conllpp |
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config: conllpp |
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split: train |
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args: conllpp |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9971177780689113 |
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- name: Recall |
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type: recall |
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value: 0.9968043586452576 |
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- name: F1 |
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type: f1 |
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value: 0.9969610437242934 |
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- name: Accuracy |
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type: accuracy |
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value: 0.995003768708948 |
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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-large-md-conllpp |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conllpp dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0457 |
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- Precision: 0.9971 |
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- Recall: 0.9968 |
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- F1: 0.9970 |
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- Accuracy: 0.9950 |
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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: 1e-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 |
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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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| 0.0748 | 1.0 | 878 | 0.0309 | 0.9959 | 0.9962 | 0.9961 | 0.9935 | |
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| 0.0111 | 2.0 | 1756 | 0.0346 | 0.9974 | 0.9967 | 0.9970 | 0.9951 | |
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| 0.0057 | 3.0 | 2634 | 0.0348 | 0.9974 | 0.9960 | 0.9967 | 0.9946 | |
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| 0.0031 | 4.0 | 3512 | 0.0434 | 0.9976 | 0.9964 | 0.9970 | 0.9951 | |
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| 0.0017 | 5.0 | 4390 | 0.0457 | 0.9971 | 0.9968 | 0.9970 | 0.9950 | |
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### Framework versions |
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- Transformers 4.21.2 |
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- Pytorch 1.12.1 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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