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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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- wikiann |
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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_ner_wikiann |
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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: wikiann |
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type: wikiann |
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args: en |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8462551098177787 |
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- name: Recall |
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type: recall |
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value: 0.8634242895518167 |
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- name: F1 |
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type: f1 |
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value: 0.8547534903250638 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9382388000397338 |
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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_ner_wikiann |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the wikiann dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2783 |
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- Precision: 0.8463 |
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- Recall: 0.8634 |
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- F1: 0.8548 |
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- Accuracy: 0.9382 |
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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: cosine |
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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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| 0.3395 | 1.0 | 1250 | 0.2652 | 0.8039 | 0.8308 | 0.8171 | 0.9242 | |
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| 0.2343 | 2.0 | 2500 | 0.2431 | 0.8354 | 0.8503 | 0.8428 | 0.9329 | |
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| 0.1721 | 3.0 | 3750 | 0.2315 | 0.8330 | 0.8503 | 0.8416 | 0.9352 | |
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| 0.1156 | 4.0 | 5000 | 0.2709 | 0.8477 | 0.8634 | 0.8554 | 0.9385 | |
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| 0.1026 | 5.0 | 6250 | 0.2783 | 0.8463 | 0.8634 | 0.8548 | 0.9382 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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