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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- toydata |
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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: xlm-roberta-large-ner-hrl-finetuned-ner |
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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: toydata |
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type: toydata |
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args: SDN |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9132452695465905 |
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- name: Recall |
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type: recall |
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value: 0.9205854126679462 |
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- name: F1 |
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type: f1 |
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value: 0.9169006511739053 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9784804945824268 |
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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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# xlm-roberta-large-ner-hrl-finetuned-ner |
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This model is a fine-tuned version of [Davlan/xlm-roberta-large-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-large-ner-hrl) on the toydata dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0944 |
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- Precision: 0.9132 |
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- Recall: 0.9206 |
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- F1: 0.9169 |
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- Accuracy: 0.9785 |
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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: 3 |
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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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| No log | 1.0 | 408 | 0.0900 | 0.8508 | 0.9303 | 0.8888 | 0.9719 | |
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| 0.1087 | 2.0 | 816 | 0.0827 | 0.9043 | 0.9230 | 0.9136 | 0.9783 | |
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| 0.0503 | 3.0 | 1224 | 0.0944 | 0.9132 | 0.9206 | 0.9169 | 0.9785 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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