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--- |
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license: agpl-3.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mim_gold_ner |
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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: XLMR-ENIS-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: mim_gold_ner |
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type: mim_gold_ner |
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args: mim-gold-ner |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8685291700903862 |
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- name: Recall |
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type: recall |
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value: 0.841273450824332 |
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- name: F1 |
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type: f1 |
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value: 0.8546840706942359 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9824748714976435 |
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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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# XLMR-ENIS-finetuned-ner |
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This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0940 |
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- Precision: 0.8685 |
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- Recall: 0.8413 |
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- F1: 0.8547 |
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- Accuracy: 0.9825 |
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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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| 0.0564 | 1.0 | 2904 | 0.0943 | 0.8505 | 0.8118 | 0.8307 | 0.9798 | |
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| 0.0321 | 2.0 | 5808 | 0.0907 | 0.8610 | 0.8235 | 0.8419 | 0.9814 | |
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| 0.0198 | 3.0 | 8712 | 0.0940 | 0.8685 | 0.8413 | 0.8547 | 0.9825 | |
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### Framework versions |
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- Transformers 4.11.2 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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