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README.md
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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: 0.8690921853065045
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- name: Recall
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type: recall
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value: 0.8446844798180785
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- name: F1
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type: f1
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value: 0.8567145245920544
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- name: Accuracy
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type: accuracy
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value: 0.9826426490551073
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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: 0.0948
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- Precision: 0.8691
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- Recall: 0.8447
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- F1: 0.8567
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- Accuracy: 0.9826
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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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| 0.0567 | 1.0 | 2904 | 0.1081 | 0.8486 | 0.8140 | 0.8309 | 0.9796 |
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| 0.0302 | 2.0 | 5808 | 0.0906 | 0.8620 | 0.8298 | 0.8456 | 0.9818 |
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| 0.0197 | 3.0 | 8712 | 0.0948 | 0.8691 | 0.8447 | 0.8567 | 0.9826 |
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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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