pytorch tensorboard xlm-roberta token-classification dataset:mim_gold_ner transformers generated_from_trainer license:agpl-3.0 model-index autotrain_compatible
---
license: agpl-3.0
tags:
- generated_from_trainer
datasets:
- mim_gold_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLMR-ENIS-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: mim_gold_ner
      type: mim_gold_ner
      args: mim-gold-ner
    metrics:
    - name: Precision
      type: precision
      value: 0.8714268909540054
    - name: Recall
      type: recall
      value: 0.842296759522456
    - name: F1
      type: f1
      value: 0.8566142460684552
    - name: Accuracy
      type: accuracy
      value: 0.9827189115812273
---

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# XLMR-ENIS-finetuned-ner

This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0955
- Precision: 0.8714
- Recall: 0.8423
- F1: 0.8566
- Accuracy: 0.9827

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0561        | 1.0   | 2904 | 0.0939          | 0.8481    | 0.8205 | 0.8341 | 0.9804   |
| 0.031         | 2.0   | 5808 | 0.0917          | 0.8652    | 0.8299 | 0.8472 | 0.9819   |
| 0.0186        | 3.0   | 8712 | 0.0955          | 0.8714    | 0.8423 | 0.8566 | 0.9827   |


### Framework versions

- Transformers 4.11.1
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
