---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- universalner/universal_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: UNER_subword_tk_en_lora_alpha_256_drop_0.3_rank_128_seed_42
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: universalner/universal_ner en_ewt
      type: universalner/universal_ner
      config: en_ewt
      split: validation
      args: en_ewt
    metrics:
    - name: Precision
      type: precision
      value: 0.7770204479065238
    - name: Recall
      type: recall
      value: 0.8260869565217391
    - name: F1
      type: f1
      value: 0.8008028098344204
    - name: Accuracy
      type: accuracy
      value: 0.9841743210465624
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# UNER_subword_tk_en_lora_alpha_256_drop_0.3_rank_128_seed_42

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the universalner/universal_ner en_ewt dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0751
- Precision: 0.7770
- Recall: 0.8261
- F1: 0.8008
- Accuracy: 0.9842

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 392  | 0.0746          | 0.6978    | 0.7578 | 0.7266 | 0.9775   |
| 0.1317        | 2.0   | 784  | 0.0618          | 0.7088    | 0.7888 | 0.7467 | 0.9807   |
| 0.0475        | 3.0   | 1176 | 0.0578          | 0.7483    | 0.8157 | 0.7806 | 0.9840   |
| 0.037         | 4.0   | 1568 | 0.0550          | 0.7439    | 0.8271 | 0.7833 | 0.9837   |
| 0.037         | 5.0   | 1960 | 0.0573          | 0.7468    | 0.8364 | 0.7891 | 0.9827   |
| 0.0305        | 6.0   | 2352 | 0.0581          | 0.7458    | 0.8230 | 0.7825 | 0.9833   |
| 0.0259        | 7.0   | 2744 | 0.0603          | 0.7683    | 0.8375 | 0.8014 | 0.9840   |
| 0.0237        | 8.0   | 3136 | 0.0622          | 0.7754    | 0.8219 | 0.7980 | 0.9843   |
| 0.0197        | 9.0   | 3528 | 0.0618          | 0.7759    | 0.8209 | 0.7978 | 0.9840   |
| 0.0197        | 10.0  | 3920 | 0.0664          | 0.7814    | 0.8178 | 0.7992 | 0.9845   |
| 0.0174        | 11.0  | 4312 | 0.0638          | 0.7751    | 0.8137 | 0.7939 | 0.9841   |
| 0.0152        | 12.0  | 4704 | 0.0678          | 0.7783    | 0.8251 | 0.8010 | 0.9845   |
| 0.0146        | 13.0  | 5096 | 0.0663          | 0.7871    | 0.8116 | 0.7992 | 0.9845   |
| 0.0146        | 14.0  | 5488 | 0.0678          | 0.7819    | 0.8313 | 0.8058 | 0.9849   |
| 0.0123        | 15.0  | 5880 | 0.0702          | 0.7862    | 0.8261 | 0.8057 | 0.9844   |
| 0.0115        | 16.0  | 6272 | 0.0727          | 0.7872    | 0.8271 | 0.8067 | 0.9846   |
| 0.0098        | 17.0  | 6664 | 0.0730          | 0.7952    | 0.8240 | 0.8094 | 0.9849   |
| 0.01          | 18.0  | 7056 | 0.0754          | 0.7891    | 0.8251 | 0.8067 | 0.9848   |
| 0.01          | 19.0  | 7448 | 0.0749          | 0.7706    | 0.8240 | 0.7964 | 0.9839   |
| 0.0091        | 20.0  | 7840 | 0.0751          | 0.7770    | 0.8261 | 0.8008 | 0.9842   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1