---
license: mit
tags:
- generated_from_trainer
datasets:
- hi_ner-original
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: hi_ner-original
      type: hi_ner-original
      args: HiNER
    metrics:
    - name: Precision
      type: precision
      value: 0.7366076627460114
    - name: Recall
      type: recall
      value: 0.6770947627585838
    - name: F1
      type: f1
      value: 0.7055985498152408
    - name: Accuracy
      type: accuracy
      value: 0.9359390321752693
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# xlm-roberta-base-finetuned-ner

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the hi_ner-original dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2314
- Precision: 0.7366
- Recall: 0.6771
- F1: 0.7056
- Accuracy: 0.9359

## 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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2025        | 0.74  | 7000  | 0.2146          | 0.7399    | 0.6197 | 0.6745 | 0.9316   |
| 0.1641        | 1.47  | 14000 | 0.2238          | 0.7618    | 0.6108 | 0.6780 | 0.9336   |
| 0.1404        | 2.21  | 21000 | 0.2302          | 0.7560    | 0.6327 | 0.6889 | 0.9350   |
| 0.1371        | 2.95  | 28000 | 0.2226          | 0.7395    | 0.6600 | 0.6975 | 0.9350   |
| 0.1248        | 3.68  | 35000 | 0.2314          | 0.7366    | 0.6771 | 0.7056 | 0.9359   |
| 0.1112        | 4.42  | 42000 | 0.2423          | 0.7089    | 0.7064 | 0.7077 | 0.9333   |
| 0.1048        | 5.16  | 49000 | 0.2599          | 0.7326    | 0.6793 | 0.7050 | 0.9349   |
| 0.1091        | 5.89  | 56000 | 0.2542          | 0.7244    | 0.6918 | 0.7077 | 0.9348   |


### Framework versions

- Transformers 4.19.4
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1