transformers tensorboard safetensors xlm-roberta token-classification generated_from_trainer ar dataset:xtreme base_model:FacebookAI/xlm-roberta-base base_model:finetune:FacebookAI/xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: xlm-roberta-base
datasets:
- xtreme
language:
- ar
license: mit
metrics:
- f1
- accuracy
pipeline_tag: token-classification
tags:
- generated_from_trainer
widget:
- text: اسمي محمد، اعمل في شركة اورانج و اسكن في القاهرة.
- text: يعمل احمد في ابل و يعيش في كاليفورنيا.
model-index:
- name: arabnizer-xlmr-panx-ar
  results: []
---

<!-- 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. -->

# arabnizer-xlmr-panx-ar

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2073
- F1: 0.8885
- Accuracy: 0.9533


## Intended uses & limitations
Simple Usages:

```python
from transformers import pipeline

ner_tagger = pipeline("token-classification", "mohammedaly22/arabnizer-xlmr-panx-ar")
text = "اسمي محمد، اعمل في أورانج و اسكن في القاهرة."

ner_tagger(text, grouped_entities=True)

```

result:
```json
[{'entity_group': 'PER',
  'score': 0.9486102,
  'word': 'محمد',
  'start': 5,
  'end': 9},
 {'entity_group': 'ORG',
  'score': 0.8212871,
  'word': 'اورانج',
  'start': 24,
  'end': 30},
 {'entity_group': 'LOC',
  'score': 0.9967932,
  'word': 'القاهرة',
  'start': 41,
  'end': 48}]
```

## Training and evaluation data

The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations. The corpus is made to evaluate how to perform inference in any language (including low-resources ones like Swahili or Urdu) when only English NLI data is available at training time. One solution is cross-lingual sentence encoding, for which XNLI is an evaluation benchmark. The Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark is a benchmark for the evaluation of the cross-lingual generalization ability of pre-trained multilingual models. It covers 40 typologically diverse languages (spanning 12 language families) and includes nine tasks that collectively require reasoning about different levels of syntax and semantics. The languages in XTREME are selected to maximize language diversity, coverage in existing tasks, and availability of training data. Among these are many under-studied languages, such as the Dravidian languages Tamil (spoken in southern India, Sri Lanka, and Singapore), Telugu and Malayalam (spoken mainly in southern India), and the Niger-Congo languages Swahili and Yoruba, spoken in Africa.

| Column | Description |
| ------ | ------ |
| tokens | A list of tokens. |
| ner_tags | A list of the associated NER tags for each token. |
| langs | A list of the language of each token. |

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-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 | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.2031        | 1.0   | 1250 | 0.2078          | 0.8598 | 0.9426   |
| 0.1559        | 2.0   | 2500 | 0.2043          | 0.8736 | 0.9490   |
| 0.0992        | 3.0   | 3750 | 0.2073          | 0.8885 | 0.9533   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2