README.md

---
datasets:
- dru-ac/ArTopicDS
- dru-ac/ArTopicDS-Books
metrics:
- accuracy
- precision
- recall
pipeline_tag: text-classification
---

`ArGTClass` is a `bloomz` based classification model, finetuned to categorize a comprehensive spectrum
of fourteen distinct subjects that are Religion,
Finance and Economics, Politics, Medical, Cul-
ture, Sports, Science and Technology, Anthro-
pology and Sociology, Art and Literature, Edu-
cation, History, Language and Linguistics, Law,
as well as Philosophy in Arabic.


For more details, check out our [paper](here)

Finetuning code in the following notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/106oPnGhe8B_BCgV6LnJbvVZNv4mCu9Zv?usp=sharing)


### Full classification example (CPU)

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("dru-ac/ArGTClass")
model = AutoModelForSequenceClassification.from_pretrained("dru-ac/ArGTClass")

text = " .قصفت إسرائيل مستشفى المعمداني في مدينة غزة، والذي خلف مئات الشهداء والجرحى"

inputs = tokenizer(text, return_tensors= 'pt')
outputs = model(**inputs)
ind = outputs.logits.argmax(dim=-1)[0]
predicted_class = model.config.id2label[ind.item()]
```

### Full classification example (GPU)

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("dru-ac/ArGTClass")
model = AutoModelForSequenceClassification.from_pretrained("dru-ac/ArGTClass", device_map = 'auto')

text = " .قصفت إسرائيل مستشفى المعمداني في مدينة غزة، والذي خلف مئات الشهداء والجرحى"

inputs = tokenizer(text, return_tensors= 'pt').to("cuda")
outputs = model(**inputs)
ind = outputs.logits.argmax(dim=-1)[0]
predicted_class = model.config.id2label[ind.item()]
```


### Pipeline example (CPU & GPU)

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dru-ac/ArGTClass")
model = AutoModelForSequenceClassification.from_pretrained("dru-ac/ArGTClass", device_map = 'auto')

classifier = pipeline("text-classification", model=model, tokenizer= tokenizer)

text = " .قصفت إسرائيل مستشفى المعمداني في مدينة غزة، والذي خلف مئات الشهداء والجرحى"

classifier(text)
```