
# bert-arsentd-lev
Arabic version bert model fine tuned on ArSentD-LEV dataset

## Data
The model were fine-tuned on ~4000 sentence from twitter multiple dialect and five classes we used 3 out of 5 int the experiment.

         

## Results
| class    | precision | recall | f1-score | Support |
|----------|-----------|--------|----------|---------|
| 0        | 0.8211    | 0.8080 | 0.8145   | 125     |
| 1        | 0.7174    | 0.7857 | 0.7500   | 84      |
| 2        | 0.6867    | 0.6404 | 0.6628   | 89      |
| Accuracy |           |        | 0.7517   |  298    |


## How to use

You can use these models by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:  

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name="mofawzy/bert-arsentd-lev"
model = AutoModelForSequenceClassification.from_pretrained(model_name,num_labels=3)
tokenizer = AutoTokenizer.from_pretrained(model_name)

```

{"language": ["ar"], "tags": ["ArSentD-LEV"], "datasets": ["ArSentD-LEV"], "widget": [{"text": "\u064a\u0647\u062f\u064a \u0627\u0644\u0644\u0647 \u0645\u0646 \u064a\u0634\u0627\u0621"}, {"text": "\u0627\u0644\u0627\u0633\u0644\u0648\u0628 \u0642\u0630\u0631 \u0648\u0642\u0645\u0627\u0645\u0647"}]}
transformers pytorch safetensors bert text-classification ArSentD-LEV ar dataset:ArSentD-LEV autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #ArSentD-LEV #ar #dataset-ArSentD-LEV #autotrain_compatible #endpoints_compatible #region-us 

bert-arsentd-lev
================


Arabic version bert model fine tuned on ArSentD-LEV dataset


Data
----


The model were fine-tuned on ~4000 sentence from twitter multiple dialect and five classes we used 3 out of 5 int the experiment.


Results
-------



How to use
----------


You can use these models by installing 'torch' or 'tensorflow' and Huggingface library 'transformers'. And you can use it directly by initializing it like this:



TAGS
#transformers #pytorch #safetensors #bert #text-classification #ArSentD-LEV #ar #dataset-ArSentD-LEV #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #ArSentD-LEV #ar #dataset-ArSentD-LEV #autotrain_compatible #endpoints_compatible #region-us 
