Add test script

Test script returns prediction probabilities.
Takes a hard-coded sentence

.gitignore

+venv

.gitmodules

+[submodule "bert-base-uncased-hatexplain-rationale-two"]
+	path = bert-base-uncased-hatexplain-rationale-two
+	url = https://huggingface.co/Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two

README.md

 # adversarial-hatespeech
 
+## Installation
+```bash
+$ pip install transformers
+$ pip install --no-cache-dir torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
+$ git clone https://huggingface.co/Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two
+```
+
 
 
 ## Getting started

bert-base-uncased-hatexplain-rational... @ 7b1a724a

+Subproject commit 7b1a724a178c639a4b3446c0ff8f13d19be4f471

hatexplain

+bert-base-uncased-hatexplain-rationale-two
\ No newline at end of file

test.py

+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+### from models.py
+from hatexplain.models import *
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+tokenizer = AutoTokenizer.from_pretrained("Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two")
+model = \
+    Model_Rational_Label.from_pretrained(
+        "Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two"
+    )
+model = model.to(device)
+inputs = tokenizer('He is a great guy', return_tensors="pt").to(device)
+prediction_logits, _ = model(input_ids=inputs['input_ids'],attention_mask=inputs['attention_mask'])
+softmax = torch.nn.Softmax(dim=1)
+probs = softmax(prediction_logits)
+print(f"Normal: {probs[0][0]}\nHatespeech: {probs[0][1]}")