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import streamlit as st | |
import tensorflow as tf | |
from transformers import pipeline | |
model_ckpt = "Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD" | |
pipe = pipeline("text-classification", model_ckpt) | |
#from transformers import AutoTokenizer, TFAutoModelForSequenceClassification | |
#tokenizer = AutoTokenizer.from_pretrained("Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD") | |
#model = TFAutoModelForSequenceClassification.from_pretrained("Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD") | |
#labels= model.config.label2id | |
text = st.text_area("Enter some text in arabic language!") | |
if text: | |
#out = tf.math.softmax(model(tokenizer(text, padding=True, truncation=True, return_tensors="np")).logits, axis = -1) | |
# res = out.numpy() | |
# labels['NEGATIVE'] = res[0,0] | |
#labels['NEUTRAL'] = res[0,1] | |
#labels['POSITIVE'] = res[0,2] | |
# st.json(labels) | |
st.json(pipe(text)) |