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| from transformers import AutoModelForSequenceClassification | |
| from transformers import TFAutoModelForSequenceClassification | |
| from transformers import AutoTokenizer, AutoConfig | |
| import numpy as np | |
| from scipy.special import softmax | |
| import streamlit as st | |
| st.title("Tho Tran - Milestone2") | |
| # Preprocess text (username and link placeholders) | |
| def preprocess(text): | |
| new_text = [] | |
| for t in text.split(" "): | |
| t = '@user' if t.startswith('@') and len(t) > 1 else t | |
| t = 'http' if t.startswith('http') else t | |
| new_text.append(t) | |
| return " ".join(new_text) | |
| MODEL = f"cardiffnlp/twitter-xlm-roberta-base-sentiment" | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL) | |
| config = AutoConfig.from_pretrained(MODEL) | |
| # PT | |
| model = AutoModelForSequenceClassification.from_pretrained(MODEL) | |
| model.save_pretrained(MODEL) | |
| tokenizer.save_pretrained(MODEL) | |
| text = st.text_input("Enter text here:","I love you") | |
| text = preprocess(text) | |
| encoded_input = tokenizer(text, return_tensors='pt') | |
| output = model(**encoded_input) | |
| scores = output[0][0].detach().numpy() | |
| scores = softmax(scores) | |
| # Print labels and scores | |
| ranking = np.argsort(scores) | |
| ranking = ranking[::-1] | |
| for i in range(scores.shape[0]): | |
| l = config.id2label[ranking[i]] | |
| s = scores[ranking[i]] | |
| # print(f"{i+1}) {l} {np.round(float(s), 4)}") | |
| st.write(l,np.round(float(s),4)) | |
| st.write("choosen model is https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment?text=T%27estimo%21") |