miasambolec commited on
Commit
3dea9cd
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verified ·
1 Parent(s): 8df3456

Update demo.py

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Files changed (1) hide show
  1. demo.py +1 -4
demo.py CHANGED
@@ -11,8 +11,7 @@ from tensorflow.keras.models import load_model
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  from tensorflow.keras.preprocessing.sequence import pad_sequences
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  import re
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- # Load SVM model and vectorizer
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- svm_repo_id = "your-username/svm-sentiment-model" # Replace with your actual repo
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  svm_model_path = hf_hub_download(repo_id=svm_repo_id, filename="svm_model.pkl")
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  with open(svm_model_path, "rb") as f:
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  svm_model = pickle.load(f)
@@ -20,7 +19,6 @@ vectorizer_path = hf_hub_download(repo_id=svm_repo_id, filename="vectorizer.pkl"
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  with open(vectorizer_path, "rb") as f:
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  vectorizer = pickle.load(f)
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- # Load LSTM model and tokenizer
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  lstm_repo_id = "arjahojnik/LSTM-sentiment-model"
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  lstm_model_path = hf_hub_download(repo_id=lstm_repo_id, filename="LSTM_model.h5")
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  lstm_model = load_model(lstm_model_path)
@@ -28,7 +26,6 @@ lstm_tokenizer_path = hf_hub_download(repo_id=lstm_repo_id, filename="my_tokeniz
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  with open(lstm_tokenizer_path, "rb") as f:
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  lstm_tokenizer = pickle.load(f)
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- # Load BERT model and tokenizer
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  bert_tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
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  bert_model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
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  from tensorflow.keras.preprocessing.sequence import pad_sequences
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  import re
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+ svm_repo_id = "HighFive-OPJ/Deep_Learning"
 
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  svm_model_path = hf_hub_download(repo_id=svm_repo_id, filename="svm_model.pkl")
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  with open(svm_model_path, "rb") as f:
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  svm_model = pickle.load(f)
 
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  with open(vectorizer_path, "rb") as f:
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  vectorizer = pickle.load(f)
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  lstm_repo_id = "arjahojnik/LSTM-sentiment-model"
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  lstm_model_path = hf_hub_download(repo_id=lstm_repo_id, filename="LSTM_model.h5")
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  lstm_model = load_model(lstm_model_path)
 
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  with open(lstm_tokenizer_path, "rb") as f:
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  lstm_tokenizer = pickle.load(f)
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  bert_tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
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  bert_model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")