Update utils.py
Browse files
utils.py
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@@ -160,13 +160,14 @@ EMBEDDING_MODELL = "sentence-transformers/all-mpnet-base-v2"
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#Modell und Tokenizer, um die Summary über die relevanten Texte zu machen
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#mögliche Modelle: "HuggingFaceH4/zephyr-7b-alpha" #"t5-small" #"meta-llama/Meta-Llama-3-8B-Instruct" #"mistralai/Mistral-7B-Instruct-v0.3" #"microsoft/Phi-3-mini-4k-instruct" #"HuggingFaceH4/zephyr-7b-alpha"
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HF_MODELL = "distilbert-base-uncased-distilled-squad"
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modell_rag = DistilBertForQuestionAnswering.from_pretrained(HF_MODELL)
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tokenizer_rag = DistilBertTokenizer.from_pretrained(HF_MODELL)
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qa_pipeline = pipeline("question-answering", model=modell_rag, tokenizer=tokenizer_rag)
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HF_MODELL ="EleutherAI/gpt-neo-2.7B"
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modell_rag = GPTNeoForCausalLM.from_pretrained(HF_MODELL)
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tokenizer_rag = GPT2Tokenizer.from_pretrained(HF_MODELL)
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#Modell und Tokenizer, um die Summary über die relevanten Texte zu machen
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#mögliche Modelle: "HuggingFaceH4/zephyr-7b-alpha" #"t5-small" #"meta-llama/Meta-Llama-3-8B-Instruct" #"mistralai/Mistral-7B-Instruct-v0.3" #"microsoft/Phi-3-mini-4k-instruct" #"HuggingFaceH4/zephyr-7b-alpha"
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"""
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HF_MODELL = "distilbert-base-uncased-distilled-squad"
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modell_rag = DistilBertForQuestionAnswering.from_pretrained(HF_MODELL)
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tokenizer_rag = DistilBertTokenizer.from_pretrained(HF_MODELL)
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qa_pipeline = pipeline("question-answering", model=modell_rag, tokenizer=tokenizer_rag)
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HF_MODELL ="EleutherAI/gpt-neo-2.7B"
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modell_rag = GPTNeoForCausalLM.from_pretrained(HF_MODELL)
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tokenizer_rag = GPT2Tokenizer.from_pretrained(HF_MODELL)
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