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Update app.py
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app.py
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@@ -1,5 +1,5 @@
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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first = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.\n\ninformal english: """
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@@ -10,6 +10,7 @@ def get_model():
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln21")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln40")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2InformalToFormalLincoln42")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/Points3")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal")
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@@ -21,8 +22,8 @@ def get_model():
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln4")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints2")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints3")
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model =
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tokenizer = AutoTokenizer.from_pretrained("
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return model, tokenizer
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model, tokenizer = get_model()
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
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import torch
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first = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.\n\ninformal english: """
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln21")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln40")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2InformalToFormalLincoln42")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/Points3")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln4")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints2")
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#model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints3")
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model = AutoModel.from_pretrained("facebook/opt-125m")
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tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
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return model, tokenizer
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model, tokenizer = get_model()
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