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Update tasks/text.py
Browse files- tasks/text.py +6 -6
tasks/text.py
CHANGED
@@ -181,7 +181,7 @@ async def evaluate_text(request: TextEvaluationRequest):
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test_dataset = dataset["test"]
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if MODEL =="mlp":
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model = ConspiracyClassification768.from_pretrained("ypesk/frugal-ai-EURECOM-mlp-768")
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model = model.to(device)
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emb_model = SentenceTransformer("sentence-transformers/sentence-t5-large")
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batch_size = 6
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@@ -205,7 +205,7 @@ async def evaluate_text(request: TextEvaluationRequest):
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elif MODEL == "ct":
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model = CTBERT.from_pretrained("ypesk/frugal-ai-EURECOM-ct-bert-baseline")
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained('digitalepidemiologylab/covid-twitter-bert')
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test_texts = [t['quote'] for t in test_dataset]
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@@ -225,7 +225,7 @@ async def evaluate_text(request: TextEvaluationRequest):
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test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
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elif MODEL == "modern-base":
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model = conspiracyModelBase.from_pretrained("ypesk/frugal-ai-EURECOM-modern-base-
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
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@@ -246,7 +246,7 @@ async def evaluate_text(request: TextEvaluationRequest):
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test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
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elif MODEL == "modern-large":
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model = conspiracyModelLarge.from_pretrained('ypesk/frugal-ai-EURECOM-modern-large-
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-large")
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@@ -267,7 +267,7 @@ async def evaluate_text(request: TextEvaluationRequest):
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test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
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elif MODEL == "gte-base":
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model = gteModel.from_pretrained("ypesk/frugal-ai-EURECOM-gte-base-
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-base-en-v1.5')
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@@ -289,7 +289,7 @@ async def evaluate_text(request: TextEvaluationRequest):
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test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
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elif MODEL == "gte-large":
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model = gteModelLarge.from_pretrained("ypesk/frugal-ai-EURECOM-gte-large-
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-large-en-v1.5')
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test_dataset = dataset["test"]
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if MODEL =="mlp":
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model = ConspiracyClassification768.from_pretrained("ypesk/frugal-ai-EURECOM-mlp-768-fullset")
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model = model.to(device)
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emb_model = SentenceTransformer("sentence-transformers/sentence-t5-large")
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batch_size = 6
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elif MODEL == "ct":
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model = CTBERT.from_pretrained("ypesk/frugal-ai-EURECOM-ct-bert-baseline")
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained('digitalepidemiologylab/covid-twitter-bert-fullset')
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test_texts = [t['quote'] for t in test_dataset]
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test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
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elif MODEL == "modern-base":
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model = conspiracyModelBase.from_pretrained("ypesk/frugal-ai-EURECOM-modern-base-fullset")
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
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test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
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elif MODEL == "modern-large":
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model = conspiracyModelLarge.from_pretrained('ypesk/frugal-ai-EURECOM-modern-large-fullset')
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-large")
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test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
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elif MODEL == "gte-base":
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model = gteModel.from_pretrained("ypesk/frugal-ai-EURECOM-gte-base-fullset")
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-base-en-v1.5')
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test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
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elif MODEL == "gte-large":
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model = gteModelLarge.from_pretrained("ypesk/frugal-ai-EURECOM-gte-large-fullset")
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-large-en-v1.5')
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