Instructions to use jla25/squareV3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jla25/squareV3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jla25/squareV3") model = AutoModelForSeq2SeqLM.from_pretrained("jla25/squareV3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update handler.py
Browse filespadding="max_length", max_length=1000
- handler.py +1 -1
handler.py
CHANGED
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@@ -12,7 +12,7 @@ class EndpointHandler:
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raise ValueError("La entrada debe ser un diccionario con la clave 'inputs' y un valor válido.")
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input_text = "Generate a valid JSON capturing data from this text: " + data["inputs"]
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-
tokens = self.tokenizer(input_text, return_tensors="pt", truncation=True, padding=
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if not tokens or not tokens["input_ids"]:
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raise ValueError("Error al tokenizar el texto de entrada. Verifica el texto.")
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return tokens
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raise ValueError("La entrada debe ser un diccionario con la clave 'inputs' y un valor válido.")
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input_text = "Generate a valid JSON capturing data from this text: " + data["inputs"]
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+
tokens = self.tokenizer(input_text, return_tensors="pt", truncation=True, padding="max_length", max_length=1000)
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if not tokens or not tokens["input_ids"]:
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raise ValueError("Error al tokenizar el texto de entrada. Verifica el texto.")
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return tokens
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