aronip/SN-T5-Base-FT

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tags: - text2text-generation - t5 - parsing - instruction-following - custom-task

Model Description

This is a fine-tuned T5-based model designed for parsing input instructions and converting them into structured outputs. It supports tasks such as:

• Log parsing
• Data transformation
• Instruction following for structured output generation.

Example Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name")
model = AutoModelForSeq2SeqLM.from_pretrained("your-username/your-model-name")

input_text = "Parse log entry: 2025-01-01T00:53:36.000000 WARN Chartered_accountant_Service: Restarting security module key0=d6c40d4c"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs["input_ids"])
print(tokenizer.decode(outputs[0], skip_special_tokens=True))