from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("Pieces/time-intent-flan-t5-small-v1")
tokenizer = AutoTokenizer.from_pretrained("Pieces/time-intent-flan-t5-small-v1")

# Example usage
input_text = "Your input text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)

Training Configuration

Base Model: google/flan-t5-small
Task Type: classification
Architecture: T5ForSequenceClassification

This checkpoint was saved during active training. For the latest version and training details, please check the model repository.