Instructions to use dasnil500/aaec_comp_flan-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dasnil500/aaec_comp_flan-t5-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("dasnil500/aaec_comp_flan-t5-base") model = AutoModelForMultimodalLM.from_pretrained("dasnil500/aaec_comp_flan-t5-base") - Notebooks
- Google Colab
- Kaggle
aaec_comp_flan-t5-base
This model is a fine-tuned checkpoint of google/flan-t5-base for AAEC composition-related generation tasks.
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_id = "dasnil500/aaec_comp_flan-t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
text = "your input text here"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Notes
- Base model:
google/flan-t5-base - Architecture:
T5ForConditionalGeneration - This repository contains model weights and configuration from training checkpoint
checkpoint-711.
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Model tree for dasnil500/aaec_comp_flan-t5-base
Base model
google/flan-t5-base