Pretrained model: GODEL-v1_1-base-seq2seq

Fine-tuning dataset: MultiWOZ 2.2

How to use:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("gonced8/godel-multiwoz")
model = AutoModelForSeq2SeqLM.from_pretrained("gonced8/godel-multiwoz")

# Encoder input
context = [
    "USER: I need train reservations from norwich to cambridge",
    "SYSTEM: I have 133 trains matching your request. Is there a specific day and time you would like to travel?",
    "USER: I'd like to leave on Monday and arrive by 18:00.",
]

input_text = " EOS ".join(context[-5:]) + " => "

model_inputs = tokenizer(
    input_text, max_length=512, truncation=True, return_tensors="pt"
)["input_ids"]

# Decoder input
answer_start = "SYSTEM: "

decoder_input_ids = tokenizer(
    "<pad>" + answer_start,
    max_length=256,
    truncation=True,
    add_special_tokens=False,
    return_tensors="pt",
)["input_ids"]

# Generate
output = model.generate(
    model_inputs, decoder_input_ids=decoder_input_ids, max_length=256
)
output = tokenizer.decode(
    output[0], clean_up_tokenization_spaces=True, skip_special_tokens=True
)

print(output)
# SYSTEM: TR4634 arrives at 17:35. Would you like me to book that for you?
Downloads last month
16
Safetensors
Model size
223M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train gonced8/godel-multiwoz