File size: 1,513 Bytes
4b135e0 1eb1bfd 4b135e0 1eb1bfd 154f0af 1eb1bfd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
---
license: gpl-3.0
datasets:
- multi_woz_v22
language:
- en
metrics:
- bleu
- rouge
---
Pretrained model: [GODEL-v1_1-base-seq2seq](https://huggingface.co/microsoft/GODEL-v1_1-base-seq2seq/)
Fine-tuning dataset: [MultiWOZ 2.2](https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2)
# How to use:
```python
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?
``` |