lora-NLIGraph

Training procedure

• Used PEFT library from huggingface and leveraged LoRA procedure to tune the model. Below are the training metrics.

• Model got shrunk by nearly 60 times and with the same efficiency as distilbert-base-uncased

Inference

from transformers import AutoTokenizer, AutoModel
from peft import get_peft_config, PeftModel, PeftConfig, get_peft_model, LoraConfig, TaskType

peft_model_id = "vishnun/lora-NLIGraph"
config = PeftConfig.from_pretrained(peft_model_id)
inference_model = AutoModelForTokenClassification.from_pretrained(
    config.base_model_name_or_path, num_labels=4, id2label=id2lab, label2id=lab2id
)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(inference_model, peft_model_id)

text = "Arsenal will win the Premier League"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

tokens = inputs.tokens()
predictions = torch.argmax(logits, dim=2)

for token, prediction in zip(tokens, predictions[0].numpy()):
    print((token, model.config.id2label[prediction]))

## results : ('<s>', 'O')
('Arsenal', 'SRC')
('Ġwill', 'O')
('Ġwin', 'REL')
('Ġthe', 'O')
('ĠPremier', 'TGT')
('ĠLeague', 'O')
('</s>', 'O')

Framework versions

• PEFT 0.4.0