Our implementation currently supports the following sequence classification benchmarks:

1. SST2 (2 classes) / SST5 (5 classes)
2. AGNews (4 classes)
3. Twitter Financial News Sentiment (twitterfin, 3 classes)

and token classification benchmarks for named entity recognition (NER): CoNLL2003 and OntonotesV5.

Commands for training LS-LLaMA and LS-unLLaMA on different tasks can follow the templates below:

foo@bar:~$ CUDA_VISIBLE_DEVICES=0 python file_name.py dataset_name model_size

file_name.py can be one of unllama_seq_clf.py, unllama_token_clf.py, llama_seq_clf.py, and llama_token_clf.py, for training LS-LLaMA and LS-unLLaMA on sequence- and token-level classification.

dataset_name can be one of sst2, sst5, agnews, twitterfin, conll03, and ontonotesv5.

model_size can be 7b or 13b, corresponding to LLaMA-2-7B and LLaMA-2-13B.

For example, the following command will train LS-unLLaMA based on LLaMA-2-7B on AGNews for sequence classification:

foo@bar:~$ CUDA_VISIBLE_DEVICES=0 python unllama_seq_clf.py agnews 7b

Load Pretrained Models

from transformers import AutoTokenizer
from modeling_llama import (
    LlamaForSequenceClassification, LlamaForTokenClassification,
    UnmaskingLlamaForSequenceClassification, UnmaskingLlamaForTokenClassification,
)


model_id = 'meta-llama/Llama-2-7b'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = LlamaForSequenceClassification.from_pretrained(model_id).bfloat16()
model = LlamaForTokenClassification.from_pretrained(model_id).bfloat16()
model = UnmaskingLlamaForSequenceClassification.from_pretrained(model_id).bfloat16()
model = UnmaskingLlamaForTokenClassification.from_pretrained(model_id).bfloat16()

For more usage, please refer to unllama_seq_clf.py, unllama_token_clf.py, llama_seq_clf.py, llama_token_clf.py.