FinGPT-V1 series (Labeled by the Market)

You can try our FinGPT v1.1 model as follows or :

from peft import PeftModel, PeftConfig
from transformers import LlamaForCausalLM, LlamaTokenizerFast

# Load Models
base_model = "daryl149/llama-2-13b-chat-hf" 
peft_model = "oliverwang15/FinGPT_v11_Llama2_13B_Sentiment_Market_Feedback_LoRA_FT_8bit"
tokenizer = LlamaTokenizerFast.from_pretrained(base_model, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
model = LlamaForCausalLM.from_pretrained(base_model, trust_remote_code=True, device_map = "cuda:0", load_in_8bit = True,)
model = PeftModel.from_pretrained(model, peft_model)
model = model.eval()

# Make prompts
# Please change the news to the news you want
news = 'A tinyurl link takes users to a scamming site promising that users can earn thousands of dollars by becoming a Google ( NASDAQ : GOOG ) Cash advertiser'
template = """News: '''{}'''

Instruction: Please 'ONLY' output 'one' sentiment of all the above News from {{ Severely Positive / Moderately Positive / Mildly Positive / Neutral / Mildly Negative / Moderately Negative / Severely Negative }} without other words.

Answer:
"""
prompt = template.format(mews)

# Generate results
tokens = tokenizer(prompt, return_tensors='pt', padding=True)
tokens = tokens.to(model.device)
with torch.no_grad():
  res = model.generate(**tokens, max_length=1024)
torch.cuda.empty_cache()
res_sentences = [tokenizer.decode(i, skip_special_tokens=True) for i in res]
out_text = [o.split("Answer:")[1] for o in res_sentences]
sentiment = out_text[0].strip()
print(sentiment)
# Severely Negative

Ⅰ. What is FinGPT-V1 series

FinGPT-V1 is a series of LoRA model for financial sentiment analysis whose labels are generated by the change of market (stock price).
You can also say these models are analyzing or predicting the market through related alternative data (news, social media, filings, etc,).

Ⅱ. Why label by the market

Labeling has always been a big issue in the ever-changing financial market, labeling by the market is more efficient and economic than human labor (not necessarily better).
Human's opinion on the market is sometimes biased. Since our final goal is to analyze or predict the market, why not let the model learn directly from the market.

Ⅲ. What do we have

Project/Model	Base Model	Data Type	Market	Frequency	# of Emotions
FinGPT v1.0	ChatGLM2-6B	News	CN	1 day	5
FinGPT v1.1	Llama 2 - 13B	News	US	1 day	7