# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Forturne/Finbert_PB")
model = AutoModelForSequenceClassification.from_pretrained("Forturne/Finbert_PB")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
You can use this model with Transformers pipeline for sentiment analysis.
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline
finbert = BertForSequenceClassification.from_pretrained('Forturne/Finbert_PB',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('Forturne/Finbert_PB')
nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
sentences = ["there is a shortage of capital, and we need extra financing",
"growth is strong and we have plenty of liquidity",
"there are doubts about our finances",
"profits are flat"]
results = nlp(sentences)
print(results) #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Forturne/Finbert_PB")