Text Classification
Transformers
PyTorch
TensorBoard
ONNX
English
bert
financial-sentiment-analysis
sentiment-analysis
sentence_50agree
Generated from Trainer
sentiment
finance
Eval Results (legacy)
text-embeddings-inference
Instructions to use nickmuchi/sec-bert-finetuned-finance-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickmuchi/sec-bert-finetuned-finance-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nickmuchi/sec-bert-finetuned-finance-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification") model = AutoModelForSequenceClassification.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification") - Notebooks
- Google Colab
- Kaggle
Enquiry about the labels
#4
by JustinJia21 - opened
Hi, I checked the dataset and the original sec Bert model, I wonder are the labels "bullish" and "bearish" just hard-transformed from "positive" and "negative"? I am asking this because in both the self labeled dataset and the sec Bert model the labels are "positive" and "negative". Thank you.
yes that is correct, just mapped them to bullish/bearish