Text Classification
Transformers
Safetensors
English
bert
finance
reddit
wallstreetbets
sentiment-analysis
NLP
BERT
FinBERT
FinTwitBERT
sentiment
financial-analysis
financial-sentiment-analysis
stocks
stock-market
crypto
cryptocurrency
text-embeddings-inference
Instructions to use StephanAkkerman/FinTwitBERT-wsb-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StephanAkkerman/FinTwitBERT-wsb-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="StephanAkkerman/FinTwitBERT-wsb-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("StephanAkkerman/FinTwitBERT-wsb-sentiment") model = AutoModelForSequenceClassification.from_pretrained("StephanAkkerman/FinTwitBERT-wsb-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
pipeline_tag: text-classification
library_name: transformers
language:
- en
tags:
- finance
- reddit
- wallstreetbets
- sentiment-analysis
- NLP
- BERT
- FinBERT
- FinTwitBERT
- sentiment
- finance
- financial-analysis
- sentiment-analysis
- financial-sentiment-analysis
- stocks
- stock-market
- crypto
- cryptocurrency
base_model: StephanAkkerman/FinTwitBERT-sentiment
datasets:
- moonscape95/WSBS
Model Description
This model is a fine-tuned version of FinTwitBERT-sentiment, specifically adapted to understand the informal financial jargon, slang, and sarcasm used on retail trading subreddits like r/wallstreetbets.
Labels
- 0: NEUTRAL
- 1: BULLISH
- 2: BEARISH