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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6069fe76d16992d39ad977f5024ea4395dbc5e0b3b5f0166023f8f862292429
- Size of remote file:
- 5.27 kB
- SHA256:
- 3dcccf7b26fff224abc5dfec5bb26ce5848e0de23597930faa724bbf5a83b5e4
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