Fill-Mask
Transformers
PyTorch
Safetensors
English
bert
NLP
BERT
FinBERT
FinTwitBERT
sentiment
finance
financial-analysis
sentiment-analysis
financial-sentiment-analysis
twitter
tweets
tweet-analysis
stocks
stock-market
crypto
cryptocurrency
Eval Results (legacy)
Instructions to use StephanAkkerman/FinTwitBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StephanAkkerman/FinTwitBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="StephanAkkerman/FinTwitBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("StephanAkkerman/FinTwitBERT") model = AutoModelForMaskedLM.from_pretrained("StephanAkkerman/FinTwitBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb270f591f65aa35cb761a12bf595d079b6333d1ffe4067d2a8af8e36ac405c1
- Size of remote file:
- 4.54 kB
- SHA256:
- 590ad9cc6e31d959bcd0bbc5032609a2f9f748303a5a0c3b8399856cf975ed73
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