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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("StephanAkkerman/FinTwitBERT") model = AutoModelForMaskedLM.from_pretrained("StephanAkkerman/FinTwitBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from StephanAkkerman/FinTwitBERT: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/StephanAkkerman/FinTwitBERT/resolve/main/tokenizer.json
- Command line
-
hf download hf://StephanAkkerman/FinTwitBERT/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/StephanAkkerman/FinTwitBERT/resolve/main/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.