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 pytorch_model.bin from StephanAkkerman/FinTwitBERT: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/StephanAkkerman/FinTwitBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://StephanAkkerman/FinTwitBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/StephanAkkerman/FinTwitBERT/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- eaa4ac378c57c7873a80de39f27cdf2813b0287debb643d88ebcc9c8d5b8fc68
- Size of remote file:
- 438 MB
- SHA256:
- 6d387f17da04395fe8baea648224b9e6387ff013081fd3201b9b45b6466a9902
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