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 rng_state.pth from StephanAkkerman/FinTwitBERT: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/StephanAkkerman/FinTwitBERT/resolve/main/rng_state.pth
- Command line
-
hf download hf://StephanAkkerman/FinTwitBERT/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/StephanAkkerman/FinTwitBERT/resolve/main/rng_state.pth
14.2 kB
- Xet hash:
- 69860ded95d64f05324389efa57c3996ebf5aff6835336cb5e497a3039196b06
- Size of remote file:
- 14.2 kB
- SHA256:
- 14471145022519f14b281d86705c9e5ca418a1833d0dfd2feea30a87977d0d8b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.