Text Classification
Transformers
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
text-embeddings-inference
Instructions to use StephanAkkerman/FinTwitBERT-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StephanAkkerman/FinTwitBERT-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="StephanAkkerman/FinTwitBERT-sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("StephanAkkerman/FinTwitBERT-sentiment") model = AutoModelForSequenceClassification.from_pretrained("StephanAkkerman/FinTwitBERT-sentiment", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download training_args.bin from StephanAkkerman/FinTwitBERT-sentiment: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/StephanAkkerman/FinTwitBERT-sentiment/resolve/main/training_args.bin
- Command line
-
hf download hf://StephanAkkerman/FinTwitBERT-sentiment/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/StephanAkkerman/FinTwitBERT-sentiment/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 47d2fa62e48527db7155f35d7fb4452143532d7156d6d4f86d183de2fffe33c4
- Size of remote file:
- 4.54 kB
- SHA256:
- d6024e09bfc226d9a2b740016ef9b2c189842a4b3d4fcc171ab807b3ca9f20d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.