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
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# FinTwitBERT
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FinTwitBERT is a language model specifically pre-trained on a large dataset of financial tweets. This specialized BERT model aims to capture the unique jargon and communication style found in the financial Twitter sphere, making it an ideal tool for sentiment analysis, trend prediction, and other financial NLP tasks.
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# FinTwitBERT
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FinTwitBERT is a language model specifically pre-trained on a large dataset of financial tweets. This specialized BERT model aims to capture the unique jargon and communication style found in the financial Twitter sphere, making it an ideal tool for sentiment analysis, trend prediction, and other financial NLP tasks.
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