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
Commit History
-
Added latest model trained on 8 million rows
7e021a6
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Added own datasets
2e7eab7
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V7 - yiyanghkust's FinBERT
5e79875
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V6 - Gradual unfreezing
9921991 -
FinTwitBERT-v5
Added weight decay and removed duplicates from data8555b0a
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Version 4
Including safetensorse82ff4e
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FinTwitBERT-v3
87d2ad7
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Improved tokenizer
88e3087
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Uploaded pretrained output
a549e3b