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
Commit ·
070f80e
1
Parent(s): 87d2ad7
Updated widget
Browse files
README.md
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- en
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tags:
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- finance
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---
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# FinTwitBERT
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- en
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tags:
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- finance
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widget:
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- text: "Paris is the [MASK] of France."
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example_title: "Generic example 1"
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- text: "The goal of life is [MASK]."
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example_title: "Generic example 2"
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- text: "AAPL is a [MASK] company."
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example_title: "AAPL example"
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- text: "I predict that this stock will go [MASK]."
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example_title: "Stock prediction example"
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---
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# FinTwitBERT
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