Text Classification
Transformers
PyTorch
English
bert
finance
sentiment analysis
regression
sentence bert
text-embeddings-inference
Instructions to use LHF/FinEAS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LHF/FinEAS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LHF/FinEAS")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LHF/FinEAS") model = AutoModelForSequenceClassification.from_pretrained("LHF/FinEAS") - Notebooks
- Google Colab
- Kaggle
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README.md
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**Note that the predictions of this HF model will go from 0 to 1 being 0.5 neutral, 1 positive and 0 negative.**
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## Evaluation
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## Code
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You can find the code for this model in the following link: https://github.com/lhf-labs/finance-news-analysis-bert
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**Note that the predictions of this HF model will go from 0 to 1 being 0.5 neutral, 1 positive and 0 negative.**
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## Evaluation
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| Dates | FinEAS | FinBERT |
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| 6 months | 0.0044 | 0.0050 |
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| 12 months | 0.0036 | 0.0034 |
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| 24 months | 0.0033 | 0.0040 |
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*Evaluated with the next two weeks.
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## Code
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You can find the code for this model in the following link: https://github.com/lhf-labs/finance-news-analysis-bert
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