Text Classification
Transformers
Safetensors
English
marks
feature-extraction
finance
earnings-calls
multi-task
regression
sec
quantitative-finance
custom_code
Instructions to use BinomialTechnologies/binomial-marks-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BinomialTechnologies/binomial-marks-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BinomialTechnologies/binomial-marks-1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BinomialTechnologies/binomial-marks-1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
docs: link Binomial AI Research → binomialtec.com
Browse files
README.md
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**An earnings-call NLP scorer that produces 23 structured signals per transcript.**
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Built by [Binomial AI Research](https://
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roster of small, deployable AI models for quantitative finance. Each model is named after
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a thinker who shaped how markets are understood. **marks-1** is named after Howard Marks
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(Oaktree), whose memos parse market sentiment, tone, and the gap between what's said and
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**An earnings-call NLP scorer that produces 23 structured signals per transcript.**
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Built by [Binomial AI Research](https://www.binomialtec.com/pages/research.html). Part of the *specialist zoo* — a
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roster of small, deployable AI models for quantitative finance. Each model is named after
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a thinker who shaped how markets are understood. **marks-1** is named after Howard Marks
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| 24 |
(Oaktree), whose memos parse market sentiment, tone, and the gap between what's said and
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