Instructions to use bubai567/nifty_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bubai567/nifty_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bubai567/nifty_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bubai567/nifty_bert") model = AutoModelForSequenceClassification.from_pretrained("bubai567/nifty_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Model description
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## Intended uses & limitations
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More information needed
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## Model description
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This model is fine-tuned on Nifty 30-minute interval close prices from 2023-06-30T09:15:00+05:30 to 2023-09-08T15:29:00+05:30 IST. To predict the next 30-minute direction, I made a dataset where each sample contains 32 elements of z-score peak detection, which is obtained from https://stackoverflow.com/questions/22583391/peak-signal-detection-in-realtime-timeseries-data. While predicting from it, I tend to provide the last 32 elements of peak detection signal, separating them with spaces, e.g., -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0. This is currently under development and does not guarantee optimal results.
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## Intended uses & limitations
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More information needed
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