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--- |
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datasets: |
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- 0xMaka/trading-candles-subset-sc-format |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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widget: |
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- text: 'identify candle: 17284.58,17264.41,17284.58,17264.41' |
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example_title: Bear |
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- text: 'identify candle: open: 17343.43, close: 17625.18, high: 17804.68, low: 17322.15' |
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example_title: Bull |
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license: gpl |
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--- |
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# Based Bert for sequence classification |
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This model is a POC and shouldn't be used for any production task. |
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## Model description |
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Based Bert SC is a text classification bot for binary classification of a trading candles opening and closing prices. |
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## Uses and limitations |
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This model can reliably return the bullish or bearish status of a candle given the opening, closing, high and low, in a format shown. |
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It will have trouble if the order of the numbers change (even if tags are included). |
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### How to use |
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You can use this model directly with a pipeline |
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```python |
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>>> from transformers import pipeline |
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>>> pipe = pipeline("text-classification", model="0xMaka/based-bert-sc") |
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>>> text = "identify candle: open: 21788.19, close: 21900, high: 21965.23, low: 21788.19" |
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>>> pipe(text) |
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[{'label': 'Bullish', 'score': 0.9999682903289795}] |
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``` |
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## Finetuning |
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For parameters: https://github.com/0xMaka/based-bert-sc/blob/main/trainer.py |
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This model was fine tuned on an RTX-3060-Mobile |
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``` |
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// BUS_WIDTH = 192 |
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// CLOCK_RATE = 1750 |
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// DDR_MULTI = 8 // DDR6 |
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// BWTheoretical = (((CLOCK_RATE * (10 ** 6)) * (BUS_WIDTH/8)) * DDR_MULI) / (10 ** 9) |
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// BWTheoretical == 336 GB/s |
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``` |
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Self-measured effective (GB/s): 316.280736 |
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