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README.md

@@ -1,41 +1,53 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: task
-    dtype: string
-  - name: split
-    dtype: string
-  - name: instruction
-    dtype: string
-  - name: input
-    dtype: string
-  - name: output
-    dtype: string
-  - name: meta
-    struct:
-    - name: date
-      dtype: string
-  splits:
-  - name: train
-    num_bytes: 12367731
-    num_examples: 4290
-  - name: validation
-    num_bytes: 2739441
-    num_examples: 1095
-  - name: test
-    num_bytes: 209521
-    num_examples: 84
-  download_size: 2287297
-  dataset_size: 15316693
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+pretty_name: Bitcoin SLLM Instruction Dataset
+tags:
+- bitcoin
+- finance
+- instruction-tuning
+- sft
+license: other
+language:
+- en
+task_categories:
+- time-series-forecasting
+- text-classification
+- tabular-regression
+size_categories:
+- 1K<n<10K
 ---
+
+# Bitcoin SLLM Instruction Dataset
+
+Daily BTC context transformed into instruction-tuning triples for small LMs.
+
+## Schema
+
+- **id**: unique example id  
+- **task**: `direction_cls` | `return_reg` | `maxdd_cls`  
+- **split**: `train` | `validation` | `test` (time-based)  
+- **instruction**: natural-language task definition  
+- **input**: compact JSON string with features (technicals, macro, on-chain, text snippets)  
+- **output**: compact JSON string with target(s)  
+- **meta**: `{ "date": "YYYY-MM-DD" }`
+
+## Targets
+
+- **direction_cls**: UP (>+2%), FLAT (−2%..+2%), DOWN (<−2%) at t+10; includes `r10`.  
+- **return_reg**: 10-day log return `r10_log`.  
+- **maxdd_cls**: next-10-day max drawdown bucket (`low`/`med`/`high`) with raw `maxdd`.
+
+## Load
+
+```python
+from datasets import load_dataset
+ds = load_dataset("tahamajs/bitcoin-sllm-instruct")
+print(ds)             # DatasetDict with train/validation/test
+print(ds["train"][0]) # one example
+````
+
+## Notes
+
+* Text is truncated to short snippets to keep token usage low.
+* Normalize numeric features at train time using **train** split stats only.
+* Review licensing of underlying sources; this repo may be private.
+