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README.md

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+---
+dataset_info:
+  features:
+    - name: input
+      dtype: string
+    - name: teacher
+      dtype: string
+  splits:
+    - name: train
+      num_examples: 10003
+    - name: test
+      num_examples: 3080
+license: mit
+task_categories:
+  - text-classification
+language:
+  - en
+tags:
+  - tracer
+  - banking77
+  - intent-classification
+  - llm-routing
+  - embeddings
+pretty_name: TRACER Banking77 Traces
+size_categories:
+  - 10K<n<100K
+---
+
+# TRACER Banking77 Traces
+
+Pre-computed traces and BGE-M3 embeddings for the [Banking77](https://huggingface.co/datasets/PolyAI/banking77) intent classification dataset, formatted for use with [TRACER](https://github.com/adrida/tracer).
+
+## Files
+
+| File | Size | Description |
+|------|------|-------------|
+| `banking77_traces.jsonl` | 2.1 MB | 10,003 traces. Each line: `{"input": "...", "teacher": "label"}` |
+| `banking77_embeddings.npy` | 39 MB | `(10003, 1024)` float32 -- BGE-M3 embeddings for train traces |
+| `banking77_test_embeddings.npy` | 12 MB | `(3080, 1024)` float32 -- BGE-M3 embeddings for test set |
+
+## Usage with TRACER
+
+```python
+from huggingface_hub import hf_hub_download
+import numpy as np
+import tracer
+
+traces = hf_hub_download("adamrida/tracer-banking77", "banking77_traces.jsonl", repo_type="dataset")
+X = np.load(hf_hub_download("adamrida/tracer-banking77", "banking77_embeddings.npy", repo_type="dataset"))
+
+result = tracer.fit(traces, embeddings=X)
+print(f"Coverage: {result.manifest.coverage_cal:.1%}")
+```
+
+## Embedding model
+
+All embeddings were computed with [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) (1024-dim, L2-normalized).
+
+## Source
+
+Banking77 is a 77-class intent detection dataset from [PolyAI](https://github.com/PolyAI-LDN/task-specific-datasets). Teacher labels were generated by GPT-5.
+
+## License
+
+MIT