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TRACER Banking77 Traces

Pre-computed traces and BGE-M3 embeddings for the Banking77 intent classification dataset, formatted for use with 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

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 (1024-dim, L2-normalized).

Source

Banking77 is a 77-class intent detection dataset from PolyAI. Teacher labels were generated by GPT-5.

License

MIT

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