---
license: cc-by-4.0
tags:
- embeddings
- dialogue
- sentence-transformers
---
# Turn embeddings for Taskmaster (Dialog2Flow encoder)

One 768-d float16 vector per utterance of **Taskmaster-1/2/3 (Google Research)**, computed with the frozen
encoder [sergioburdisso/dialog2flow-joint-bert-base](https://huggingface.co/sergioburdisso/dialog2flow-joint-bert-base)
(SentenceTransformer recipe, `convert_to_numpy`, no normalization). The encoder
truncates inputs at 64 tokens. If you use these embeddings, please cite the
Dialog2Flow paper (Burdisso et al., EMNLP 2024) and the source corpus.

## Files
- `taskmaster_e_t.f16.npy` — numpy array `(n_turns, 768)`, float16; row *i* is turn *i*.
- `taskmaster_dialogs.slim.pkl` — pandas DataFrame aligned row-by-row with the array:
  columns `dataset, split, dialogue_id, turn_id, speaker` (no utterance text).
- `taskmaster_e_t.f16.npy.meta.json` — encoding metadata.

Utterance text is **not** redistributed; recover it from the source corpus
(https://github.com/google-research-datasets/Taskmaster, license `cc-by-4.0`) joining on the row order defined by the slim frame.
TM-1, TM-2 and TM-3 combined; speaker roles mapped by name (USER/ASSISTANT).

## Load
```python
import numpy as np, pandas as pd
emb = np.load("taskmaster_e_t.f16.npy", mmap_mode="r")
meta = pd.read_pickle("taskmaster_dialogs.slim.pkl")
assert len(meta) == len(emb)
```