| --- |
| 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) |
| ``` |
|
|
