| --- |
| license: other |
| license_name: chalearn-fi-derived |
| license_link: https://chalearnlap.cvc.uab.cat/dataset/24/description/ |
| pretty_name: PersonalityLinMulT — FI Extracted Features (fi.h5) |
| tags: |
| - personality |
| - big-five |
| - multimodal |
| - affective-computing |
| - feature-extraction |
| task_categories: |
| - audio-classification |
| - video-classification |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # PersonalityLinMulT — First Impressions V2 extracted features (`fi.h5`) |
|
|
| <!-- |
| This is the source of truth for the Hugging Face dataset card at |
| https://huggingface.co/datasets/fodorad/personalitylinmult-fi |
| Keep the two in sync (see `make push-hf-card`). The published card adds a YAML |
| frontmatter block (license/tags/size) that HF renders; this docs copy omits it. |
| --> |
|
|
| Pre-extracted multimodal features for the **First Impressions V2 (FI)** dataset, |
| packaged as a single HDF5 file for training the models in |
| [**PersonalityLinMulT**](https://github.com/fodorad/PersonalityLinMulT): |
| Big Five personality perception and interview-variable (hireability) estimation. |
|
|
| > ⚠️ **These are derived features (embeddings), not the source videos.** |
| > **No raw ChaLearn First Impressions video, audio, or frames are re-shared |
| > here.** This file contains only numerical feature vectors extracted from the |
| > official dataset. To obtain the raw videos, register for and download the |
| > dataset from the [official ChaLearn LAP |
| > release](https://chalearnlap.cvc.uab.cat/dataset/24/description/) under its |
| > own license terms. |
|
|
| ## What this is for |
|
|
| Training reads this file **and nothing else** — it is the reproducibility |
| boundary for the project. Pull it and train immediately, with no need to obtain |
| the raw videos or re-run the multi-hour GPU feature extraction: |
|
|
| ```bash |
| # In a clone of https://github.com/fodorad/PersonalityLinMulT |
| make pull-fi-h5 # downloads fi.h5 into data/processed/FI/h5/ |
| make train-fi-bigfive # train Big Five regression |
| ``` |
|
|
| Or download directly: |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| path = hf_hub_download( |
| "fodorad/personalitylinmult-fi", "fi.h5", repo_type="dataset" |
| ) |
| ``` |
|
|
| ## Contents |
|
|
| `fi.h5` — 10 000 clips (6000 train / 2000 valid / 2000 test), ~33 GB, `float16`. |
| Row-indexed and feature-major: `__getitem__(i)` reads exactly one chunk per |
| feature. Every sequence feature carries a `mask` (`True` = the protagonist was |
| detected / a real token) and a `length`. |
|
|
| | feature | modality | source model | shape `(N, T, D)` | |
| |---|---|---|---| |
| | `wavlm` | acoustic | WavLM Base+ (layer 9) | `(10000, 750, 768)` | |
| | `emotion2vec` | acoustic | emotion2vec+ | `(10000, 750, 768)` | |
| | `dinov2` | visual | DINOv2 Base (full frame) | `(10000, 375, 768)` | |
| | `emotieffnet` | visual | EmotiEffNet (EfficientNet-B0) | `(10000, 375, 1280)` | |
| | `farl` | visual | FaRL (ViT-B/16) | `(10000, 375, 512)` | |
| | `xlm_roberta_gt` | textual | XLM-RoBERTa tokens, ground-truth | `(10000, 128, 768)` | |
| | `mmbert_gt` | textual | mmBERT tokens, ground-truth | `(10000, 128, 768)` | |
|
|
| Visual features (`emotieffnet`, `farl`) track the **protagonist's** face; the |
| mask marks frames where the protagonist was detected. `dinov2` is dense (full |
| frame). Text is **token-level** `(T, 768)` (not pooled) for both XLM-RoBERTa and |
| mmBERT — the representation a cross-modal sequence model attends over — padded to |
| 128 tokens with the mask marking real tokens. Both are extracted over the |
| **ground-truth** transcript. |
|
|
| Labels stored per clip: the Big Five traits (`openness`, `conscientiousness`, |
| `extraversion`, `agreeableness`, `neuroticism`, and `emotional_stability` |
| `= 1 − neuroticism`) plus the `interview` variable, all in `[0, 1]`. |
|
|
| ## Provenance and reproducibility |
|
|
| The file's HDF5 root attributes embed the **builder config verbatim**, the |
| **git commit SHA** of the code that produced it (with a `-dirty` suffix if built |
| from an uncommitted tree), and the source label CSV's SHA-256 — so any copy |
| traces back to the exact recipe. To rebuild from scratch and verify: |
|
|
| ```bash |
| make pipeline-fi # decode → extract features → build fi.h5 (requires raw FI) |
| ``` |
|
|
| ## Citation |
|
|
| Features derived from the **ChaLearn First Impressions V2** dataset. If you use |
| this artifact, please cite the original dataset and the PersonalityLinMulT |
| paper: |
|
|
| > Fodor et al., *Multimodal Sentiment and Personality Perception Under Speech: |
| > A Comparison of Transformer-based Architectures.* |
| > [PMLR v173](https://proceedings.mlr.press/v173/fodor22a.html) |
|
|
| ## Links |
|
|
| - **Code / models:** https://github.com/fodorad/PersonalityLinMulT |
| - **Documentation:** https://fodorad.github.io/PersonalityLinMulT/ |
| - **Original dataset:** https://chalearnlap.cvc.uab.cat/dataset/24/description/ |
|
|
