Text Classification
Transformers
Safetensors
English
multimodal
image-classification
distilbert
vit
gated-fusion
digital-humanities
Instructions to use xablex/prosody_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xablex/prosody_models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xablex/prosody_models")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xablex/prosody_models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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957fc66 c81f977 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ---
license: mit
language:
- en
library_name: transformers
tags:
- multimodal
- text-classification
- image-classification
- distilbert
- vit
- gated-fusion
- digital-humanities
---
# Prosody Page Classifiers
Three finetuned models for binary page classification in the
[Princeton Prosody Archive](https://prosody.princeton.edu/) corpus. Labels are
`TU` (0) and `non-TU` (1). Each subfolder is independently loadable
— use whichever modality you have inputs for.
| Folder | Model |
|--------|-------|
| `distilbert-text/` | DistilBERT text classifier (text-only) |
| `vit-image/` | ViT-base image classifier (image-only) |
| `gated-fusion/` | Gated-fusion multimodal classifier (text + image) |
- **`distilbert-text/`** and **`vit-image/`** are standard Hugging Face repos
(`AutoModelForSequenceClassification` / `AutoModelForImageClassification`).
- **`gated-fusion/`** is a custom multimodal model; load it via the bundled
`modeling_gatedfusion.py` (see that folder's README).
See each subfolder's `README.md` for a copy-paste usage snippet.
|