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
| # Prosody DistilBERT Text Classifier | |
| Fine-tuned `distilbert-base-uncased` for binary page classification in the | |
| [Princeton Prosody Archive](https://prosody.princeton.edu/) corpus, using page | |
| **text** (OCR transcription) only. | |
| - **Classes:** `TU` (0), `non-TU` (1) | |
| - **Architecture:** `DistilBertForSequenceClassification` (HF-native) | |
| ```python | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| import torch | |
| model = AutoModelForSequenceClassification.from_pretrained("./distilbert-text") | |
| tok = AutoTokenizer.from_pretrained("./distilbert-text") | |
| enc = tok("a line of verse ...", truncation=True, max_length=512, return_tensors="pt") | |
| with torch.no_grad(): | |
| probs = model(**enc).logits.softmax(-1)[0] | |
| print({model.config.id2label[i]: float(p) for i, p in enumerate(probs)}) | |
| ``` | |
| `label_encoder.pkl` is the original sklearn `LabelEncoder` (`class1`->0, `class2`->1) | |
| kept for provenance. | |