Instructions to use PleIAs/Topical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PleIAs/Topical with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("PleIAs/Topical") model = AutoModelForSeq2SeqLM.from_pretrained("PleIAs/Topical") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: t5-small | |
| language: | |
| - en | |
| - fr | |
| - de | |
| - es | |
| **Topical** is a small language model specialized for topic extraction. Given a document Pleias-Topic-Deduction will return a main topic that can be used for further downstream tasks (annotation, embedding indexation) | |
| Like other model from PleIAs Bad Data Toolbox, Topical has been volontarily trained on 70,000 documents extracted from Common Corpus with a various range of digitization artifact. | |
| Topical is a lightweight model (70 million parameters) tha can be especially used for classification at scale on a large corpus. | |
| ## Example |