Instructions to use l3cube-pune/marathi-paraphrase-detection-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-paraphrase-detection-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/marathi-paraphrase-detection-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-paraphrase-detection-bert") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/marathi-paraphrase-detection-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add Transformers library tag, paper link, GitHub link, and usage details
#1
by nielsr HF Staff - opened
This PR enhances the model card for the MahaParaphrase-BERT model by:
- Adding
library_name: transformersto the metadata, which enables proper integration and displays an auto-generated usage snippet on the model page, as the model's configuration indicates compatibility with thetransformerslibrary. - Updating the paper link to the official publication: MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models.
- Adding an explicit link to the L3Cube-MahaNLP GitHub repository for code and resources.
- Including a "Usage" section that directs users to the GitHub repository and an associated Colab notebook for practical examples.
- Updating the citation section to refer to the specific paper for this model.
Please review and merge this PR if these improvements are acceptable.
l3cube-pune changed pull request status to merged