Instructions to use Abhi964/MahaPhrase_IndicBERT_Finetuning_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhi964/MahaPhrase_IndicBERT_Finetuning_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abhi964/MahaPhrase_IndicBERT_Finetuning_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abhi964/MahaPhrase_IndicBERT_Finetuning_3") model = AutoModelForSequenceClassification.from_pretrained("Abhi964/MahaPhrase_IndicBERT_Finetuning_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9d74378f348281dbc19046f614a1588d0a9f462919bad0f09ac7e71722125e9
- Size of remote file:
- 134 MB
- SHA256:
- c9312dbb2de3cfc5d26e8da7834efda67f1f29c953ea8e0b3be6eb3246ea68b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.