Instructions to use Abhi964/Paraphrase_indicBERT_onfull_FT1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhi964/Paraphrase_indicBERT_onfull_FT1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abhi964/Paraphrase_indicBERT_onfull_FT1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abhi964/Paraphrase_indicBERT_onfull_FT1") model = AutoModelForSequenceClassification.from_pretrained("Abhi964/Paraphrase_indicBERT_onfull_FT1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- badc86ec2d84534cf806246fce0824cdb71eadca6e850a732d2c4c7cde9abfaf
- Size of remote file:
- 5.3 kB
- SHA256:
- c43c4182f9be67c044e0cba9006c90ad02ea1e465be0335feefe6377441897d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.