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:
- 4130bb59869cf642381479360547c23691a85a8449321c9d8b5ce39f03fd6c31
- Size of remote file:
- 134 MB
- SHA256:
- 9c6dd80bf4b383e4114f9a00c605f4e16eefe30aa2b866402d446a2a41759ba8
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