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
File size: 286 Bytes
f944c67 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"bos_token": "[CLS]",
"cls_token": "[CLS]",
"eos_token": "[SEP]",
"mask_token": {
"content": "[MASK]",
"lstrip": true,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": "<pad>",
"sep_token": "[SEP]",
"unk_token": "<unk>"
}
|