Instructions to use livinNector/indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use livinNector/indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="livinNector/indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("livinNector/indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12") model = AutoModelForSequenceClassification.from_pretrained("livinNector/indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72d1d90e5c9d43772d4cae50ff6056b4f88b5a8e5ab3b0b4ea6bbd1c53031ec7
- Size of remote file:
- 471 MB
- SHA256:
- 01511a6e89ea33d7585e564bf220682b516ecdbc71b2624f47750b27eb956905
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