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
| { | |
| "config": { | |
| "activation_function": "gelu", | |
| "bias": true, | |
| "embedding_size": 768, | |
| "head_type": "masked_lm", | |
| "label2id": null, | |
| "layer_norm": true, | |
| "layers": 2, | |
| "shift_labels": false, | |
| "vocab_size": 250000 | |
| }, | |
| "hidden_size": 768, | |
| "model_class": "BertAdapterModel", | |
| "model_name": "ai4bharat/IndicBERTv2-MLM-only", | |
| "model_type": "bert", | |
| "name": "default", | |
| "version": "adapters.1.0.1" | |
| } |