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": { | |
| "alpha": 8, | |
| "architecture": "lora", | |
| "attn_matrices": [ | |
| "q", | |
| "v" | |
| ], | |
| "composition_mode": "add", | |
| "dropout": 0.1, | |
| "init_weights": "lora", | |
| "intermediate_lora": false, | |
| "leave_out": [], | |
| "output_lora": false, | |
| "r": 12, | |
| "selfattn_lora": true, | |
| "use_gating": false | |
| }, | |
| "config_id": "a0c8452a4cfb970e", | |
| "hidden_size": 768, | |
| "model_class": "BertAdapterModel", | |
| "model_name": "ai4bharat/IndicBERTv2-MLM-only", | |
| "model_type": "bert", | |
| "name": "tam_mal_ai_aw_classification_adapter", | |
| "version": "adapters.1.0.1" | |
| } |