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
File size: 587 Bytes
d451bf8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"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"
} |