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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from livinNector/indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12: direct link, hf CLI and curl.
- Browser
- Download file 758 Bytes
-
https://huggingface.co/livinNector/indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12/resolve/main/config.json
- Command line
-
hf download hf://livinNector/indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12/config.json
-
curl -L -o config.json https://huggingface.co/livinNector/indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12/resolve/main/config.json
758 Bytes
| { | |
| "_name_or_path": "microsoft/Multilingual-MiniLM-L12-H384", | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.45.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 250037 | |
| } | |