Text Classification
Transformers
Safetensors
Indonesian
bert
indonesian
indobert
procurement
lpse
spse
tesis
xai
text-embeddings-inference
Instructions to use derhan/indobert-klasifikasi-alasan-tender-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use derhan/indobert-klasifikasi-alasan-tender-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="derhan/indobert-klasifikasi-alasan-tender-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("derhan/indobert-klasifikasi-alasan-tender-v2") model = AutoModelForSequenceClassification.from_pretrained("derhan/indobert-klasifikasi-alasan-tender-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from derhan/indobert-klasifikasi-alasan-tender-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.38 kB
-
https://huggingface.co/derhan/indobert-klasifikasi-alasan-tender-v2/resolve/main/config.json
- Command line
-
hf download hf://derhan/indobert-klasifikasi-alasan-tender-v2/config.json
-
curl -L -o config.json https://huggingface.co/derhan/indobert-klasifikasi-alasan-tender-v2/resolve/main/config.json
2.38 kB
| { | |
| "_num_labels": 5, | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "directionality": "bidi", | |
| "dtype": "float32", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Lainnya", | |
| "1": "X.1.1.1", | |
| "2": "X.1.1.2", | |
| "3": "X.1.1.3", | |
| "4": "X.1.1.4", | |
| "5": "X.1.1.5", | |
| "6": "X.1.2.1", | |
| "7": "X.1.2.2", | |
| "8": "X.2.1.1", | |
| "9": "X.2.1.2", | |
| "10": "X.2.1.3", | |
| "11": "X.2.1.4", | |
| "12": "X.2.1.5", | |
| "13": "X.2.1.6", | |
| "14": "X.2.1.7", | |
| "15": "X.2.1.8", | |
| "16": "X.2.2.1", | |
| "17": "X.2.2.2", | |
| "18": "X.2.2.3", | |
| "19": "X.2.3.1", | |
| "20": "X.2.3.2", | |
| "21": "X.3.1.1", | |
| "22": "X.3.1.2", | |
| "23": "X.3.1.3", | |
| "24": "X.3.2.1", | |
| "25": "X.3.2.2", | |
| "26": "X.3.3.1", | |
| "27": "X.3.3.2", | |
| "28": "X.3.4.1", | |
| "29": "X.3.4.2", | |
| "30": "X.4.1.1", | |
| "31": "X.4.2.2", | |
| "32": "X.4.2.3", | |
| "33": "X.4.3.1", | |
| "34": "X.4.4.1", | |
| "35": "X.4.4.2", | |
| "36": "X.5.1.1" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "Lainnya": 0, | |
| "X.1.1.1": 1, | |
| "X.1.1.2": 2, | |
| "X.1.1.3": 3, | |
| "X.1.1.4": 4, | |
| "X.1.1.5": 5, | |
| "X.1.2.1": 6, | |
| "X.1.2.2": 7, | |
| "X.2.1.1": 8, | |
| "X.2.1.2": 9, | |
| "X.2.1.3": 10, | |
| "X.2.1.4": 11, | |
| "X.2.1.5": 12, | |
| "X.2.1.6": 13, | |
| "X.2.1.7": 14, | |
| "X.2.1.8": 15, | |
| "X.2.2.1": 16, | |
| "X.2.2.2": 17, | |
| "X.2.2.3": 18, | |
| "X.2.3.1": 19, | |
| "X.2.3.2": 20, | |
| "X.3.1.1": 21, | |
| "X.3.1.2": 22, | |
| "X.3.1.3": 23, | |
| "X.3.2.1": 24, | |
| "X.3.2.2": 25, | |
| "X.3.3.1": 26, | |
| "X.3.3.2": 27, | |
| "X.3.4.1": 28, | |
| "X.3.4.2": 29, | |
| "X.4.1.1": 30, | |
| "X.4.2.2": 31, | |
| "X.4.2.3": 32, | |
| "X.4.3.1": 33, | |
| "X.4.4.1": 34, | |
| "X.4.4.2": 35, | |
| "X.5.1.1": 36 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "pooler_fc_size": 768, | |
| "pooler_num_attention_heads": 12, | |
| "pooler_num_fc_layers": 3, | |
| "pooler_size_per_head": 128, | |
| "pooler_type": "first_token_transform", | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "transformers_version": "4.57.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 50000 | |
| } | |