Text Classification
Transformers
Safetensors
Indonesian
bert
indobert
intent-classification
text-embeddings-inference
Instructions to use Firdania/sindesa-indobert-intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Firdania/sindesa-indobert-intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Firdania/sindesa-indobert-intent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Firdania/sindesa-indobert-intent") model = AutoModelForSequenceClassification.from_pretrained("Firdania/sindesa-indobert-intent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,409 Bytes
d0fe243 | 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | {
"_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": "oos",
"1": "tanya_durasi",
"2": "tanya_informasi",
"3": "tanya_jadwal",
"4": "tanya_kendala",
"5": "tanya_lokasi",
"6": "tanya_pembaruan",
"7": "tanya_pembayaran",
"8": "tanya_prosedur",
"9": "tanya_status",
"10": "tanya_syarat"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"oos": 0,
"tanya_durasi": 1,
"tanya_informasi": 2,
"tanya_jadwal": 3,
"tanya_kendala": 4,
"tanya_lokasi": 5,
"tanya_pembaruan": 6,
"tanya_pembayaran": 7,
"tanya_prosedur": 8,
"tanya_status": 9,
"tanya_syarat": 10
},
"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",
"transformers_version": "4.57.6",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 50000
}
|