Instructions to use adhityaprimandhika/fine-tuned-bge-category-by-notes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adhityaprimandhika/fine-tuned-bge-category-by-notes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="adhityaprimandhika/fine-tuned-bge-category-by-notes")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("adhityaprimandhika/fine-tuned-bge-category-by-notes") model = AutoModelForSequenceClassification.from_pretrained("adhityaprimandhika/fine-tuned-bge-category-by-notes") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "MoritzLaurer/bge-m3-zeroshot-v2.0-c", | |
| "architectures": [ | |
| "XLMRobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "Uang Keluar", | |
| "1": "Tabungan & Investasi", | |
| "10": "Biaya & Lainnya", | |
| "11": "Hobi & Gaya Hidup", | |
| "12": "Perawatan Diri", | |
| "13": "Kesehatan", | |
| "14": "Pendidikan", | |
| "15": "Uang Masuk", | |
| "16": "Gaji", | |
| "17": "Pencairan Investasi", | |
| "18": "Bunga", | |
| "19": "Refund", | |
| "2": "Pinjaman", | |
| "20": "Pencairan Pinjaman", | |
| "21": "Cashback", | |
| "3": "Tagihan", | |
| "4": "Hadiah & Amal", | |
| "5": "Transportasi", | |
| "6": "Belanja", | |
| "7": "Top Up", | |
| "8": "Hiburan", | |
| "9": "Makanan & Minuman" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "Belanja": 6, | |
| "Biaya & Lainnya": 10, | |
| "Bunga": 18, | |
| "Cashback": 21, | |
| "Gaji": 16, | |
| "Hadiah & Amal": 4, | |
| "Hiburan": 8, | |
| "Hobi & Gaya Hidup": 11, | |
| "Kesehatan": 13, | |
| "Makanan & Minuman": 9, | |
| "Pencairan Investasi": 17, | |
| "Pencairan Pinjaman": 20, | |
| "Pendidikan": 14, | |
| "Perawatan Diri": 12, | |
| "Pinjaman": 2, | |
| "Refund": 19, | |
| "Tabungan & Investasi": 1, | |
| "Tagihan": 3, | |
| "Top Up": 7, | |
| "Transportasi": 5, | |
| "Uang Keluar": 0, | |
| "Uang Masuk": 15 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 8194, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.42.2", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |