Text Classification
Transformers
Safetensors
Russian
xlm-roberta
russian-nlp
grnti
multiclass
Eval Results (legacy)
text-embeddings-inference
Instructions to use kiselyovd/grnti-text-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kiselyovd/grnti-text-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kiselyovd/grnti-text-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kiselyovd/grnti-text-classifier") model = AutoModelForSequenceClassification.from_pretrained("kiselyovd/grnti-text-classifier") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "FacebookAI/xlm-roberta-base", | |
| "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": 768, | |
| "id2label": { | |
| "0": "20000: Философия", | |
| "1": "30000: История. Исторические науки", | |
| "2": "40000: Социология", | |
| "3": "60000: Экономика. Экономические науки", | |
| "4": "100000: Государство и право. Юридические науки", | |
| "5": "110000: Политика. Политические науки", | |
| "6": "130000: Культура. Культурология", | |
| "7": "140000: Народное образование. Педагогика", | |
| "8": "150000: Психология", | |
| "9": "160000: Языкознание", | |
| "10": "170000: Литература. Литературоведение. Устное народное творчество", | |
| "11": "180000: Искусство", | |
| "12": "270000: Математика", | |
| "13": "290000: Физика", | |
| "14": "310000: Химия", | |
| "15": "340000: Биология", | |
| "16": "380000: Геология", | |
| "17": "440000: Энергетика", | |
| "18": "500000: Автоматика. Вычислительная техника", | |
| "19": "520000: Горное дело", | |
| "20": "550000: Машиностроение", | |
| "21": "650000: Пищевая промышленность", | |
| "22": "670000: Строительство. Архитектура", | |
| "23": "680000: Сельское и лесное хозяйство", | |
| "24": "683500: Сельское и лесное хозяйство", | |
| "25": "730000: Транспорт", | |
| "26": "760000: Медицина и здравоохранение", | |
| "27": "770000: Физическая культура и спорт" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "20000: Философия": 0, | |
| "30000: История. Исторические науки": 1, | |
| "40000: Социология": 2, | |
| "60000: Экономика. Экономические науки": 3, | |
| "100000: Государство и право. Юридические науки": 4, | |
| "110000: Политика. Политические науки": 5, | |
| "130000: Культура. Культурология": 6, | |
| "140000: Народное образование. Педагогика": 7, | |
| "150000: Психология": 8, | |
| "160000: Языкознание": 9, | |
| "170000: Литература. Литературоведение. Устное народное творчество": 10, | |
| "180000: Искусство": 11, | |
| "270000: Математика": 12, | |
| "290000: Физика": 13, | |
| "310000: Химия": 14, | |
| "340000: Биология": 15, | |
| "380000: Геология": 16, | |
| "440000: Энергетика": 17, | |
| "500000: Автоматика. Вычислительная техника": 18, | |
| "520000: Горное дело": 19, | |
| "550000: Машиностроение": 20, | |
| "650000: Пищевая промышленность": 21, | |
| "670000: Строительство. Архитектура": 22, | |
| "680000: Сельское и лесное хозяйство": 23, | |
| "683500: Сельское и лесное хозяйство": 24, | |
| "730000: Транспорт": 25, | |
| "760000: Медицина и здравоохранение": 26, | |
| "770000: Физическая культура и спорт": 27 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.46.3", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |