Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use orpe42/deberta_MP_dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use orpe42/deberta_MP_dynamic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="orpe42/deberta_MP_dynamic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("orpe42/deberta_MP_dynamic") model = AutoModelForSequenceClassification.from_pretrained("orpe42/deberta_MP_dynamic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from orpe42/deberta_MP_dynamic: direct link, hf CLI and curl.
- Browser
- Download file 3.36 kB
-
https://huggingface.co/orpe42/deberta_MP_dynamic/resolve/main/config.json
- Command line
-
hf download hf://orpe42/deberta_MP_dynamic/config.json
-
curl -L -o config.json https://huggingface.co/orpe42/deberta_MP_dynamic/resolve/main/config.json
3.36 kB
| { | |
| "architectures": [ | |
| "DebertaV2ForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "1": "FOREIGN_SPECIAL_RELATIONS", | |
| "2": "ANTI_IMPERIALISM", | |
| "3": "MILITARY", | |
| "4": "PEACE", | |
| "5": "INTERNATIONALISM", | |
| "6": "EUROPEAN_INTEGRATION", | |
| "7": "FREEDOM_HUMAN_RIGHTS", | |
| "8": "DEMOCRACY_GENERAL", | |
| "9": "CONSTITUTIONALISM", | |
| "10": "FEDERALISM", | |
| "11": "GOVERNMENTAL_EFFICIENCY", | |
| "12": "POLITICAL_CORRUPTION", | |
| "13": "POLITICAL_AUTHORITY", | |
| "14": "FREE_ENTERPRISE", | |
| "15": "INCENTIVES", | |
| "16": "MARKET_REGULATION", | |
| "17": "PLANNING_PRODUCTIVITY", | |
| "18": "PROTECTIONISM", | |
| "19": "KEYNESIAN_DEMAND_MANAGEMENT", | |
| "20": "TECHNOLOGY_AND_INFRASTRUCTURE", | |
| "21": "CONTROLLED_ECONOMY", | |
| "22": "NATIONALISATION", | |
| "23": "ECONOMIC_ORTHODOXY", | |
| "24": "MARXIST_ANALYSIS", | |
| "25": "ENVIRONMENTAL_PROTECTION", | |
| "26": "CULTURE", | |
| "27": "SOCIAL_JUSTICE", | |
| "28": "WELFARE_STATE", | |
| "29": "EDUCATION", | |
| "30": "NATIONAL_WAY_OF_LIFE", | |
| "31": "TRADITIONAL_MORALITY", | |
| "32": "LAW_AND_ORDER", | |
| "33": "CIVIC_MINDEDNESS", | |
| "34": "MULTICULTURALISM", | |
| "35": "LABOUR_GROUPS_CORPORATISM", | |
| "36": "AGRICULTURE_AND_FARMERS", | |
| "37": "MIDDLE_CLASS_AND_PROFESSIONALS", | |
| "38": "MINORITY_AND_DEMOGRAPHIC_GROUPS" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "AGRICULTURE_AND_FARMERS": 36, | |
| "ANTI_IMPERIALISM": 2, | |
| "CIVIC_MINDEDNESS": 33, | |
| "CONSTITUTIONALISM": 9, | |
| "CONTROLLED_ECONOMY": 21, | |
| "CULTURE": 26, | |
| "DEMOCRACY_GENERAL": 8, | |
| "ECONOMIC_ORTHODOXY": 23, | |
| "EDUCATION": 29, | |
| "ENVIRONMENTAL_PROTECTION": 25, | |
| "EUROPEAN_INTEGRATION": 6, | |
| "FEDERALISM": 10, | |
| "FOREIGN_SPECIAL_RELATIONS": 1, | |
| "FREEDOM_HUMAN_RIGHTS": 7, | |
| "FREE_ENTERPRISE": 14, | |
| "GOVERNMENTAL_EFFICIENCY": 11, | |
| "INCENTIVES": 15, | |
| "INTERNATIONALISM": 5, | |
| "KEYNESIAN_DEMAND_MANAGEMENT": 19, | |
| "LABOUR_GROUPS_CORPORATISM": 35, | |
| "LAW_AND_ORDER": 32, | |
| "MARKET_REGULATION": 16, | |
| "MARXIST_ANALYSIS": 24, | |
| "MIDDLE_CLASS_AND_PROFESSIONALS": 37, | |
| "MILITARY": 3, | |
| "MINORITY_AND_DEMOGRAPHIC_GROUPS": 38, | |
| "MULTICULTURALISM": 34, | |
| "NATIONALISATION": 22, | |
| "NATIONAL_WAY_OF_LIFE": 30, | |
| "PEACE": 4, | |
| "PLANNING_PRODUCTIVITY": 17, | |
| "POLITICAL_AUTHORITY": 13, | |
| "POLITICAL_CORRUPTION": 12, | |
| "PROTECTIONISM": 18, | |
| "SOCIAL_JUSTICE": 27, | |
| "TECHNOLOGY_AND_INFRASTRUCTURE": 20, | |
| "TRADITIONAL_MORALITY": 31, | |
| "WELFARE_STATE": 28 | |
| }, | |
| "layer_norm_eps": 1e-07, | |
| "legacy": true, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "model_type": "deberta-v2", | |
| "norm_rel_ebd": "layer_norm", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0.0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 1024, | |
| "pos_att_type": [ | |
| "p2c", | |
| "c2p" | |
| ], | |
| "position_biased_input": false, | |
| "position_buckets": 256, | |
| "problem_type": "multi_label_classification", | |
| "relative_attention": true, | |
| "share_att_key": true, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.12.1", | |
| "type_vocab_size": 0, | |
| "use_cache": false, | |
| "vocab_size": 128100 | |
| } | |