Text Classification
Transformers
Safetensors
Russian
bert
intent-classification
russian
aitu
text-embeddings-inference
Instructions to use govnejri/aitu-intent-rubert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use govnejri/aitu-intent-rubert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="govnejri/aitu-intent-rubert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("govnejri/aitu-intent-rubert-base-cased") model = AutoModelForSequenceClassification.from_pretrained("govnejri/aitu-intent-rubert-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "directionality": "bidi", | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "advisor_contact", | |
| "1": "appeal_grade", | |
| "2": "assignment_deadline", | |
| "3": "assignment_requirements", | |
| "4": "canteen_info", | |
| "5": "check_grades", | |
| "6": "course_materials", | |
| "7": "dormitory_info", | |
| "8": "exam_format", | |
| "9": "exam_room", | |
| "10": "exam_rules", | |
| "11": "exam_schedule", | |
| "12": "goodbye", | |
| "13": "gpa_question", | |
| "14": "grade_formula", | |
| "15": "greeting", | |
| "16": "human_operator", | |
| "17": "id_card_problem", | |
| "18": "lesson_cancelled", | |
| "19": "lesson_room", | |
| "20": "lesson_time", | |
| "21": "library_info", | |
| "22": "military_office", | |
| "23": "missed_assignment", | |
| "24": "moodle_problem", | |
| "25": "online_or_offline", | |
| "26": "out_of_scope", | |
| "27": "payment_deadline", | |
| "28": "payment_receipt", | |
| "29": "registrar_office", | |
| "30": "retake_exam", | |
| "31": "retake_grade", | |
| "32": "schedule_group", | |
| "33": "schedule_teacher", | |
| "34": "schedule_today", | |
| "35": "schedule_tomorrow", | |
| "36": "schedule_week", | |
| "37": "scholarship_lost", | |
| "38": "scholarship_threshold", | |
| "39": "student_certificate", | |
| "40": "submit_assignment", | |
| "41": "thanks", | |
| "42": "transcript_request", | |
| "43": "tuition_payment", | |
| "44": "wifi_problem" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "label2id": { | |
| "advisor_contact": 0, | |
| "appeal_grade": 1, | |
| "assignment_deadline": 2, | |
| "assignment_requirements": 3, | |
| "canteen_info": 4, | |
| "check_grades": 5, | |
| "course_materials": 6, | |
| "dormitory_info": 7, | |
| "exam_format": 8, | |
| "exam_room": 9, | |
| "exam_rules": 10, | |
| "exam_schedule": 11, | |
| "goodbye": 12, | |
| "gpa_question": 13, | |
| "grade_formula": 14, | |
| "greeting": 15, | |
| "human_operator": 16, | |
| "id_card_problem": 17, | |
| "lesson_cancelled": 18, | |
| "lesson_room": 19, | |
| "lesson_time": 20, | |
| "library_info": 21, | |
| "military_office": 22, | |
| "missed_assignment": 23, | |
| "moodle_problem": 24, | |
| "online_or_offline": 25, | |
| "out_of_scope": 26, | |
| "payment_deadline": 27, | |
| "payment_receipt": 28, | |
| "registrar_office": 29, | |
| "retake_exam": 30, | |
| "retake_grade": 31, | |
| "schedule_group": 32, | |
| "schedule_teacher": 33, | |
| "schedule_today": 34, | |
| "schedule_tomorrow": 35, | |
| "schedule_week": 36, | |
| "scholarship_lost": 37, | |
| "scholarship_threshold": 38, | |
| "student_certificate": 39, | |
| "submit_assignment": 40, | |
| "thanks": 41, | |
| "transcript_request": 42, | |
| "tuition_payment": 43, | |
| "wifi_problem": 44 | |
| }, | |
| "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", | |
| "problem_type": "single_label_classification", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.12.1", | |
| "type_vocab_size": 2, | |
| "use_cache": false, | |
| "vocab_size": 119547 | |
| } | |