Instructions to use Shehryar718/bert-educational-tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shehryar718/bert-educational-tagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shehryar718/bert-educational-tagger")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shehryar718/bert-educational-tagger") model = AutoModelForSequenceClassification.from_pretrained("Shehryar718/bert-educational-tagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,181 Bytes
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"_name_or_path": "bert-base-uncased",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "Data Structures",
"1": "Writing",
"2": "Political Science",
"3": "Geometry",
"4": "Engineering",
"5": "Philosophy",
"6": "Business Studies",
"7": "Vocabulary",
"8": "Biology",
"9": "World History",
"10": "Trigonometry",
"11": "Astronomy",
"12": "Programming",
"13": "History",
"14": "Social Studies",
"15": "Statistics",
"16": "Language Arts",
"17": "Art History",
"18": "Psychology",
"19": "Health Education",
"20": "Architecture",
"21": "Poetry",
"22": "Prose",
"23": "Earth Science",
"24": "Fiction",
"25": "Non-fiction",
"26": "American History",
"27": "Grammar",
"28": "Algorithms",
"29": "Entrepreneurship",
"30": "Sociology",
"31": "Algebra",
"32": "Technology",
"33": "Computer Science",
"34": "Marketing",
"35": "Environmental Science",
"36": "Literature",
"37": "Reading Comprehension",
"38": "Physical Education",
"39": "Calculus",
"40": "Mathematics",
"41": "Music Theory",
"42": "Current Events / News",
"43": "Finance",
"44": "Economics",
"45": "Physics",
"46": "Ethics",
"47": "Chemistry",
"48": "Geography",
"49": "European History"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"Computer Science": 0,
"Philosophy": 1,
"Algorithms": 2,
"Non-fiction": 3,
"Trigonometry": 4,
"Calculus": 5,
"Language Arts": 6,
"Writing": 7,
"Physics": 8,
"Marketing": 9,
"Art History": 10,
"History": 11,
"Geometry": 12,
"Social Studies": 13,
"Chemistry": 14,
"Geography": 15,
"Literature": 16,
"Music Theory": 17,
"Political Science": 18,
"Engineering": 19,
"Finance": 20,
"Entrepreneurship": 21,
"Algebra": 22,
"Technology": 23,
"Statistics": 24,
"Mathematics": 25,
"European History": 26,
"World History": 27,
"Reading Comprehension": 28,
"American History": 29,
"Earth Science": 30,
"Physical Education": 31,
"Ethics": 32,
"Architecture": 33,
"Data Structures": 34,
"Business Studies": 35,
"Astronomy": 36,
"Psychology": 37,
"Fiction": 38,
"Sociology": 39,
"Vocabulary": 40,
"Current Events / News": 41,
"Prose": 42,
"Environmental Science": 43,
"Poetry": 44,
"Economics": 45,
"Biology": 46,
"Health Education": 47,
"Programming": 48,
"Grammar": 49
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.38.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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