Text Classification
Transformers
Safetensors
English
electra
biomedical
topic-classification
openalex
scientific-papers
Instructions to use jimnoneill/paper-to-field with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jimnoneill/paper-to-field with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jimnoneill/paper-to-field")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jimnoneill/paper-to-field") model = AutoModelForSequenceClassification.from_pretrained("jimnoneill/paper-to-field", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ElectraForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "embedding_size": 1024, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "Agricultural and Biological Sciences", | |
| "1": "Arts and Humanities", | |
| "2": "Biochemistry, Genetics and Molecular Biology", | |
| "3": "Business, Management and Accounting", | |
| "4": "Chemical Engineering", | |
| "5": "Chemistry", | |
| "6": "Computer Science", | |
| "7": "Decision Sciences", | |
| "8": "Earth and Planetary Sciences", | |
| "9": "Economics, Econometrics and Finance", | |
| "10": "Energy", | |
| "11": "Engineering", | |
| "12": "Environmental Science", | |
| "13": "Immunology and Microbiology", | |
| "14": "Materials Science", | |
| "15": "Mathematics", | |
| "16": "Medicine", | |
| "17": "Neuroscience", | |
| "18": "Nursing", | |
| "19": "Pharmacology, Toxicology and Pharmaceutics", | |
| "20": "Physics and Astronomy", | |
| "21": "Psychology", | |
| "22": "Social Sciences", | |
| "23": "Veterinary", | |
| "24": "Dentistry", | |
| "25": "Health Professions" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "Agricultural and Biological Sciences": 0, | |
| "Arts and Humanities": 1, | |
| "Biochemistry, Genetics and Molecular Biology": 2, | |
| "Business, Management and Accounting": 3, | |
| "Chemical Engineering": 4, | |
| "Chemistry": 5, | |
| "Computer Science": 6, | |
| "Decision Sciences": 7, | |
| "Dentistry": 24, | |
| "Earth and Planetary Sciences": 8, | |
| "Economics, Econometrics and Finance": 9, | |
| "Energy": 10, | |
| "Engineering": 11, | |
| "Environmental Science": 12, | |
| "Health Professions": 25, | |
| "Immunology and Microbiology": 13, | |
| "Materials Science": 14, | |
| "Mathematics": 15, | |
| "Medicine": 16, | |
| "Neuroscience": 17, | |
| "Nursing": 18, | |
| "Pharmacology, Toxicology and Pharmaceutics": 19, | |
| "Physics and Astronomy": 20, | |
| "Psychology": 21, | |
| "Social Sciences": 22, | |
| "Veterinary": 23 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "electra", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "summary_activation": "gelu", | |
| "summary_last_dropout": 0.1, | |
| "summary_type": "first", | |
| "summary_use_proj": true, | |
| "transformers_version": "4.57.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 28895 | |
| } | |