Text Classification
Transformers
Safetensors
bert
research-library
repository-library
metadata-category-classifier
m2
t1_metadata
v2
text-embeddings-inference
Instructions to use PeytonT/metadata-category-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeytonT/metadata-category-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PeytonT/metadata-category-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PeytonT/metadata-category-classifier") model = AutoModelForSequenceClassification.from_pretrained("PeytonT/metadata-category-classifier") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "astro-ph", | |
| "1": "cond-mat.dis-nn", | |
| "2": "cond-mat.mes-hall", | |
| "3": "cond-mat.mtrl-sci", | |
| "4": "cond-mat.other", | |
| "5": "cond-mat.soft", | |
| "6": "cond-mat.stat-mech", | |
| "7": "cond-mat.str-el", | |
| "8": "cond-mat.supr-con", | |
| "9": "cs.IT", | |
| "10": "gr-qc", | |
| "11": "hep-ex", | |
| "12": "hep-lat", | |
| "13": "hep-ph", | |
| "14": "hep-th", | |
| "15": "math-ph", | |
| "16": "math.AG", | |
| "17": "math.AP", | |
| "18": "math.CO", | |
| "19": "math.DG", | |
| "20": "math.DS", | |
| "21": "math.FA", | |
| "22": "math.GT", | |
| "23": "math.NT", | |
| "24": "math.PR", | |
| "25": "math.RT", | |
| "26": "math.ST", | |
| "27": "nucl-ex", | |
| "28": "nucl-th", | |
| "29": "physics.gen-ph", | |
| "30": "physics.optics", | |
| "31": "quant-ph" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "astro-ph": 0, | |
| "cond-mat.dis-nn": 1, | |
| "cond-mat.mes-hall": 2, | |
| "cond-mat.mtrl-sci": 3, | |
| "cond-mat.other": 4, | |
| "cond-mat.soft": 5, | |
| "cond-mat.stat-mech": 6, | |
| "cond-mat.str-el": 7, | |
| "cond-mat.supr-con": 8, | |
| "cs.IT": 9, | |
| "gr-qc": 10, | |
| "hep-ex": 11, | |
| "hep-lat": 12, | |
| "hep-ph": 13, | |
| "hep-th": 14, | |
| "math-ph": 15, | |
| "math.AG": 16, | |
| "math.AP": 17, | |
| "math.CO": 18, | |
| "math.DG": 19, | |
| "math.DS": 20, | |
| "math.FA": 21, | |
| "math.GT": 22, | |
| "math.NT": 23, | |
| "math.PR": 24, | |
| "math.RT": 25, | |
| "math.ST": 26, | |
| "nucl-ex": 27, | |
| "nucl-th": 28, | |
| "physics.gen-ph": 29, | |
| "physics.optics": 30, | |
| "quant-ph": 31 | |
| }, | |
| "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", | |
| "transformers_version": "4.57.6", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 31090 | |
| } | |