Text Classification
Transformers
Safetensors
English
bert
arxiv
scientific-text-classification
scibert
streamlit-demo
text-embeddings-inference
Instructions to use Ian-Khalzov/article-topic-service-scibert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ian-Khalzov/article-topic-service-scibert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ian-Khalzov/article-topic-service-scibert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ian-Khalzov/article-topic-service-scibert") model = AutoModelForSequenceClassification.from_pretrained("Ian-Khalzov/article-topic-service-scibert") - Notebooks
- Google Colab
- Kaggle
| { | |
| "prepared_at_utc": "2026-04-06T15:01:43.444774+00:00", | |
| "model_name": "/home/yakh/ML/ML2/article_topic_service/artifacts/scibert_topics12_run1", | |
| "max_length": 256, | |
| "per_device_train_batch_size": 4, | |
| "per_device_eval_batch_size": 8, | |
| "gradient_accumulation_steps": 8, | |
| "num_train_epochs": 12, | |
| "learning_rate": 2e-05, | |
| "weight_decay": 0.01, | |
| "warmup_ratio": 0.1, | |
| "label_smoothing_factor": 0.05, | |
| "title_only_prob": 0.2, | |
| "early_stopping_patience": 3, | |
| "seed": 42, | |
| "resume_from_checkpoint": null, | |
| "use_bf16": true, | |
| "use_fp16": false, | |
| "taxonomy_profile": "topics12", | |
| "num_labels": 12, | |
| "label_order": [ | |
| "artificial_intelligence", | |
| "natural_language_processing", | |
| "computer_vision", | |
| "machine_learning", | |
| "computer_science_theory", | |
| "mathematics", | |
| "statistics", | |
| "electrical_engineering", | |
| "astrophysics", | |
| "condensed_matter_physics", | |
| "quantum_physics", | |
| "quantitative_biology" | |
| ] | |
| } | |