Text Classification
Transformers
Safetensors
bert
scibert
data-paper-classification
scholarly-papers
binary-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use zehralx/scibert-data-paper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zehralx/scibert-data-paper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zehralx/scibert-data-paper")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zehralx/scibert-data-paper") model = AutoModelForSequenceClassification.from_pretrained("zehralx/scibert-data-paper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model": "scibert", | |
| "base_model": "allenai/scibert_scivocab_uncased", | |
| "continued_training": true, | |
| "initial_epochs": 5, | |
| "continue_epochs": 3, | |
| "initial_lr": 2e-05, | |
| "continue_lr": 5e-06, | |
| "edge_weight": 5, | |
| "label_smoothing": 0.1, | |
| "sample_ratio": 0.3, | |
| "fp16": true, | |
| "batch_size": 24, | |
| "edge_cases_before": 37, | |
| "edge_cases_after": 38, | |
| "edge_cases_total": 38, | |
| "timestamp": "2026-02-10T00:20:39.488330" | |
| } |