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
File size: 429 Bytes
0909d27 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"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"
} |