Text Classification
Transformers
TensorBoard
Safetensors
modernbert
Trained with AutoTrain
text-embeddings-inference
Instructions to use MaxPilg/ThesisBertN1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaxPilg/ThesisBertN1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MaxPilg/ThesisBertN1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MaxPilg/ThesisBertN1") model = AutoModelForSequenceClassification.from_pretrained("MaxPilg/ThesisBertN1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.12418725341558456
f1_macro: 0.9621447231119773
f1_micro: 0.9620462046204621
f1_weighted: 0.9621447231119773
precision_macro: 0.9626325130833898
precision_micro: 0.9620462046204621
precision_weighted: 0.96263251308339
recall_macro: 0.962046204620462
recall_micro: 0.9620462046204621
recall_weighted: 0.9620462046204621
accuracy: 0.9620462046204621
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Model tree for MaxPilg/ThesisBertN1
Base model
answerdotai/ModernBERT-base