Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use roy2231/Kazbrekker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roy2231/Kazbrekker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="roy2231/Kazbrekker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("roy2231/Kazbrekker") model = AutoModelForSequenceClassification.from_pretrained("roy2231/Kazbrekker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.33374306559562683
f1_macro: 0.9216916996963803
f1_micro: 0.9220727053625226
f1_weighted: 0.9221657143338037
precision_macro: 0.9223457154268401
precision_micro: 0.9220727053625226
precision_weighted: 0.9225224467178239
recall_macro: 0.9212914584806995
recall_micro: 0.9220727053625226
recall_weighted: 0.9220727053625226
accuracy: 0.9220727053625226
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Model tree for roy2231/Kazbrekker
Base model
google-bert/bert-base-uncased