Text Classification
Transformers
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/website-classifier")
model = AutoModelForSequenceClassification.from_pretrained("KnutJaegersberg/website-classifier")Quick Links
Used the website categories from URL Classification Dataset [DMOZ] https://www.kaggle.com/datasets/shawon10/url-classification-dataset-dmoz and a zero-shot-classifier on c4 sample to make a training dataset. Only a subset of the linked dataset was used.
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.45181071758270264
f1_macro: 0.9103583954158208
f1_micro: 0.9108333333333334
f1_weighted: 0.9103583954158205
precision_macro: 0.9123282026272069
precision_micro: 0.9108333333333334
precision_weighted: 0.9123282026272069
recall_macro: 0.9108333333333334
recall_micro: 0.9108333333333334
recall_weighted: 0.9108333333333334
accuracy: 0.9108333333333334
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KnutJaegersberg/website-classifier")