Text Classification
Transformers
TensorBoard
Safetensors
Persian
bert
Trained with AutoTrain
text-embeddings-inference
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="papooabedini/testing")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("papooabedini/testing")
model = AutoModelForSequenceClassification.from_pretrained("papooabedini/testing")Quick Links
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.07203829288482666
f1_macro: 0.988301685943485
f1_micro: 0.9868173258003766
f1_weighted: 0.9867815660378358
precision_macro: 0.9884888715707831
precision_micro: 0.9868173258003766
precision_weighted: 0.9869930752212238
recall_macro: 0.9883333333333333
recall_micro: 0.9868173258003766
recall_weighted: 0.9868173258003766
accuracy: 0.9868173258003766
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Model tree for papooabedini/testing
Base model
HooshvareLab/bert-fa-base-uncased
# Gated model: Login with a HF token with gated access permission hf auth login