Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Sifter/product-classify-name-uom-v2")
model = AutoModelForSequenceClassification.from_pretrained("Sifter/product-classify-name-uom-v2", device_map="auto")Quick Links
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.5061441659927368
f1_macro: 0.6556271855575035
f1_micro: 0.8693012755828093
f1_weighted: 0.858861582878527
precision_macro: 0.6902311301863028
precision_micro: 0.8693012755828093
precision_weighted: 0.8629469884040201
recall_macro: 0.6596235527365081
recall_micro: 0.8693012755828093
recall_weighted: 0.8693012755828093
accuracy: 0.8693012755828093
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Model tree for Sifter/product-classify-name-uom-v2
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sifter/product-classify-name-uom-v2")