Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use Sifter/product-classify-name-uom-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sifter/product-classify-name-uom-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sifter/product-classify-name-uom-v2")# 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") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - autotrain | |
| - text-classification | |
| base_model: google-bert/bert-base-uncased | |
| widget: | |
| - text: "I love AutoTrain" | |
| # 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 | |