Text Classification
Transformers
PyTorch
Enawené-Nawé
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use Tarive/Test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tarive/Test1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tarive/Test1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tarive/Test1") model = AutoModelForSequenceClassification.from_pretrained("Tarive/Test1") - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: I love AutoTrain 🤗
datasets:
- Tarive/autotrain-data-4xes-khh9-98zg
co2_eq_emissions:
emissions: 0
Model Trained Using AutoTrain
- Problem type: Text Classification
- CO2 Emissions (in grams): 0.0000
Validation Metrics
loss: 0.688549816608429
f1: 0.7096774193548387
precision: 0.55
recall: 1.0
auc: 0.45075757575757575
accuracy: 0.55