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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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 | |