Text Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use wendys-llc/creepy-wapo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wendys-llc/creepy-wapo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wendys-llc/creepy-wapo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wendys-llc/creepy-wapo") model = AutoModelForSequenceClassification.from_pretrained("wendys-llc/creepy-wapo") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 38907102177
- CO2 Emissions (in grams): 0.0029
Validation Metrics
- Loss: 0.101
- Accuracy: 0.985
- Precision: 1.000
- Recall: 0.750
- AUC: 0.988
- F1: 0.857
Usage
You can use cURL to access this model:
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' /static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fmodels%2Fwendys-llc%2Fautotrain-creepy-wapo-38907102177
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("wendys-llc/autotrain-creepy-wapo-38907102177", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("wendys-llc/autotrain-creepy-wapo-38907102177", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Downloads last month
- 6