Text Classification
Transformers
PyTorch
Safetensors
English
deberta
Trained with AutoTrain
text-regression
Instructions to use PavloR/autotrain-ooo-81283141687 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PavloR/autotrain-ooo-81283141687 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PavloR/autotrain-ooo-81283141687")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PavloR/autotrain-ooo-81283141687") model = AutoModelForSequenceClassification.from_pretrained("PavloR/autotrain-ooo-81283141687", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 81283141687
- CO2 Emissions (in grams): 0.0191
Validation Metrics
- Loss: 0.343
- MSE: 0.343
- MAE: 0.412
- R2: 0.668
- RMSE: 0.586
- Explained Variance: 0.671
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%2FPavloR%2Fautotrain-ooo-81283141687
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("PavloR/autotrain-ooo-81283141687", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("PavloR/autotrain-ooo-81283141687", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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