Text Classification
Transformers
PyTorch
Safetensors
English
deberta
Trained with AutoTrain
text-regression
Instructions to use MichaelS91/autotrain-hub_testing-75008139803 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MichaelS91/autotrain-hub_testing-75008139803 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MichaelS91/autotrain-hub_testing-75008139803")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MichaelS91/autotrain-hub_testing-75008139803") model = AutoModelForSequenceClassification.from_pretrained("MichaelS91/autotrain-hub_testing-75008139803") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 75008139803
- CO2 Emissions (in grams): 1.5911
Validation Metrics
- Loss: 1.889
- MSE: 1.889
- MAE: 1.094
- R2: 0.221
- RMSE: 1.374
- Explained Variance: 0.242
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"}' https://api-inference.huggingface.co/models/MichaelS91/autotrain-hub_testing-75008139803
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("MichaelS91/autotrain-hub_testing-75008139803", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("MichaelS91/autotrain-hub_testing-75008139803", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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