Instructions to use idjotherwise/autonlp-reading_prediction-172506 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use idjotherwise/autonlp-reading_prediction-172506 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="idjotherwise/autonlp-reading_prediction-172506")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("idjotherwise/autonlp-reading_prediction-172506") model = AutoModelForSequenceClassification.from_pretrained("idjotherwise/autonlp-reading_prediction-172506") - Notebooks
- Google Colab
- Kaggle
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Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 172506
Validation Metrics
- Loss: 0.03257797285914421
- MSE: 0.03257797285914421
- MAE: 0.14246532320976257
- R2: 0.9693824457290849
- RMSE: 0.18049369752407074
- Explained Variance: 0.9699198007583618
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 AutoNLP"}' /static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fmodels%2Fidjotherwise%2Fautonlp-reading_prediction-172506
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("idjotherwise/autonlp-reading_prediction-172506")
tokenizer = AutoTokenizer.from_pretrained("idjotherwise/autonlp-reading_prediction-172506")
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
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