Text Classification
Transformers
PyTorch
Safetensors
English
deberta
Trained with AutoTrain
text-regression
Instructions to use yl5212/autotrain-sentimentfinal-70270138111 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yl5212/autotrain-sentimentfinal-70270138111 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yl5212/autotrain-sentimentfinal-70270138111")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yl5212/autotrain-sentimentfinal-70270138111") model = AutoModelForSequenceClassification.from_pretrained("yl5212/autotrain-sentimentfinal-70270138111", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 70270138111
- CO2 Emissions (in grams): 0.2460
Validation Metrics
- Loss: 0.129
- MSE: 0.129
- MAE: 0.266
- R2: 0.006
- RMSE: 0.359
- Explained Variance: 0.006
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%2Fyl5212%2Fautotrain-sentimentfinal-70270138111
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("yl5212/autotrain-sentimentfinal-70270138111", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("yl5212/autotrain-sentimentfinal-70270138111", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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