Text Classification
Transformers
PyTorch
Enawené-Nawé
bert
Trained with AutoTrain
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ziadA123/trainModel_p1")
model = AutoModelForSequenceClassification.from_pretrained("ziadA123/trainModel_p1")Quick Links
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 3672198102
- CO2 Emissions (in grams): 0.0093
Validation Metrics
- Loss: 0.112
- Accuracy: 0.972
- Precision: 0.964
- Recall: 0.980
- AUC: 0.990
- F1: 0.972
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/ziadA123/autotrain-test_prepreocessing2-3672198102
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ziadA123/autotrain-test_prepreocessing2-3672198102", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ziadA123/autotrain-test_prepreocessing2-3672198102", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ziadA123/trainModel_p1")