Text Classification
Transformers
PyTorch
Safetensors
Enawené-Nawé
bert
Trained with AutoTrain
text-embeddings-inference
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="GRPUI/sgugit-model")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("GRPUI/sgugit-model")
model = AutoModelForSequenceClassification.from_pretrained("GRPUI/sgugit-model")Quick Links
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 92034144745
- CO2 Emissions (in grams): 1.0135
Validation Metrics
- Loss: 0.027
- Accuracy: 0.997
- Macro F1: 0.989
- Micro F1: 0.997
- Weighted F1: 0.997
- Macro Precision: 0.991
- Micro Precision: 0.997
- Weighted Precision: 0.997
- Macro Recall: 0.989
- Micro Recall: 0.997
- Weighted Recall: 0.997
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/GRPUI/autotrain-sgugit-model-v4-92034144745
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("GRPUI/autotrain-sgugit-model-v4-92034144745", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("GRPUI/autotrain-sgugit-model-v4-92034144745", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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