Text Classification
Transformers
PyTorch
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use madmancity/loadedbert3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madmancity/loadedbert3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="madmancity/loadedbert3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("madmancity/loadedbert3") model = AutoModelForSequenceClassification.from_pretrained("madmancity/loadedbert3") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 49590119598
- CO2 Emissions (in grams): 0.0015
Validation Metrics
- Loss: 0.247
- Accuracy: 0.900
- Precision: 0.917
- Recall: 0.880
- AUC: 0.957
- F1: 0.898
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/madmancity/autotrain-loadedbert3-49590119598
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("madmancity/autotrain-loadedbert3-49590119598", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("madmancity/autotrain-loadedbert3-49590119598", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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