palakagl/distilbert_MultiClass_TextClassification

1---2tags: autotrain3language: en4widget:5- text: "I love AutoTrain 🤗"6datasets:7- palakagl/autotrain-data-PersonalAssitant8co2_eq_emissions: 2.2583634918293829---10 11# Model Trained Using AutoTrain12 13- Problem type: Multi-class Classification14- Model ID: 71722178115- CO2 Emissions (in grams): 2.25836349182938216 17## Validation Metrics18 19- Loss: 0.3866031467914581320- Accuracy: 0.904208194905869321- Macro F1: 0.907920029513109422- Micro F1: 0.904208194905869223- Weighted F1: 0.905276673096351224- Macro Precision: 0.911610166408750825- Micro Precision: 0.904208194905869326- Weighted Precision: 0.909768051445617527- Macro Recall: 0.908024600293630128- Micro Recall: 0.904208194905869329- Weighted Recall: 0.904208194905869330 31 32## Usage33 34You can use cURL to access this model:35 36```37$ 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/palakagl/autotrain-PersonalAssitant-71722178138```39 40Or Python API:41 42```43from transformers import AutoModelForSequenceClassification, AutoTokenizer44 45model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221781", use_auth_token=True)46 47tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221781", use_auth_token=True)48 49inputs = tokenizer("I love AutoTrain", return_tensors="pt")50 51outputs = model(**inputs)52```