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---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- palakagl/autotrain-data-PersonalAssitant
co2_eq_emissions: 0.014567637985425905
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221783
- CO2 Emissions (in grams): 0.014567637985425905
## Validation Metrics
- Loss: 0.38848456740379333
- Accuracy: 0.9180509413067552
- Macro F1: 0.9157418163085091
- Micro F1: 0.9180509413067552
- Weighted F1: 0.9185290137253468
- Macro Precision: 0.9189981206383326
- Micro Precision: 0.9180509413067552
- Weighted Precision: 0.9221607328493303
- Macro Recall: 0.9158232837734661
- Micro Recall: 0.9180509413067552
- Weighted Recall: 0.9180509413067552
## 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/palakagl/autotrain-PersonalAssitant-717221783
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221783", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221783", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |