---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "INSTRUCTION:\nReview the given chart and find the outlier.\nINPUT:\nData Series A: 0, 5, 8, 10, 11, 10, 9\nOUTPUT:\nThe outlier of the given data series is 11, as it is numerically greater than the rest of the numbers in the series.\n"
datasets:
- dvilasuero/autotrain-data-alpaca-bs-detector
co2_eq_emissions:
  emissions: 0.4102361717910936
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 46079114807
- CO2 Emissions (in grams): 0.4102

## Validation Metrics

- Loss: 0.305
- Accuracy: 0.891
- Macro F1: 0.887
- Micro F1: 0.891
- Weighted F1: 0.891
- Macro Precision: 0.890
- Micro Precision: 0.891
- Weighted Precision: 0.891
- Macro Recall: 0.885
- Micro Recall: 0.891
- Weighted Recall: 0.891


## 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/dvilasuero/autotrain-alpaca-bs-detector-46079114807
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("dvilasuero/autotrain-alpaca-bs-detector-46079114807", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("dvilasuero/autotrain-alpaca-bs-detector-46079114807", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)
```