negative objective positive
---
tags: autotrain
language: tr
widget:
- text: "I love AutoTrain 🤗"
datasets:
- bsenker/autotrain-data-sentanaly
co2_eq_emissions: 2.4274113973426568
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1016134101
- CO2 Emissions (in grams): 2.4274113973426568

## Validation Metrics

- Loss: 0.8357052803039551
- Accuracy: 0.6425438596491229
- Macro F1: 0.6449751139113629
- Micro F1: 0.6425438596491229
- Weighted F1: 0.644975113911363
- Macro Precision: 0.6642782595845687
- Micro Precision: 0.6425438596491229
- Weighted Precision: 0.6642782595845685
- Macro Recall: 0.6425438596491229
- Micro Recall: 0.6425438596491229
- Weighted Recall: 0.6425438596491229


## 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/bsenker/autotrain-sentanaly-1016134101
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("bsenker/autotrain-sentanaly-1016134101", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("bsenker/autotrain-sentanaly-1016134101", use_auth_token=True)

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

outputs = model(**inputs)
```