negative neutral positive
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- kakashi210/autotrain-data-tweet-sentiment-classifier
co2_eq_emissions: 17.43982800509071
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1055036381
- CO2 Emissions (in grams): 17.43982800509071

## Validation Metrics

- Loss: 0.6177256107330322
- Accuracy: 0.7306006137658921
- Macro F1: 0.719534854339415
- Micro F1: 0.730600613765892
- Weighted F1: 0.7302204676842725
- Macro Precision: 0.714938066281146
- Micro Precision: 0.7306006137658921
- Weighted Precision: 0.7316651970219867
- Macro Recall: 0.7258484087500343
- Micro Recall: 0.7306006137658921
- Weighted Recall: 0.7306006137658921


## 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/kakashi210/autotrain-tweet-sentiment-classifier-1055036381
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("kakashi210/autotrain-tweet-sentiment-classifier-1055036381", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("kakashi210/autotrain-tweet-sentiment-classifier-1055036381", use_auth_token=True)

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

outputs = model(**inputs)
```