---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- sasha/autotrain-data-BERTBase-TweetEval
co2_eq_emissions:
  emissions: 0.1031242092898596
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1281248998
- CO2 Emissions (in grams): 0.1031

## Validation Metrics

- Loss: 0.602
- Accuracy: 0.746
- Macro F1: 0.718
- Micro F1: 0.746
- Weighted F1: 0.743
- Macro Precision: 0.740
- Micro Precision: 0.746
- Weighted Precision: 0.744
- Macro Recall: 0.705
- Micro Recall: 0.746
- Weighted Recall: 0.746


## 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/sasha/autotrain-BERTBase-TweetEval-1281248998
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248998", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248998", use_auth_token=True)

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

outputs = model(**inputs)
```