pytorch distilbert text-classification unk dataset:erickdp/autotrain-data-sentiment-analysis-distillbert-es transformers autotrain co2_eq_emissions
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- erickdp/autotrain-data-sentiment-analysis-distillbert-es
co2_eq_emissions: 4.070674106910222
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1164342966
- CO2 Emissions (in grams): 4.070674106910222

## Validation Metrics

- Loss: 0.5068035125732422
- Accuracy: 0.8406482106684673
- Macro F1: 0.8355269443836222
- Micro F1: 0.8406482106684673
- Weighted F1: 0.8423675674232264
- Macro Precision: 0.8364960686615248
- Micro Precision: 0.8406482106684673
- Weighted Precision: 0.8455742631643787
- Macro Recall: 0.8361938729437037
- Micro Recall: 0.8406482106684673
- Weighted Recall: 0.8406482106684673


## 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/erickdp/autotrain-sentiment-analysis-distillbert-es-1164342966
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("erickdp/autotrain-sentiment-analysis-distillbert-es-1164342966", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("erickdp/autotrain-sentiment-analysis-distillbert-es-1164342966", use_auth_token=True)

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

outputs = model(**inputs)
```