neg pos
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lewtun/autotrain-data-my-eval-project-615
co2_eq_emissions: 172.04481351504182
model-index:
- name: bhadresh-savani/distilbert-base-uncased-emotion
  results:
  - task:
      name: Multi-class Classification
      type: text-classification
    dataset:
      type: emotion
      name: Emotion
      config: default
      split: test
    metrics:
       - name: Loss
         type: loss
         value: 0.17404702305793762
       - name: Accuracy
         type: accuracy
         value: 0.927
       - name: Macro F1
         type: macro_f1
         value: 0.8825061528287809
       - name: Recall
         type: micro_f1
         value: 0.927
       - name: Weighted F1
         type: weighted_f1
         value: 0.926876082854655
       - name: Macro Precision
         type: macro_precision
         value: 0.8880230732280744
       - name: Micro Precision
         type: micro_precision
         value: 0.927
       - name: Weighted Precision
         type: weighted_precision
         value: 0.9272902840835793
       - name: Macro Recall
         type: macro_recall
         value: 0.8790126653780703
       - name: Micro Recall
         type: micro_recall
         value: 0.927
       - name: Weighted Recall
         type: weighted_recall
         value: 0.927
---

# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 5694363
- CO2 Emissions (in grams): 172.04481351504182

## Validation Metrics

- Loss: 0.2228243350982666
- Accuracy: 0.9298
- Precision: 0.9434585224927775
- Recall: 0.9144
- AUC: 0.9566112000000001
- F1: 0.9287020109689214

## 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/lewtun/autotrain-my-eval-project-615-5694363
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)

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

outputs = model(**inputs)
```