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---
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)
``` |