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---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- sasha/autotrain-data-DistilBERT-TweetEval
co2_eq_emissions:
emissions: 7.4450095136306444
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1281148991
- CO2 Emissions (in grams): 7.4450
## Validation Metrics
- Loss: 0.610
- Accuracy: 0.739
- Macro F1: 0.721
- Micro F1: 0.739
- Weighted F1: 0.739
- Macro Precision: 0.727
- Micro Precision: 0.739
- Weighted Precision: 0.740
- Macro Recall: 0.715
- Micro Recall: 0.739
- Weighted Recall: 0.739
## 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-DistilBERT-TweetEval-1281148991
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-DistilBERT-TweetEval-1281148991", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-DistilBERT-TweetEval-1281148991", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |