README.md

---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain"
datasets:
- Vishwas1/autotrain-data-customer-intent-bert
co2_eq_emissions:
  emissions: 0.006826171695324974
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 99967147525
- CO2 Emissions (in grams): 0.0068

## Validation Metrics

- Loss: 0.013
- Accuracy: 0.998
- Macro F1: 0.999
- Micro F1: 0.998
- Weighted F1: 0.998
- Macro Precision: 0.999
- Micro Precision: 0.998
- Weighted Precision: 0.998
- Macro Recall: 0.999
- Micro Recall: 0.998
- Weighted Recall: 0.998


## 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"}' /static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fmodels%2FVishwas1%2Fautotrain-customer-intent-bert-99967147525%3C%2Fspan%3E
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Vishwas1/autotrain-customer-intent-bert-99967147525", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Vishwas1/autotrain-customer-intent-bert-99967147525", use_auth_token=True)

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

outputs = model(**inputs)
```