---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lewtun/autotrain-data-acronym-identification
- acronym_identification
co2_eq_emissions: 10.435358044493652
model-index:
- name: autotrain-demo
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: acronym_identification
      type: acronym_identification
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9708090976211485
---

# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 7324788
- CO2 Emissions (in grams): 10.435358044493652

## Validation Metrics

- Loss: 0.08991389721632004
- Accuracy: 0.9708090976211485
- Precision: 0.8998421675654347
- Recall: 0.9309429854401959
- F1: 0.9151284109149278

## 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-acronym-identification-7324788
```

Or Python API:

```
from transformers import AutoModelForTokenClassification, AutoTokenizer

model = AutoModelForTokenClassification.from_pretrained("lewtun/autotrain-acronym-identification-7324788", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-acronym-identification-7324788", use_auth_token=True)

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

outputs = model(**inputs)
```