|
--- |
|
tags: autotrain |
|
language: en |
|
widget: |
|
- text: "I love AutoTrain 🤗" |
|
datasets: |
|
- lewtun/autotrain-data-acronym-identification |
|
co2_eq_emissions: 10.435358044493652 |
|
--- |
|
|
|
# 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) |
|
``` |
