|
--- |
|
tags: autonlp |
|
language: unk |
|
widget: |
|
- text: "I love AutoNLP 🤗" |
|
datasets: |
|
- doctorlan/autonlp-data-JD-bert |
|
co2_eq_emissions: 5.919372931976555 |
|
--- |
|
|
|
# Model Trained Using AutoNLP |
|
|
|
- Problem type: Binary Classification |
|
- Model ID: 653619233 |
|
- CO2 Emissions (in grams): 5.919372931976555 |
|
|
|
## Validation Metrics |
|
|
|
- Loss: 0.15083155035972595 |
|
- Accuracy: 0.952650883627876 |
|
- Precision: 0.9631399317406143 |
|
- Recall: 0.9412941961307538 |
|
- AUC: 0.9828776962419389 |
|
- F1: 0.9520917678812415 |
|
|
|
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/doctorlan/autonlp-JD-bert-653619233 |
|
``` |
|
|
|
Or Python API: |
|
|
|
``` |
|
from transformers import AutoModelForSequenceClassification, AutoTokenizer |
|
|
|
model = AutoModelForSequenceClassification.from_pretrained("doctorlan/autonlp-JD-bert-653619233", use_auth_token=True) |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("doctorlan/autonlp-JD-bert-653619233", use_auth_token=True) |
|
|
|
inputs = tokenizer("I love AutoNLP", return_tensors="pt") |
|
|
|
outputs = model(**inputs) |
|
``` |
