Downloads · 30 days
10
2% of all-time downloads
autonlp-JD-bert-653619233
doctorlan/autonlp-JD-bert-653619233
autonlp-JD-bert-653619233 is a text classification model from doctorlan. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Binary Classification - Model ID: 653619233 - CO2 Emissions (in grams): 5.919372931976555
All-time downloads
575
Public
Repo size
818 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin409 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Mar 21, 2022
- Updated
- Mar 21, 2022
- SHA
- 67bb35e5
Try a prompt
Datasets
Model card
From the Hugging Face model README
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)
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- unk
- Created
- Mar 21, 2022
- Updated
- Mar 21, 2022