Downloads · 30 days
9
15% of all-time downloads
drug-recommend-reco-v1
hunk3000/drug-recommend-reco-v1
drug-recommend-reco-v1 is a text classification model from hunk3000. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Binary Classification - Model ID: 2617579252 - CO2 Emissions (in grams): 2.7483
All-time downloads
62
Public
Repo size
819 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
- Dec 26, 2022
- Updated
- Dec 26, 2022
- SHA
- e44729f9
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 2617579252
- CO2 Emissions (in grams): 2.7483
Validation Metrics
- Loss: 0.051
- Accuracy: 0.987
- Precision: 0.985
- Recall: 0.991
- AUC: 0.999
- F1: 0.988
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/hunk3000/autotrain-drugrecommendreco-2617579252
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("hunk3000/autotrain-drugrecommendreco-2617579252", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("hunk3000/autotrain-drugrecommendreco-2617579252", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- unk
- Created
- Dec 26, 2022
- Updated
- Dec 26, 2022