Downloads · 30 days
7
20% of all-time downloads
sgugit-model-v3
GRPUI/sgugit-model-v3
sgugit-model-v3 is a text classification model from GRPUI. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Multi-class Classification - Model ID: 91416144506 - CO2 Emissions (in grams): 0.7900
All-time downloads
35
Public
Parameters
167M
1.3 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin670 MB · 50%
Parameter types
How the weights are stored.
F32167M · 100%
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 91416144506
- CO2 Emissions (in grams): 0.7900
Validation Metrics
- Loss: 0.020
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000
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/GRPUI/autotrain-sgugit-model-v3-91416144506
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("GRPUI/autotrain-sgugit-model-v3-91416144506", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("GRPUI/autotrain-sgugit-model-v3-91416144506", 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
- Sep 25, 2023
- Updated
- Sep 25, 2023