Downloads · 30 days
5
0% of all-time downloads
unhappyZebra100
FuriouslyAsleep/unhappyZebra100
unhappyZebra100 is a text classification model from FuriouslyAsleep. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Binary Classification - Model ID: 664919631 - CO2 Emissions (in grams): 0.6969569001670619
All-time downloads
1.3K
Public
Repo size
657 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin329 MB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Mar 24, 2022
- Updated
- Mar 24, 2022
- SHA
- fbe1acc0
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 664919631
- CO2 Emissions (in grams): 0.6969569001670619
Validation Metrics
- Loss: 0.022509008646011353
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
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/FuriouslyAsleep/autotrain-techDataClassifeier-664919631
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("FuriouslyAsleep/autotrain-techDataClassifeier-664919631", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("FuriouslyAsleep/autotrain-techDataClassifeier-664919631", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Languages
- en
- Created
- Mar 24, 2022
- Updated
- Mar 24, 2022