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autotrain-email_tagger-3627996965
abhibagda/autotrain-email_tagger-3627996965
autotrain-email_tagger-3627996965 is a text classification model from abhibagda. 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: 3627996965 - CO2 Emissions (in grams): 2.3421
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Feb 21, 2023
- Updated
- Feb 21, 2023
- SHA
- 593b1640
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3627996965
- CO2 Emissions (in grams): 2.3421
Validation Metrics
- Loss: 1.029
- Accuracy: 0.667
- Macro F1: 0.550
- Micro F1: 0.667
- Weighted F1: 0.651
- Macro Precision: 0.545
- Micro Precision: 0.667
- Weighted Precision: 0.644
- Macro Recall: 0.563
- Micro Recall: 0.667
- Weighted Recall: 0.667
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/abhibagda/autotrain-email_tagger-3627996965
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhibagda/autotrain-email_tagger-3627996965", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhibagda/autotrain-email_tagger-3627996965", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- en
- Created
- Feb 21, 2023
- Updated
- Feb 21, 2023