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revmlc
madmancity/revmlc
revmlc is a text classification model from madmancity. Use it when you need a label for a piece of text. It is set up for transformers.
- Loss: 0.595 - Accuracy: 0.789 - Macro F1: 0.575 - Micro F1: 0.789 - Weighted F1: 0.763 - Macro Precision: 0.630 - Micro Precision: 0.789 - Weighted Precision: 0.775 - Macro Recall: 0.588 - Micro Recall: 0.789 - Weig…
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- deberta-v2
- Access
- Public
- Created
- Apr 9, 2023
- Updated
- Apr 11, 2023
- SHA
- 98c203d9
Try a prompt
Datasets
Model card
From the Hugging Face model README
Validation Metrics
- Loss: 0.595
- Accuracy: 0.789
- Macro F1: 0.575
- Micro F1: 0.789
- Weighted F1: 0.763
- Macro Precision: 0.630
- Micro Precision: 0.789
- Weighted Precision: 0.775
- Macro Recall: 0.588
- Micro Recall: 0.789
- Weighted Recall: 0.789
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 this product! One of my best purchases this year."}' https://api-inference.huggingface.co/models/madmancity/revmlc
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("madmancity/revmlc", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("madmancity/revmlc", use_auth_token=True)
inputs = tokenizer("I love this product! One of my best purchases this year.", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- Languages
- en
- Created
- Apr 9, 2023
- Updated
- Apr 11, 2023