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arabertv2_flodusta
MMars/arabertv2_flodusta
arabertv2_flodusta is a text classification model from MMars. Use it when you need a label for a piece of text. It is set up for transformers.
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Jan 8, 2023
- Updated
- Jan 8, 2023
- SHA
- b7a75616
Try a prompt
Model card
From the Hugging Face model README
Labels Mapping
0 non event
1 flood
2 dust storm
3 traffic accident
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2782582128
- CO2 Emissions (in grams): 3.6263
Validation Metrics
- Loss: 0.144
- Accuracy: 0.953
- Macro F1: 0.951
- Micro F1: 0.953
- Weighted F1: 0.953
- Macro Precision: 0.951
- Micro Precision: 0.953
- Weighted Precision: 0.953
- Macro Recall: 0.952
- Micro Recall: 0.953
- Weighted Recall: 0.953
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/MMars/autotrain-arabertv2_flodusta-2782582128
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("MMars/arabertv2_flodusta", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("MMars/arabertv2_flodusta", 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
- Jan 8, 2023
- Updated
- Jan 8, 2023