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distilbert-multilingual-finetuned-sentiment
mgb-dx-meetup/distilbert-multilingual-finetuned-sentiment
distilbert-multilingual-finetuned-sentiment is a text classification model from mgb-dx-meetup. 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: 1904564767 - CO2 Emissions (in grams): 5.5231
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At a glance
- Task
- Text Classification
- Library
- transformers
- Access
- Public
- Created
- Oct 27, 2022
- Updated
- Oct 27, 2022
- SHA
- e8c6f858
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1904564767
- CO2 Emissions (in grams): 5.5231
Validation Metrics
- Loss: 1.135
- Accuracy: 0.514
- Macro F1: 0.504
- Micro F1: 0.514
- Weighted F1: 0.505
- Macro Precision: 0.506
- Micro Precision: 0.514
- Weighted Precision: 0.507
- Macro Recall: 0.513
- Micro Recall: 0.514
- Weighted Recall: 0.514
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/lewtun/autotrain-mgb-product-reviews-mbert-1904564767
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lewtun/autotrain-mgb-product-reviews-mbert-1904564767", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-mgb-product-reviews-mbert-1904564767", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Languages
- unk
- Created
- Oct 27, 2022
- Updated
- Oct 27, 2022