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strat_call_followup_prod
futuredatascience/strat_call_followup_prod
strat_call_followup_prod is a text classification model from futuredatascience. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Binary Classification - Model ID: 61102134664 - CO2 Emissions (in grams): 0.1979
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Repo size
1.5 GB
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Repo makeup
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.bin738 MB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- deberta-v2
- Access
- Public
- Created
- May 23, 2023
- Updated
- May 23, 2023
- SHA
- 475a7a57
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 61102134664
- CO2 Emissions (in grams): 0.1979
Validation Metrics
- Loss: 0.266
- Accuracy: 0.939
- Precision: 0.955
- Recall: 0.913
- AUC: 0.952
- F1: 0.933
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/futuredatascience/autotrain-strat_call_follow_up-61102134664
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("futuredatascience/autotrain-strat_call_follow_up-61102134664", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("futuredatascience/autotrain-strat_call_follow_up-61102134664", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- Languages
- en
- Created
- May 23, 2023
- Updated
- May 23, 2023