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@@ -8,11 +8,10 @@ datasets:
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  co2_eq_emissions: 60.744727079482495
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  ---
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- # Model Trained Using AutoNLP
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  - Problem type: Multi-class Classification
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  - Model ID: 25805800
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- - CO2 Emissions (in grams): 60.744727079482495
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  ## Validation Metrics
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@@ -31,22 +30,14 @@ co2_eq_emissions: 60.744727079482495
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  ## Usage
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- You can use cURL to access this model:
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-
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- ```
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- $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Crasher222/autonlp-kaggle-test-25805800
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- ```
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-
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- Or Python API:
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-
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  ```
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  from transformers import AutoModelForSequenceClassification, AutoTokenizer
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- model = AutoModelForSequenceClassification.from_pretrained("Crasher222/autonlp-kaggle-test-25805800", use_auth_token=True)
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- tokenizer = AutoTokenizer.from_pretrained("Crasher222/autonlp-kaggle-test-25805800", use_auth_token=True)
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- inputs = tokenizer("I love AutoNLP", return_tensors="pt")
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  outputs = model(**inputs)
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  ```
 
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  co2_eq_emissions: 60.744727079482495
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  ---
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+ # Model Finetuned from BERT-base for
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  - Problem type: Multi-class Classification
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  - Model ID: 25805800
 
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  ## Validation Metrics
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  ## Usage
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  ```
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  from transformers import AutoModelForSequenceClassification, AutoTokenizer
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+ model = AutoModelForSequenceClassification.from_pretrained("Crasher222/kaggle-comp-test")
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+ tokenizer = AutoTokenizer.from_pretrained("Crasher222/kaggle-comp-test")
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+ inputs = tokenizer("I am in love with you", return_tensors="pt")
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  outputs = model(**inputs)
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  ```