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Commit From AutoTrain

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.gitattributes CHANGED
@@ -32,3 +32,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ tags:
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+ - autotrain
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+ - text-classification
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+ language:
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+ - unk
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+ widget:
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+ - text: "I love AutoTrain 🤗"
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+ datasets:
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+ - MMars/autotrain-data-camelbert-mix_flodusta
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+ co2_eq_emissions:
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+ emissions: 0.010214592292905006
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+ ---
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+
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+ # Model Trained Using AutoTrain
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+
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+ - Problem type: Multi-class Classification
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+ - Model ID: 2783082152
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+ - CO2 Emissions (in grams): 0.0102
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+
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+ ## Validation Metrics
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+
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+ - Loss: 0.149
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+ - Accuracy: 0.949
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+ - Macro F1: 0.946
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+ - Micro F1: 0.949
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+ - Weighted F1: 0.949
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+ - Macro Precision: 0.942
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+ - Micro Precision: 0.949
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+ - Weighted Precision: 0.950
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+ - Macro Recall: 0.951
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+ - Micro Recall: 0.949
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+ - Weighted Recall: 0.949
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+
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+
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+ ## Usage
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+
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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 AutoTrain"}' https://api-inference.huggingface.co/models/MMars/autotrain-camelbert-mix_flodusta-2783082152
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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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+
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+ model = AutoModelForSequenceClassification.from_pretrained("MMars/autotrain-camelbert-mix_flodusta-2783082152", use_auth_token=True)
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+
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+ tokenizer = AutoTokenizer.from_pretrained("MMars/autotrain-camelbert-mix_flodusta-2783082152", use_auth_token=True)
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+
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+ inputs = tokenizer("I love AutoTrain", return_tensors="pt")
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+
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+ outputs = model(**inputs)
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+ ```
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+ "position_embedding_type": "absolute",
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+ "problem_type": "single_label_classification",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.25.1",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30000
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+ }
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vocab.txt ADDED
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