abhishek HF staff commited on
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Commit From AutoNLP

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.gitattributes CHANGED
@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.tar.gz filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ tags: autonlp
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+ language: en
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+ widget:
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+ - text: "I love AutoNLP 🤗"
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+ datasets:
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+ - vinaydngowda/autonlp-data-case-classify-xlnet
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+ co2_eq_emissions: 19.964760910364927
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+ ---
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+
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+ # Model Trained Using AutoNLP
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+
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+ - Problem type: Multi-class Classification
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+ - Model ID: 496213536
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+ - CO2 Emissions (in grams): 19.964760910364927
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+
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+ ## Validation Metrics
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+
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+ - Loss: 0.7149562835693359
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+ - Accuracy: 0.8092592592592592
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+ - Macro F1: 0.8085189591849891
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+ - Micro F1: 0.8092592592592593
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+ - Weighted F1: 0.8085189591849888
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+ - Macro Precision: 0.8137745564384112
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+ - Micro Precision: 0.8092592592592592
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+ - Weighted Precision: 0.8137745564384112
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+ - Macro Recall: 0.8092592592592592
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+ - Micro Recall: 0.8092592592592592
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+ - Weighted Recall: 0.8092592592592592
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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 AutoNLP"}' https://api-inference.huggingface.co/models/vinaydngowda/autonlp-case-classify-xlnet-496213536
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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("vinaydngowda/autonlp-case-classify-xlnet-496213536", use_auth_token=True)
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+
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+ tokenizer = AutoTokenizer.from_pretrained("vinaydngowda/autonlp-case-classify-xlnet-496213536", use_auth_token=True)
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+
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+ inputs = tokenizer("I love AutoNLP", return_tensors="pt")
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+
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+ outputs = model(**inputs)
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+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "AutoNLP",
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+ "_num_labels": 9,
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+ "architectures": [
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+ "BertForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 1024,
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+ "id2label": {
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+ "0": "Checking or savings account",
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+ "1": "Credit card or prepaid card",
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+ "2": "Credit reporting, credit repair services, or other personal consumer reports",
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+ "3": "Debt collection",
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+ "4": "Money transfer, virtual currency, or money service",
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+ "5": "Mortgage",
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+ "6": "Payday loan, title loan, or personal loan",
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+ "7": "Student loan",
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+ "8": "Vehicle loan or lease"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 4096,
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+ "label2id": {
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+ "Checking or savings account": 0,
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+ "Credit card or prepaid card": 1,
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+ "Credit reporting, credit repair services, or other personal consumer reports": 2,
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+ "Debt collection": 3,
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+ "Money transfer, virtual currency, or money service": 4,
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+ "Mortgage": 5,
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+ "Payday loan, title loan, or personal loan": 6,
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+ "Student loan": 7,
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+ "Vehicle loan or lease": 8
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "max_length": 512,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 16,
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+ "num_hidden_layers": 24,
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+ "pad_token_id": 0,
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+ "padding": "max_length",
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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": "4.15.0",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30522
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+ }
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tokenizer.json ADDED
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vocab.txt ADDED
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