Commit From AutoNLP

.gitattributes

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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
+*.pkl filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+tags: autonlp
+language: en
+widget:
+- text: "I love AutoNLP 🤗"
+datasets:
+- vinaydngowda/autonlp-data-case-classify-xlnet
+co2_eq_emissions: 19.964760910364927
+---
+
+# Model Trained Using AutoNLP
+
+- Problem type: Multi-class Classification
+- Model ID: 496213536
+- CO2 Emissions (in grams): 19.964760910364927
+
+## Validation Metrics
+
+- Loss: 0.7149562835693359
+- Accuracy: 0.8092592592592592
+- Macro F1: 0.8085189591849891
+- Micro F1: 0.8092592592592593
+- Weighted F1: 0.8085189591849888
+- Macro Precision: 0.8137745564384112
+- Micro Precision: 0.8092592592592592
+- Weighted Precision: 0.8137745564384112
+- Macro Recall: 0.8092592592592592
+- Micro Recall: 0.8092592592592592
+- Weighted Recall: 0.8092592592592592
+
+
+## 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 AutoNLP"}' https://api-inference.huggingface.co/models/vinaydngowda/autonlp-case-classify-xlnet-496213536
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("vinaydngowda/autonlp-case-classify-xlnet-496213536", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("vinaydngowda/autonlp-case-classify-xlnet-496213536", use_auth_token=True)
+
+inputs = tokenizer("I love AutoNLP", return_tensors="pt")
+
+outputs = model(**inputs)
+```

config.json

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+{
+  "_name_or_path": "AutoNLP",
+  "_num_labels": 9,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "Checking or savings account",
+    "1": "Credit card or prepaid card",
+    "2": "Credit reporting, credit repair services, or other personal consumer reports",
+    "3": "Debt collection",
+    "4": "Money transfer, virtual currency, or money service",
+    "5": "Mortgage",
+    "6": "Payday loan, title loan, or personal loan",
+    "7": "Student loan",
+    "8": "Vehicle loan or lease"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "Checking or savings account": 0,
+    "Credit card or prepaid card": 1,
+    "Credit reporting, credit repair services, or other personal consumer reports": 2,
+    "Debt collection": 3,
+    "Money transfer, virtual currency, or money service": 4,
+    "Mortgage": 5,
+    "Payday loan, title loan, or personal loan": 6,
+    "Student loan": 7,
+    "Vehicle loan or lease": 8
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.15.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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sample_input.pkl

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special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "AutoNLP", "tokenizer_class": "BertTokenizer"}