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:
+- Chijioke/autonlp-data-mono
+co2_eq_emissions: 1.1406456838043837
+---
+
+# Model Trained Using AutoNLP
+
+- Problem type: Multi-class Classification
+- Model ID: 625317956
+- CO2 Emissions (in grams): 1.1406456838043837
+
+## Validation Metrics
+
+- Loss: 0.513037919998169
+- Accuracy: 0.8982035928143712
+- Macro F1: 0.7843756230226546
+- Micro F1: 0.8982035928143712
+- Weighted F1: 0.8891653474608059
+- Macro Precision: 0.8210878091622635
+- Micro Precision: 0.8982035928143712
+- Weighted Precision: 0.8888857327766032
+- Macro Recall: 0.7731018645485747
+- Micro Recall: 0.8982035928143712
+- Weighted Recall: 0.8982035928143712
+
+
+## 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/Chijioke/autonlp-mono-625317956
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("Chijioke/autonlp-mono-625317956", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("Chijioke/autonlp-mono-625317956", 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": 23,
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "Rent",
+    "1": "atm_withdrawal",
+    "2": "atm_withdrawal_charges",
+    "3": "bank_charges",
+    "4": "bills_or_fees",
+    "5": "card_request_commission",
+    "6": "cash_deposit",
+    "7": "food",
+    "8": "health",
+    "9": "investment",
+    "10": "loan_repayment",
+    "11": "mature_loan_instalment",
+    "12": "miscellaneous",
+    "13": "offline_transactions",
+    "14": "online_transactions",
+    "15": "others",
+    "16": "phone_and_internet",
+    "17": "reversals",
+    "18": "salary",
+    "19": "stamp_duties_charges",
+    "20": "transfer",
+    "21": "transportation",
+    "22": "vat"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "Rent": 0,
+    "atm_withdrawal": 1,
+    "atm_withdrawal_charges": 2,
+    "bank_charges": 3,
+    "bills_or_fees": 4,
+    "card_request_commission": 5,
+    "cash_deposit": 6,
+    "food": 7,
+    "health": 8,
+    "investment": 9,
+    "loan_repayment": 10,
+    "mature_loan_instalment": 11,
+    "miscellaneous": 12,
+    "offline_transactions": 13,
+    "online_transactions": 14,
+    "others": 15,
+    "phone_and_internet": 16,
+    "reversals": 17,
+    "salary": 18,
+    "stamp_duties_charges": 19,
+    "transfer": 20,
+    "transportation": 21,
+    "vat": 22
+  },
+  "max_length": 64,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.15.0",
+  "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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