Commit From AutoTrain

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,56 @@
+---
+tags:
+- autotrain
+- text-classification
+language:
+- en
+widget:
+- text: "I love AutoTrain 🤗"
+datasets:
+- jogoni/autotrain-data-cuad-document-type
+co2_eq_emissions:
+  emissions: 4.141997048700727
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 2883884341
+- CO2 Emissions (in grams): 4.1420
+
+## Validation Metrics
+
+- Loss: 0.541
+- Accuracy: 0.938
+- Macro F1: 0.869
+- Micro F1: 0.938
+- Weighted F1: 0.925
+- Macro Precision: 0.875
+- Micro Precision: 0.938
+- Weighted Precision: 0.927
+- Macro Recall: 0.884
+- Micro Recall: 0.938
+- Weighted Recall: 0.938
+
+
+## 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 AutoTrain"}' https://api-inference.huggingface.co/models/jogoni/autotrain-cuad-document-type-2883884341
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("jogoni/autotrain-cuad-document-type-2883884341", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("jogoni/autotrain-cuad-document-type-2883884341", use_auth_token=True)
+
+inputs = tokenizer("I love AutoTrain", return_tensors="pt")
+
+outputs = model(**inputs)
+```

config.json

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+{
+  "_name_or_path": "AutoTrain",
+  "_num_labels": 27,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Affiliate_Agreements",
+    "1": "Agency Agreements",
+    "2": "Co_Branding",
+    "3": "Collaboration",
+    "4": "Consulting Agreements",
+    "5": "Development",
+    "6": "Distributor",
+    "7": "Endorsement",
+    "8": "Endorsement Agreement",
+    "9": "Franchise",
+    "10": "Hosting",
+    "11": "IP",
+    "12": "Joint Venture",
+    "13": "Joint Venture _ Filing",
+    "14": "License_Agreements",
+    "15": "Maintenance",
+    "16": "Manufacturing",
+    "17": "Marketing",
+    "18": "Non_Compete_Non_Solicit",
+    "19": "Outsourcing",
+    "20": "Promotion",
+    "21": "Reseller",
+    "22": "Service",
+    "23": "Sponsorship",
+    "24": "Strategic Alliance",
+    "25": "Supply",
+    "26": "Transportation"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Affiliate_Agreements": 0,
+    "Agency Agreements": 1,
+    "Co_Branding": 2,
+    "Collaboration": 3,
+    "Consulting Agreements": 4,
+    "Development": 5,
+    "Distributor": 6,
+    "Endorsement": 7,
+    "Endorsement Agreement": 8,
+    "Franchise": 9,
+    "Hosting": 10,
+    "IP": 11,
+    "Joint Venture": 12,
+    "Joint Venture _ Filing": 13,
+    "License_Agreements": 14,
+    "Maintenance": 15,
+    "Manufacturing": 16,
+    "Marketing": 17,
+    "Non_Compete_Non_Solicit": 18,
+    "Outsourcing": 19,
+    "Promotion": 20,
+    "Reseller": 21,
+    "Service": 22,
+    "Sponsorship": 23,
+    "Strategic Alliance": 24,
+    "Supply": 25,
+    "Transportation": 26
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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special_tokens_map.json

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+  "mask_token": "[MASK]",
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+  "sep_token": "[SEP]",
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tokenizer.json

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tokenizer_config.json

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