Initial push of bert-wiki-paragraphs.

README.md

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+# BERT-Wiki-Paragraphs
+
+Authors: Satya Almasian*, Dennis Aumiller*, Michael Gertz  
+Details for the training method can be found in our work [Structural Text Segmentation of Legal Documents](https://arxiv.org/abs/2012.03619).
+The training procedure follows the same setup, but we substitute legal documents for Wikipedia in this model.
+
+Training is performed in a form of weakly-supervised fashion to determine whether paragraphs topically belong together or not.
+We utilize automatically generated samples from Wikipedia for training,
+where paragrahs from within the same section are assumed to be topically coherent.  
+We use the same articles as ([Koshorek et al., 2018](https://arxiv.org/abs/1803.09337)),
+albeit from a 2021 dump of Wikpeida, and split at paragraph boundaries instead of the sentence level.
+
+## Training Setup
+The model was trained for 3 epochs from the "bert-base-uncased" checkpoint on paragraph pairs (truncated to 512 max length).
+Training was performed on a single Titan RTX GPU over the duration of 3 weeks.

config.json

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+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": 0,
+    "1": 1
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "0": 0,
+    "1": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.8.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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+oid sha256:6d80454cbc388be64d08cb4268f5b7cd00e4ad74c94f9e4fafa2d5b90da77b1d
+size 438019245

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": "bert-base-uncased", "tokenizer_class": "BertTokenizer"}