Add model weights and configuration

conf.yml

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+# Path to pretrained model or model identifier from huggingface.co/models
+model_name_or_path: "bert-large-uncased-whole-word-masking"
+
+
+train_file: "../contract-nli-bert/data/train.json"
+
+dev_file: "../contract-nli-bert/data/dev.json"
+
+# Pretrained config name or path if not the same as model_name
+config_name: null
+
+# Pretrained tokenizer name or path if not the same as model_name
+tokenizer_name: null
+
+# Directory to save downloaded pretrained model
+# Default to ~/.cache/huggingface/transformers
+cache_dir: null
+
+# The maximum total input sequence length.
+# Sequence longer max_seq_length will be splitted into different chunks.
+max_seq_length: 512
+
+# How many tokens should the first span have in each chunk.
+# Note that it may not be honored when the span is too long.
+doc_stride: 128
+
+# The maximum number of tokens for the hypothesis.
+# Hypotheses longer than this will be truncated.
+max_query_length: 256
+
+# Set this flag if you are using an uncased model.
+do_lower_case: true
+
+per_gpu_train_batch_size: 2
+
+per_gpu_eval_batch_size: 2
+
+learning_rate: !!float 2e-5
+
+# Number of updates steps to accumulate before performing a backward/update pass.
+gradient_accumulation_steps: 3
+
+weight_decay: 0.0
+
+adam_epsilon: !!float 1e-8
+
+max_grad_norm: 1.0
+
+num_epochs: 3.0
+
+# If set, total number of training steps to perform. Conflicts with num_epochs.
+max_steps: null
+
+# Linear warmup over warmup_steps
+warmup_steps: 1000
+
+# language id of input for language-specific xlm models (see tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)
+lang_id: null
+
+# Validate every n steps
+valid_steps: 3000
+
+early_stopping: true
+
+# save model every n steps
+save_steps: -1
+
+seed: 42
+
+# Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit
+fp16: false
+
+# For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3'].
+# See details at https://nvidia.github.io/apex/amp.html
+fp16_opt_level: "O1"
+
+# Make it true if you have a gpu but you don't want to use it
+no_cuda: false
+
+# Overwrite the cached training and evaluation sets
+overwrite_cache: false
+
+weight_class_probs_by_span_probs: true
+
+# class loss is multiplied by this value
+class_loss_weight: 0.05
+
+# Either of 'identification_classification' or 'classification'
+task: "identification_classification"
+
+# Whether to treat hypothesis (query) texts as a symbol instead of feeding the
+# hypothesis descriptions
+symbol_based_hypothesis: false

config.json

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+{
+  "_name_or_path": "bert-large-uncased-whole-word-masking",
+  "architectures": [
+    "BertForQuestionAnswering"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.5.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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+size 1336543060

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": false, "do_basic_tokenize": true, "never_split": null, "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, "tokenizer_file": "/home/yiukei/.cache/huggingface/transformers/33ee0bae279476c742373ecfd5a127d27372fbb9e2f5a84ccb38bbd72775f296.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4", "name_or_path": "bert-large-uncased-whole-word-masking"}

training_args.bin

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+size 1711