| # 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 | |