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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/elgeish/cs224n-squad2.0-distilbert-base-uncased/README.md

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+ ## CS224n SQuAD2.0 Project Dataset
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+ The goal of this model is to save CS224n students GPU time when establishing
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+ baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
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+ The training set used to fine-tune this model is the same as
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+ the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however,
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+ evaluation and model selection were performed using roughly half of the official
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+ dev set, 6078 examples, picked at random. The data files can be found at
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+ <https://github.com/elgeish/squad/tree/master/data> — this is the Winter 2020
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+ version. Given that the official SQuAD2.0 dev set contains the project's test
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+ set, students must make sure not to use the official SQuAD2.0 dev set in any way
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+ — including the use of models fine-tuned on the official SQuAD2.0, since they
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+ used the official SQuAD2.0 dev set for model selection.
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+
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+ ## Results
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+ ```json
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+ {
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+ "exact": 65.16946363935504,
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+ "f1": 67.87348075352251,
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+ "total": 6078,
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+ "HasAns_exact": 69.51890034364261,
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+ "HasAns_f1": 75.16667217179045,
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+ "HasAns_total": 2910,
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+ "NoAns_exact": 61.17424242424242,
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+ "NoAns_f1": 61.17424242424242,
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+ "NoAns_total": 3168,
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+ "best_exact": 65.16946363935504,
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+ "best_exact_thresh": 0.0,
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+ "best_f1": 67.87348075352243,
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+ "best_f1_thresh": 0.0
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+ }
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+ ```
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+
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+ ## Notable Arguments
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+ ```json
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+ {
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+ "do_lower_case": true,
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+ "doc_stride": 128,
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+ "fp16": false,
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+ "fp16_opt_level": "O1",
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+ "gradient_accumulation_steps": 24,
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+ "learning_rate": 3e-05,
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+ "max_answer_length": 30,
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+ "max_grad_norm": 1,
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+ "max_query_length": 64,
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+ "max_seq_length": 384,
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+ "model_name_or_path": "distilbert-base-uncased-distilled-squad",
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+ "model_type": "distilbert",
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+ "num_train_epochs": 4,
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+ "per_gpu_train_batch_size": 32,
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+ "save_steps": 5000,
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+ "seed": 42,
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+ "train_batch_size": 32,
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+ "version_2_with_negative": true,
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+ "warmup_steps": 0,
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+ "weight_decay": 0
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+ }
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+ ```
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+
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+ ## Environment Setup
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+ ```json
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+ {
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+ "transformers": "2.5.1",
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+ "pytorch": "1.4.0=py3.6_cuda10.1.243_cudnn7.6.3_0",
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+ "python": "3.6.5=hc3d631a_2",
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+ "os": "Linux 4.15.0-1060-aws #62-Ubuntu SMP Tue Feb 11 21:23:22 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux",
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+ "gpu": "Tesla V100-SXM2-16GB"
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+ }
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+ ```
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+
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+ ## How to Cite
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+ ```BibTeX
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+ @misc{elgeish2020gestalt,
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+ title={Gestalt: a Stacking Ensemble for SQuAD2.0},
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+ author={Mohamed El-Geish},
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+ journal={arXiv e-prints},
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+ archivePrefix={arXiv},
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+ eprint={2004.07067},
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+ year={2020},
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+ }
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+ ```
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+
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+ ## Related Models
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+ * [elgeish/cs224n-squad2.0-albert-base-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-base-v2)
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+ * [elgeish/cs224n-squad2.0-albert-large-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-large-v2)
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+ * [elgeish/cs224n-squad2.0-albert-xxlarge-v1](https://huggingface.co/elgeish/cs224n-squad2.0-albert-xxlarge-v1)
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+ * [elgeish/cs224n-squad2.0-roberta-base](https://huggingface.co/elgeish/cs224n-squad2.0-roberta-base)