diff --git a/model_cards/elgeish/cs224n-squad2.0-albert-base-v2/README.md b/model_cards/elgeish/cs224n-squad2.0-albert-base-v2/README.md new file mode 100644 index 0000000000..7739fc6029 --- /dev/null +++ b/model_cards/elgeish/cs224n-squad2.0-albert-base-v2/README.md @@ -0,0 +1,74 @@ +## CS224n SQuAD2.0 Project Dataset +The goal of this model is to save CS224n students GPU time when establising +baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf). +The training set used to fine-tune this model is the same as +the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however, +evaluation and model selection were performed using roughly half of the official +dev set, 6078 examples, picked at random. The data files can be found at + — this is the Winter 2020 +version. Given that the official SQuAD2.0 dev set contains the project's test +set, students must make sure not to use the official SQuAD2.0 dev set in any way +— including the use of models fine-tuned on the official SQuAD2.0, since they +used the official SQuAD2.0 dev set for model selection. + +## Results +```json +{ + "exact": 78.94044093451794, + "f1": 81.7724930324639, + "total": 6078, + "HasAns_exact": 76.28865979381443, + "HasAns_f1": 82.20385314478195, + "HasAns_total": 2910, + "NoAns_exact": 81.37626262626263, + "NoAns_f1": 81.37626262626263, + "NoAns_total": 3168, + "best_exact": 78.95689371503784, + "best_exact_thresh": 0.0, + "best_f1": 81.78894581298378, + "best_f1_thresh": 0.0 +} +``` + +## Notable Arguments +```json +{ + "do_lower_case": true, + "doc_stride": 128, + "fp16": false, + "fp16_opt_level": "O1", + "gradient_accumulation_steps": 24, + "learning_rate": 3e-05, + "max_answer_length": 30, + "max_grad_norm": 1, + "max_query_length": 64, + "max_seq_length": 384, + "model_name_or_path": "albert-base-v2", + "model_type": "albert", + "num_train_epochs": 3, + "per_gpu_train_batch_size": 8, + "save_steps": 5000, + "seed": 42, + "train_batch_size": 8, + "version_2_with_negative": true, + "warmup_steps": 0, + "weight_decay": 0 +} +``` + +## Environment Setup +```json +{ + "transformers": "2.5.1", + "pytorch": "1.4.0=py3.6_cuda10.1.243_cudnn7.6.3_0", + "python": "3.6.5=hc3d631a_2", + "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", + "gpu": "Tesla V100-SXM2-16GB" +} +``` + +## Related Models +* [elgeish/cs224n-squad2.0-albert-large-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-large-v2) +* [elgeish/cs224n-squad2.0-albert-xxlarge-v1](https://huggingface.co/elgeish/cs224n-squad2.0-albert-xxlarge-v1) +* [elgeish/cs224n-squad2.0-distilbert-base-uncased](https://huggingface.co/elgeish/cs224n-squad2.0-distilbert-base-uncased) +* [elgeish/cs224n-squad2.0-roberta-base](https://huggingface.co/elgeish/cs224n-squad2.0-roberta-base) diff --git a/model_cards/elgeish/cs224n-squad2.0-albert-large-v2/README.md b/model_cards/elgeish/cs224n-squad2.0-albert-large-v2/README.md new file mode 100644 index 0000000000..4166656ce1 --- /dev/null +++ b/model_cards/elgeish/cs224n-squad2.0-albert-large-v2/README.md @@ -0,0 +1,74 @@ +## CS224n SQuAD2.0 Project Dataset +The goal of this model is to save CS224n students GPU time when establising +baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf). +The training set used to fine-tune this model is the same as +the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however, +evaluation and model selection were performed using roughly half of the official +dev set, 6078 examples, picked at random. The data files can be found at + — this is the Winter 2020 +version. Given that the official SQuAD2.0 dev set contains the project's test +set, students must make sure not to use the official SQuAD2.0 dev set in any way +— including the use of models fine-tuned on the official SQuAD2.0, since they +used the official SQuAD2.0 dev set for model selection. + +## Results +```json +{ + "exact": 79.2694965449161, + "f1": 82.50844352970152, + "total": 6078, + "HasAns_exact": 74.87972508591065, + "HasAns_f1": 81.64478342732858, + "HasAns_total": 2910, + "NoAns_exact": 83.30176767676768, + "NoAns_f1": 83.30176767676768, + "NoAns_total": 3168, + "best_exact": 79.2694965449161, + "best_exact_thresh": 0.0, + "best_f1": 82.50844352970155, + "best_f1_thresh": 0.0 +} +``` + +## Notable Arguments +```json +{ + "do_lower_case": true, + "doc_stride": 128, + "fp16": false, + "fp16_opt_level": "O1", + "gradient_accumulation_steps": 1, + "learning_rate": 3e-05, + "max_answer_length": 30, + "max_grad_norm": 1, + "max_query_length": 64, + "max_seq_length": 384, + "model_name_or_path": "albert-large-v2", + "model_type": "albert", + "num_train_epochs": 5, + "per_gpu_train_batch_size": 8, + "save_steps": 5000, + "seed": 42, + "train_batch_size": 8, + "version_2_with_negative": true, + "warmup_steps": 0, + "weight_decay": 0 +} +``` + +## Environment Setup +```json +{ + "transformers": "2.5.1", + "pytorch": "1.4.0=py3.6_cuda10.1.243_cudnn7.6.3_0", + "python": "3.6.5=hc3d631a_2", + "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", + "gpu": "Tesla V100-SXM2-16GB" +} +``` + +## Related Models +* [elgeish/cs224n-squad2.0-albert-base-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-base-v2) +* [elgeish/cs224n-squad2.0-albert-xxlarge-v1](https://huggingface.co/elgeish/cs224n-squad2.0-albert-xxlarge-v1) +* [elgeish/cs224n-squad2.0-distilbert-base-uncased](https://huggingface.co/elgeish/cs224n-squad2.0-distilbert-base-uncased) +* [elgeish/cs224n-squad2.0-roberta-base](https://huggingface.co/elgeish/cs224n-squad2.0-roberta-base) diff --git a/model_cards/elgeish/cs224n-squad2.0-albert-xxlarge-v1/README.md b/model_cards/elgeish/cs224n-squad2.0-albert-xxlarge-v1/README.md new file mode 100644 index 0000000000..e21a7b741f --- /dev/null +++ b/model_cards/elgeish/cs224n-squad2.0-albert-xxlarge-v1/README.md @@ -0,0 +1,74 @@ +## CS224n SQuAD2.0 Project Dataset +The goal of this model is to save CS224n students GPU time when establising +baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf). +The training set used to fine-tune this model is the same as +the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however, +evaluation and model selection were performed using roughly half of the official +dev set, 6078 examples, picked at random. The data files can be found at + — this is the Winter 2020 +version. Given that the official SQuAD2.0 dev set contains the project's test +set, students must make sure not to use the official SQuAD2.0 dev set in any way +— including the use of models fine-tuned on the official SQuAD2.0, since they +used the official SQuAD2.0 dev set for model selection. + +## Results +```json +{ + "exact": 85.93287265547877, + "f1": 88.91258331187983, + "total": 6078, + "HasAns_exact": 84.36426116838489, + "HasAns_f1": 90.58786301361013, + "HasAns_total": 2910, + "NoAns_exact": 87.37373737373737, + "NoAns_f1": 87.37373737373737, + "NoAns_total": 3168, + "best_exact": 85.93287265547877, + "best_exact_thresh": 0.0, + "best_f1": 88.91258331187993, + "best_f1_thresh": 0.0 +} +``` + +## Notable Arguments +```json +{ + "do_lower_case": true, + "doc_stride": 128, + "fp16": false, + "fp16_opt_level": "O1", + "gradient_accumulation_steps": 24, + "learning_rate": 3e-05, + "max_answer_length": 30, + "max_grad_norm": 1, + "max_query_length": 64, + "max_seq_length": 512, + "model_name_or_path": "albert-xxlarge-v1", + "model_type": "albert", + "num_train_epochs": 4, + "per_gpu_train_batch_size": 1, + "save_steps": 1000, + "seed": 42, + "train_batch_size": 1, + "version_2_with_negative": true, + "warmup_steps": 814, + "weight_decay": 0 +} +``` + +## Environment Setup +```json +{ + "transformers": "2.5.1", + "pytorch": "1.4.0=py3.6_cuda10.1.243_cudnn7.6.3_0", + "python": "3.6.5=hc3d631a_2", + "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", + "gpu": "Tesla V100-SXM2-16GB" +} +``` + +## Related Models +* [elgeish/cs224n-squad2.0-albert-base-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-base-v2) +* [elgeish/cs224n-squad2.0-albert-large-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-large-v2) +* [elgeish/cs224n-squad2.0-distilbert-base-uncased](https://huggingface.co/elgeish/cs224n-squad2.0-distilbert-base-uncased) +* [elgeish/cs224n-squad2.0-roberta-base](https://huggingface.co/elgeish/cs224n-squad2.0-roberta-base) diff --git a/model_cards/elgeish/cs224n-squad2.0-distilbert-base-uncased/README.md b/model_cards/elgeish/cs224n-squad2.0-distilbert-base-uncased/README.md new file mode 100644 index 0000000000..4f90877b33 --- /dev/null +++ b/model_cards/elgeish/cs224n-squad2.0-distilbert-base-uncased/README.md @@ -0,0 +1,74 @@ +## CS224n SQuAD2.0 Project Dataset +The goal of this model is to save CS224n students GPU time when establising +baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf). +The training set used to fine-tune this model is the same as +the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however, +evaluation and model selection were performed using roughly half of the official +dev set, 6078 examples, picked at random. The data files can be found at + — this is the Winter 2020 +version. Given that the official SQuAD2.0 dev set contains the project's test +set, students must make sure not to use the official SQuAD2.0 dev set in any way +— including the use of models fine-tuned on the official SQuAD2.0, since they +used the official SQuAD2.0 dev set for model selection. + +## Results +```json +{ + "exact": 65.16946363935504, + "f1": 67.87348075352251, + "total": 6078, + "HasAns_exact": 69.51890034364261, + "HasAns_f1": 75.16667217179045, + "HasAns_total": 2910, + "NoAns_exact": 61.17424242424242, + "NoAns_f1": 61.17424242424242, + "NoAns_total": 3168, + "best_exact": 65.16946363935504, + "best_exact_thresh": 0.0, + "best_f1": 67.87348075352243, + "best_f1_thresh": 0.0 +} +``` + +## Notable Arguments +```json +{ + "do_lower_case": true, + "doc_stride": 128, + "fp16": false, + "fp16_opt_level": "O1", + "gradient_accumulation_steps": 24, + "learning_rate": 3e-05, + "max_answer_length": 30, + "max_grad_norm": 1, + "max_query_length": 64, + "max_seq_length": 384, + "model_name_or_path": "distilbert-base-uncased-distilled-squad", + "model_type": "distilbert", + "num_train_epochs": 4, + "per_gpu_train_batch_size": 32, + "save_steps": 5000, + "seed": 42, + "train_batch_size": 32, + "version_2_with_negative": true, + "warmup_steps": 0, + "weight_decay": 0 +} +``` + +## Environment Setup +```json +{ + "transformers": "2.5.1", + "pytorch": "1.4.0=py3.6_cuda10.1.243_cudnn7.6.3_0", + "python": "3.6.5=hc3d631a_2", + "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", + "gpu": "Tesla V100-SXM2-16GB" +} +``` + +## Related Models +* [elgeish/cs224n-squad2.0-albert-base-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-base-v2) +* [elgeish/cs224n-squad2.0-albert-large-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-large-v2) +* [elgeish/cs224n-squad2.0-albert-xxlarge-v1](https://huggingface.co/elgeish/cs224n-squad2.0-albert-xxlarge-v1) +* [elgeish/cs224n-squad2.0-roberta-base](https://huggingface.co/elgeish/cs224n-squad2.0-roberta-base) diff --git a/model_cards/elgeish/cs224n-squad2.0-roberta-base/README.md b/model_cards/elgeish/cs224n-squad2.0-roberta-base/README.md new file mode 100644 index 0000000000..3dc6e4edf2 --- /dev/null +++ b/model_cards/elgeish/cs224n-squad2.0-roberta-base/README.md @@ -0,0 +1,74 @@ +## CS224n SQuAD2.0 Project Dataset +The goal of this model is to save CS224n students GPU time when establising +baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf). +The training set used to fine-tune this model is the same as +the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however, +evaluation and model selection were performed using roughly half of the official +dev set, 6078 examples, picked at random. The data files can be found at + — this is the Winter 2020 +version. Given that the official SQuAD2.0 dev set contains the project's test +set, students must make sure not to use the official SQuAD2.0 dev set in any way +— including the use of models fine-tuned on the official SQuAD2.0, since they +used the official SQuAD2.0 dev set for model selection. + +## Results +```json +{ + "exact": 75.32082922013821, + "f1": 78.66699523704254, + "total": 6078, + "HasAns_exact": 74.84536082474227, + "HasAns_f1": 81.83436324767868, + "HasAns_total": 2910, + "NoAns_exact": 75.75757575757575, + "NoAns_f1": 75.75757575757575, + "NoAns_total": 3168, + "best_exact": 75.32082922013821, + "best_exact_thresh": 0.0, + "best_f1": 78.66699523704266, + "best_f1_thresh": 0.0 +} +``` + +## Notable Arguments +```json +{ + "do_lower_case": true, + "doc_stride": 128, + "fp16": false, + "fp16_opt_level": "O1", + "gradient_accumulation_steps": 24, + "learning_rate": 3e-05, + "max_answer_length": 30, + "max_grad_norm": 1, + "max_query_length": 64, + "max_seq_length": 384, + "model_name_or_path": "roberta-base", + "model_type": "roberta", + "num_train_epochs": 4, + "per_gpu_train_batch_size": 16, + "save_steps": 5000, + "seed": 42, + "train_batch_size": 16, + "version_2_with_negative": true, + "warmup_steps": 0, + "weight_decay": 0 +} +``` + +## Environment Setup +```json +{ + "transformers": "2.5.1", + "pytorch": "1.4.0=py3.6_cuda10.1.243_cudnn7.6.3_0", + "python": "3.6.5=hc3d631a_2", + "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", + "gpu": "Tesla V100-SXM2-16GB" +} +``` + +## Related Models +* [elgeish/cs224n-squad2.0-albert-base-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-base-v2) +* [elgeish/cs224n-squad2.0-albert-large-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-large-v2) +* [elgeish/cs224n-squad2.0-albert-xxlarge-v1](https://huggingface.co/elgeish/cs224n-squad2.0-albert-xxlarge-v1) +* [elgeish/cs224n-squad2.0-distilbert-base-uncased](https://huggingface.co/elgeish/cs224n-squad2.0-distilbert-base-uncased)