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.gitignore ADDED
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+ checkpoint-*/
README.md ADDED
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+ ---
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - conll2003
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+ model_index:
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+ - name: distilbert-base-uncased-squad2-with-ner-with-neg
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+ results:
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+ - task:
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+ name: Question Answering
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+ type: question-answering
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+ dataset:
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+ name: conll2003
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+ type: conll2003
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+ args: conll2003
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # distilbert-base-uncased-squad2-with-ner-with-neg
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+
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+ This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 dataset.
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 2.0
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+
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+ ### Training results
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+
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.8.2
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+ - Pytorch 1.8.1+cu111
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+ - Datasets 1.8.0
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+ - Tokenizers 0.10.3
config.json ADDED
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+ {
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+ "_name_or_path": "twmkn9/distilbert-base-uncased-squad2",
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+ "activation": "gelu",
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+ "architectures": [
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+ "DistilBertForQuestionAnswering"
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+ ],
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+ "attention_dropout": 0.1,
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+ "dim": 768,
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+ "dropout": 0.1,
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+ "eos_token_ids": null,
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+ "hidden_dim": 3072,
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+ "initializer_range": 0.02,
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+ "max_position_embeddings": 512,
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+ "model_type": "distilbert",
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+ "n_heads": 12,
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+ "n_layers": 6,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "qa_dropout": 0.1,
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+ "seq_classif_dropout": 0.2,
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+ "sinusoidal_pos_embds": false,
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+ "tie_weights_": true,
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+ "transformers_version": "4.8.2",
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+ "vocab_size": 30522
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+ }
log.log ADDED
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+ Training dataset length:
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+ 56164
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+ Validation dataset length:
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+ 13000
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+ Test dataset length:
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+ 13812
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+ Current eval performance:
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+ Evaluate results:
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+ {'eval_exact': 63.38461538461539, 'eval_f1': 65.59405430995848, 'eval_total': 13000, 'eval_HasAns_exact': 42.352654651729175, 'eval_HasAns_f1': 49.34795568179736, 'eval_HasAns_total': 4106, 'eval_NoAns_exact': 73.09422082302676, 'eval_NoAns_f1': 73.09422082302676, 'eval_NoAns_total': 8894, 'eval_best_exact': 68.5, 'eval_best_exact_thresh': 0.0, 'eval_best_f1': 68.52960805860803, 'eval_best_f1_thresh': 0.0, 'eval_samples': 13000}
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+ Predict results:
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+ {'test_exact': 63.90095569070374, 'test_f1': 66.14471389068335, 'test_total': 13812, 'test_HasAns_exact': 39.21136419299535, 'test_HasAns_f1': 46.80156459909871, 'test_HasAns_total': 4083, 'test_NoAns_exact': 74.26251413300442, 'test_NoAns_f1': 74.26251413300442, 'test_NoAns_total': 9729, 'test_best_exact': 70.55459021141037, 'test_best_exact_thresh': 0.0, 'test_best_f1': 70.6760355196497, 'test_best_f1_thresh': 0.0, 'predict_samples': 13812}
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+ Best trial:
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+ BestRun(run_id='8', objective=97.27663003662997, hyperparameters={'learning_rate': 5e-05, 'num_train_epochs': 5, 'per_device_train_batch_size': 16, 'warmup_steps': 1000})
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tokenizer.json ADDED
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