Upload folder using huggingface_hub
Browse files- README.md +29 -16
- all_results.json +12 -10
- config.json +5 -5
- eval_results.json +8 -6
- pytorch_model.bin +1 -1
- tokenizer.json +2 -2
- train_results.json +5 -5
- trainer_state.json +1298 -104
- training_args.bin +2 -2
README.md
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---
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license: apache-2.0
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base_model: bert-base-cased
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: bert-base-qqp
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results:
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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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# bert-base-qqp
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size:
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- eval_batch_size:
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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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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- num_epochs:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|
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| 0.2764 | 1.0 | 2842 | 0.2651 | 0.8867 |
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| 0.1888 | 2.0 | 5685 | 0.2454 | 0.9002 |
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| 0.1161 | 3.0 | 8528 | 0.2765 | 0.9062 |
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| 0.0663 | 4.0 | 11368 | 0.3249 | 0.9077 |
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### Framework versions
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---
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language:
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- en
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license: apache-2.0
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base_model: bert-base-cased
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tags:
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- generated_from_trainer
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datasets:
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- glue
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metrics:
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- accuracy
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- f1
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model-index:
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- name: bert-base-qqp
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: GLUE QQP
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type: glue
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args: qqp
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.908904279000742
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- name: F1
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type: f1
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value: 0.8769585407409882
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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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# bert-base-qqp
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QQP dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5173
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- Accuracy: 0.9089
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- F1: 0.8770
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- Combined Score: 0.8929
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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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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- num_epochs: 5.0
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### Training results
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### Framework versions
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all_results.json
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"eval_samples": 40430,
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"epoch": 5.0,
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"eval_accuracy": 0.908904279000742,
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"eval_combined_score": 0.8929314098708652,
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"eval_f1": 0.8769585407409882,
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"eval_loss": 0.517304003238678,
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"eval_runtime": 56.7867,
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"eval_samples": 40430,
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"eval_samples_per_second": 711.963,
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"eval_steps_per_second": 89.0,
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"train_loss": 0.16809054645142282,
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"train_samples_per_second": 340.239,
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"train_steps_per_second": 21.266
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}
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config.json
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"finetuning_task": "
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"finetuning_task": "qqp",
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "not_duplicate",
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"1": "duplicate"
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"duplicate": 1,
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"not_duplicate": 0
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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eval_results.json
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"eval_combined_score": 0.8929314098708652,
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"eval_f1": 0.8769585407409882,
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"eval_loss": 0.517304003238678,
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"eval_runtime": 56.7867,
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"eval_samples": 40430,
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"eval_samples_per_second": 711.963,
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"eval_steps_per_second": 89.0
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 433315633
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size 433315633
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tokenizer.json
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"version": "1.0",
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"truncation": {
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train_results.json
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trainer_state.json
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| 48 |
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| 66 |
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| 72 |
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| 93 |
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| 105 |
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| 111 |
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| 117 |
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| 123 |
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| 129 |
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| 144 |
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| 150 |
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| 156 |
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| 162 |
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| 170 |
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| 177 |
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| 180 |
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