Upload folder using huggingface_hub
Browse files- README.md +28 -15
- 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-large-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-large-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-large-qqp
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This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-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: 16
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- eval_batch_size:
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- seed: 42
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- gradient_accumulation_steps: 8
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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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| 0.2866 | 1.0 | 2842 | 0.2589 | 0.8891 |
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| 0.2022 | 2.0 | 5685 | 0.2509 | 0.8970 |
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| 0.1383 | 3.0 | 8527 | 0.2721 | 0.9083 |
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| 0.0938 | 4.0 | 11368 | 0.2742 | 0.9116 |
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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-large-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-large-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: 0.9132574820677715
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- name: F1
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type: f1
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value: 0.8825794354973717
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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-large-qqp
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This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE QQP dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4196
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- Accuracy: 0.9133
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- F1: 0.8826
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- Combined Score: 0.8979
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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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"train_samples_per_second": 174.6,
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config.json
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eval_results.json
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pytorch_model.bin
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tokenizer.json
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train_results.json
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1380 |
}
|
1381 |
],
|
1382 |
+
"max_steps": 113705,
|
1383 |
+
"num_train_epochs": 5,
|
1384 |
+
"total_flos": 4.2384937352679936e+17,
|
1385 |
"trial_name": null,
|
1386 |
"trial_params": null
|
1387 |
}
|
training_args.bin
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:760fe693984347cb90ffd1b835dab78653fb190c76695d0548197b645f8c8c4d
|
3 |
+
size 3963
|