Upload folder using huggingface_hub
Browse files- README.md +22 -15
- all_results.json +17 -10
- config.json +7 -7
- eval_results.json +13 -6
- pytorch_model.bin +1 -1
- tokenizer.json +2 -2
- train_results.json +5 -5
- trainer_state.json +1399 -109
- 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-mnli
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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-mnli
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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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| 0.414 | 1.0 | 3068 | 0.4073 | 0.8414 |
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| 0.269 | 2.0 | 6136 | 0.4017 | 0.8580 |
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| 0.1355 | 3.0 | 9204 | 0.5104 | 0.8556 |
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| 0.0636 | 4.0 | 12272 | 0.6533 | 0.8582 |
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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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model-index:
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- name: bert-large-mnli
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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 MNLI
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type: glue
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args: mnli
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8613710333604556
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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-mnli
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This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE MNLI dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9132
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- Accuracy: 0.8614
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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_per_second": 380.669,
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"train_samples_per_second": 171.967,
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config.json
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"directionality": "bidi",
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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": 1024,
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"id2label": {
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"initializer_range": 0.02,
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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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"directionality": "bidi",
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"finetuning_task": "mnli",
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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": 1024,
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"id2label": {
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"0": "entailment",
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"initializer_range": 0.02,
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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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pytorch_model.bin
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size 1334463793
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tokenizer.json
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train_results.json
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trainer_state.json
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| 46 |
{
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| 54 |
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| 57 |
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| 58 |
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| 59 |
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| 60 |
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| 63 |
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| 64 |
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| 65 |
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| 66 |
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| 68 |
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| 69 |
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| 70 |
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| 71 |
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| 72 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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|
| 81 |
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| 82 |
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| 83 |
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| 84 |
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| 87 |
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| 89 |
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| 90 |
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|
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| 102 |
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| 105 |
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| 108 |
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| 111 |
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| 117 |
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| 123 |
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| 129 |
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| 135 |
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| 138 |
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| 142 |
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| 143 |
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| 144 |
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| 146 |
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| 147 |
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| 150 |
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| 153 |
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| 156 |
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| 159 |
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| 162 |
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training_args.bin
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