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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- tweet_eval |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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model-index: |
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- name: bert-1-epoch-sentiment |
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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: tweet_eval |
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type: tweet_eval |
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config: sentiment |
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split: test |
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args: sentiment |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6895962227287529 |
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- name: Precision |
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type: precision |
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value: 0.6932981822495374 |
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- name: Recall |
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type: recall |
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value: 0.6895962227287529 |
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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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# bert-1-epoch-sentiment |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_eval dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6998 |
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- Accuracy: 0.6896 |
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- Precision: 0.6933 |
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- Recall: 0.6896 |
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- Micro-avg-recall: 0.6896 |
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- Micro-avg-precision: 0.6896 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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: 16 |
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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: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Micro-avg-recall | Micro-avg-precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:----------------:|:-------------------:| |
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| 0.5756 | 1.0 | 2851 | 0.6998 | 0.6896 | 0.6933 | 0.6896 | 0.6896 | 0.6896 | |
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### Framework versions |
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- Transformers 4.33.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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