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README.md
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---
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license: apache-2.0
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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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- f1
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model-index:
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- name: demo_hate_1234567
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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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args: hate
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metrics:
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- name: F1
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type: f1
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value: 0.7772939485986298
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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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# demo_hate_1234567
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-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: 0.8697
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- F1: 0.7773
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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: 7.320702985778492e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 0
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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: 4
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| No log | 1.0 | 282 | 0.4850 | 0.7645 |
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| 0.3877 | 2.0 | 564 | 0.5160 | 0.7856 |
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| 0.3877 | 3.0 | 846 | 0.6927 | 0.7802 |
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| 0.1343 | 4.0 | 1128 | 0.8697 | 0.7773 |
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### Framework versions
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- Transformers 4.12.5
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- Pytorch 1.9.1
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- Datasets 1.16.1
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- Tokenizers 0.10.3
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