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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: sentiment_trained_42
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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: sentiment
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metrics:
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- name: F1
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type: f1
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value: 0.7131935389791447
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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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# sentiment_trained_42
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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: 1.3194
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- F1: 0.7132
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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: 1.2140338797769864e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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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: 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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| 0.6405 | 1.0 | 11404 | 0.6631 | 0.7046 |
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| 0.5998 | 2.0 | 22808 | 0.8429 | 0.7102 |
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| 0.5118 | 3.0 | 34212 | 1.0906 | 0.7155 |
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| 0.3745 | 4.0 | 45616 | 1.3194 | 0.7132 |
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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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