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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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- accuracy
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- f1
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model-index:
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- name: tiny-vanilla-target-tweet
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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: emotion
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split: train
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args: emotion
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.7032085561497327
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- name: F1
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type: f1
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value: 0.704229444708009
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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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# tiny-vanilla-target-tweet
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This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the tweet_eval dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9887
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- Accuracy: 0.7032
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- F1: 0.7042
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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: 3e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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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: constant
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- num_epochs: 200
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| 1.1604 | 4.9 | 500 | 0.9784 | 0.6604 | 0.6290 |
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| 0.7656 | 9.8 | 1000 | 0.8273 | 0.7139 | 0.6905 |
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| 0.534 | 14.71 | 1500 | 0.8138 | 0.7219 | 0.7143 |
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| 0.3832 | 19.61 | 2000 | 0.8591 | 0.7086 | 0.7050 |
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| 0.2722 | 24.51 | 2500 | 0.9250 | 0.7112 | 0.7118 |
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| 0.1858 | 29.41 | 3000 | 0.9887 | 0.7032 | 0.7042 |
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.12.1
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- Datasets 2.7.1
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- Tokenizers 0.13.2
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