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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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- precision
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- recall
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model-index:
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- name: bert-emotion
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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: Precision
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type: precision
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value: 0.7233519737225043
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- name: Recall
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type: recall
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value: 0.7300875430242757
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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-emotion
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1567
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- Precision: 0.7234
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- Recall: 0.7301
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- Fscore: 0.7253
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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: 5e-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: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
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| 0.8665 | 1.0 | 815 | 0.7583 | 0.7486 | 0.6417 | 0.6654 |
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| 0.5527 | 2.0 | 1630 | 0.9565 | 0.6848 | 0.6260 | 0.6438 |
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| 0.289 | 3.0 | 2445 | 1.1567 | 0.7234 | 0.7301 | 0.7253 |
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.13.0+cu116
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- Datasets 2.8.0
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- Tokenizers 0.13.2
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