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bert-emotion

Text Classification
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distilbert
Generated from Trainer
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end of training 3 epochs

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  1. README.md +14 -13
  2. model.safetensors +1 -1
  3. runs/Nov29_22-57-16_39f981cb8869/events.out.tfevents.1701298650.39f981cb8869.2106.0 +2 -2
README.md CHANGED
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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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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.7505623807659564
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  - name: Recall
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  type: recall
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- value: 0.7243031825553111
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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
@@ -35,10 +36,10 @@ should probably proofread and complete it, then remove this comment. -->
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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: 1.1413
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- - Precision: 0.7506
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- - Recall: 0.7243
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- - Fscore: 0.7340
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
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- | 0.8556 | 1.0 | 815 | 0.7854 | 0.7461 | 0.5929 | 0.6088 |
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- | 0.5369 | 2.0 | 1630 | 0.9014 | 0.7549 | 0.7278 | 0.7359 |
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- | 0.2571 | 3.0 | 2445 | 1.1413 | 0.7506 | 0.7243 | 0.7340 |
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  ### Framework versions
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- - Transformers 4.28.1
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- - Pytorch 2.0.0+cu118
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- - Datasets 2.11.0
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- - Tokenizers 0.13.3
 
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  ---
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  license: apache-2.0
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+ base_model: distilbert-base-cased
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.7412691902027423
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  - name: Recall
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  type: recall
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+ value: 0.7200253439873575
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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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  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: 1.2007
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+ - Precision: 0.7413
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+ - Recall: 0.7200
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+ - Fscore: 0.7268
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
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+ | 0.8416 | 1.0 | 815 | 0.7683 | 0.7000 | 0.7141 | 0.7062 |
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+ | 0.5465 | 2.0 | 1630 | 0.8561 | 0.7640 | 0.6735 | 0.6979 |
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+ | 0.2747 | 3.0 | 2445 | 1.2007 | 0.7413 | 0.7200 | 0.7268 |
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  ### Framework versions
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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