Training complete

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: bert-goemotions-15epochs-run2
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# bert-goemotions-15epochs-run2
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1106
+- Accuracy Thresh: 0.9619
+- F1 weighted: {'f1': 0.3982045872720165}
+- F1 macro: {'f1': 0.31538372135978}
+- Accuracy: {'accuracy': 0.4170647653000594}
+- Recall weighted: {'recall': 0.4170647653000594}
+- Recall macro: {'recall': 0.32808068442725}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy Thresh | F1 weighted                 | F1 macro                    | Accuracy                          | Recall weighted                 | Recall macro                    |
+|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:---------------------------:|:---------------------------:|:---------------------------------:|:-------------------------------:|:-------------------------------:|
+| 0.1259        | 1.0   | 5286  | 0.1127          | 0.9617          | {'f1': 0.3890983386624071}  | {'f1': 0.3007761920553838}  | {'accuracy': 0.41630421865715983} | {'recall': 0.41630421865715983} | {'recall': 0.3213896867494273}  |
+| 0.1102        | 2.0   | 10572 | 0.1106          | 0.9619          | {'f1': 0.3982045872720165}  | {'f1': 0.31538372135978}    | {'accuracy': 0.4170647653000594}  | {'recall': 0.4170647653000594}  | {'recall': 0.32808068442725}    |
+| 0.1052        | 3.0   | 15858 | 0.1107          | 0.9619          | {'f1': 0.3980887152485667}  | {'f1': 0.3181332487636058}  | {'accuracy': 0.4168983957219251}  | {'recall': 0.4168983957219251}  | {'recall': 0.32790458379700677} |
+| 0.1008        | 4.0   | 21144 | 0.1117          | 0.9616          | {'f1': 0.39966069827702533} | {'f1': 0.32238147844285014} | {'accuracy': 0.4169459298871064}  | {'recall': 0.4169459298871064}  | {'recall': 0.3280426755048108}  |
+| 0.0968        | 5.0   | 26430 | 0.1138          | 0.9609          | {'f1': 0.39833587917024693} | {'f1': 0.32459673497912495} | {'accuracy': 0.4110516934046346}  | {'recall': 0.4110516934046346}  | {'recall': 0.3347612567297387}  |
+| 0.0934        | 6.0   | 31716 | 0.1158          | 0.9604          | {'f1': 0.3893454681480969}  | {'f1': 0.3185494353334119}  | {'accuracy': 0.39786096256684494} | {'recall': 0.39786096256684494} | {'recall': 0.3332642189610775}  |
+| 0.0902        | 7.0   | 37002 | 0.1188          | 0.9596          | {'f1': 0.3843621716402447}  | {'f1': 0.3127316237002698}  | {'accuracy': 0.39332144979203804} | {'recall': 0.39332144979203804} | {'recall': 0.32900959748461356} |
+
+
+### Framework versions
+
+- Transformers 4.35.1
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1

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