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update model card README.md
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README.md
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---
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license:
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---
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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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- go_emotions
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: distilbert-go-emotions
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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: go_emotions
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type: go_emotions
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args: simplified
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.42867674161444896
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- name: Precision
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type: precision
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value: 0.4503794622630937
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- name: Recall
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type: recall
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value: 0.5002719498073401
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- name: F1
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type: f1
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value: 0.47024637118703766
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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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# distilbert-go-emotions
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the go_emotions dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0907
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- Accuracy: 0.4287
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- Precision: 0.4504
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- Recall: 0.5003
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- F1: 0.4702
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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: 128
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- eval_batch_size: 128
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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: 6
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| No log | 1.0 | 340 | 0.1102 | 0.4125 | 0.2946 | 0.2139 | 0.2014 |
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| 0.1347 | 2.0 | 680 | 0.0890 | 0.3900 | 0.4252 | 0.4627 | 0.4262 |
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| 0.1347 | 3.0 | 1020 | 0.0860 | 0.4097 | 0.4344 | 0.4992 | 0.4578 |
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| 0.0771 | 4.0 | 1360 | 0.0864 | 0.4298 | 0.4561 | 0.4974 | 0.4701 |
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| 0.0771 | 5.0 | 1700 | 0.0891 | 0.4266 | 0.4522 | 0.4992 | 0.4703 |
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| 0.0617 | 6.0 | 2040 | 0.0907 | 0.4287 | 0.4504 | 0.5003 | 0.4702 |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.12.0
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- Datasets 2.1.0
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- Tokenizers 0.12.1
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