Lewis Tunstall - Natural Language Processing with Transforme
Collection
A collection of models that I pushed while referring to code from the book,
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1 item
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Updated
distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1577
- Accuracy: 0.934
- F1: 0.9340
Classes
LABEL_0 : sadness
LABEL_1 : joy
LABEL_2 : love
LABEL_3 : anger
LABEL_4 : fear
LABEL_5 : surprise
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 250 | 0.1802 | 0.93 | 0.9303 |
| No log | 2.0 | 500 | 0.1577 | 0.934 | 0.9340 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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Model tree for coldn00dl3s/distilbert-base-uncased-finetuned-emotion
Base model
distilbert/distilbert-base-uncased
Finetuned
this model
Dataset used to train coldn00dl3s/distilbert-base-uncased-finetuned-emotion
Collection including coldn00dl3s/distilbert-base-uncased-finetuned-emotion
Evaluation results
- Accuracy on emotionvalidation set self-reported0.934
- F1 on emotionvalidation set self-reported0.934