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--- |
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license: apache-2.0 |
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datasets: |
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- amazon_polarity |
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base_model: distilbert-base-uncased |
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model-index: |
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- name: distilbert-base-uncased-finetuned-emotion-balanced |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: amazon_polarity |
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type: sentiment |
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args: default |
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metrics: |
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- type: accuracy |
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value: 0.94112 |
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name: Accuracy |
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- type: loss |
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value: 0.1634 |
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name: Loss |
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- type: f1 |
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value: 0.9417 |
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name: F1 |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: amazon_polarity |
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type: amazon_polarity |
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config: amazon_polarity |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.94112 |
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name: Accuracy |
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verified: true |
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verifyToken: >- |
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- type: precision |
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value: 0.9321570625232675 |
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name: Precision |
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verified: true |
|
verifyToken: >- |
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- type: recall |
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value: 0.95149 |
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name: Recall |
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verified: true |
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verifyToken: >- |
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|
- type: auc |
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value: 0.9849019044624999 |
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name: AUC |
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verified: true |
|
verifyToken: >- |
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- type: f1 |
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value: 0.9417243188138998 |
|
name: F1 |
|
verified: true |
|
verifyToken: >- |
|
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- type: loss |
|
value: 0.16342754662036896 |
|
name: loss |
|
verified: true |
|
verifyToken: >- |
|
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--- |
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|
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# distilbert-sentiment |
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|
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a subset of the [amazon-polarity dataset](https://huggingface.co/datasets/amazon_polarity). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.163 |
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- Accuracy: 0.941 |
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- F1_score: 0.942 |
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|
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## Model description |
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This sentiment classifier has been trained on 180_000 samples for the training set, 20_000 samples for the validation set and 20_000 samples for the test set. |
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## Intended uses & limitations |
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```python |
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from transformers import pipeline |
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# Create the pipeline |
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sentiment_classifier = pipeline('text-classification', model='AdamCodd/distilbert-base-uncased-finetuned-sentiment-amazon') |
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|
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# Now you can use the pipeline to classify emotions |
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result = sentiment_classifier("This product doesn't fit me at all.") |
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print(result) |
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#[{'label': 'negative', 'score': 0.9994848966598511}] |
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``` |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 1270 |
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- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 150 |
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- num_epochs: 2 |
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- weight_decay: 0.01 |
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### Training results |
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|
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| key | value | |
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| --- | ----- | |
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| eval_accuracy | 0.94112 | |
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| eval_auc | 0.9849 | |
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| eval_f1_score | 0.9417 | |
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| eval_precision | 0.9321 | |
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| eval_recall | 0.95149 | |
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### Framework versions |
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|
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- Transformers 4.34.0 |
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- Pytorch lightning 2.0.9 |
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- Tokenizers 0.14.0 |
