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--- |
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language: |
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- en |
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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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- dair-ai/emotion |
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metrics: |
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- accuracy |
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- f1 |
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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 |
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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: emotion |
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type: emotion |
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config: split |
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split: validation |
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args: split |
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metrics: |
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- type: accuracy |
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value: 0.9375 |
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name: Accuracy |
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- type: f1 |
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value: 0.937890467332837 |
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name: F1 |
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--- |
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# distilbert-base-uncased-finetuned-emotion |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1448 |
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- Accuracy: 0.9375 |
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- F1: 0.9379 |
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The notebook used to fine-tune this model may be found [HERE](https://www.kaggle.com/marcoloureno/distilbert-base-uncased-finetuned-emotion). |
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## Model description |
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DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a |
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self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, |
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with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic |
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process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained |
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with three objectives: |
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- Distillation loss: the model was trained to return the same probabilities as the BERT base model. |
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- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a |
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sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the |
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model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that |
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usually see the words one after the other, or from autoregressive models like GPT which internally mask the future |
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tokens. It allows the model to learn a bidirectional representation of the sentence. |
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- Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base |
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model. |
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This way, the model learns the same inner representation of the English language than its teacher model, while being |
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faster for inference or downstream tasks. |
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## Intended uses & limitations |
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[Emotion](https://huggingface.co/datasets/dair-ai/emotion) is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. This dataset was developed for the paper entitled "CARER: Contextualized Affect Representations for Emotion Recognition" (Saravia et al.) through noisy labels, annotated via distant |
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supervision as in the paper"Twitter sentiment classification using distant supervision" (Go et al). |
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The DistilBERT model was fine-tuned to this dataset, allowing for the classification of sentences into one of the six basic emotions (anger, fear, joy, love, sadness, and surprise). |
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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: 64 |
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- eval_batch_size: 64 |
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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: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.5337 | 1.0 | 250 | 0.1992 | 0.927 | 0.9262 | |
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| 0.1405 | 2.0 | 500 | 0.1448 | 0.9375 | 0.9379 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
