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--- |
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license: mit |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: laxsvips/minilm-finetuned-emotion |
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results: [] |
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datasets: |
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- emotion |
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language: |
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- en |
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metrics: |
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- f1 |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# laxsvips/minilm-finetuned-emotion |
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This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the Hugging Face emotion (https://huggingface.co/datasets/emotion) dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.1168 |
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- Train Accuracy: 0.9446 |
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- Validation Loss: 0.1709 |
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- Validation Accuracy: 0.9350 |
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- Epoch: 4 |
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## Model description |
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# MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation |
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MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers". |
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## Intended uses & limitations |
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This model has been created as a learning guide on: |
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- How to implement a text classification model using Hugging Face Transformers in TensorFlow |
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- How to handle imbalanced class distribution |
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# How to use the model |
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``` |
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from transformers import pipeline |
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model_cpt = "laxsvips/minilm-finetuned-emotion" |
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pipe = pipeline("text-classification", model=model_cpt) |
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predicted_scores = pipe("I am so glad you could help me") |
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print(predicted_scores) |
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```` |
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The results: |
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``` |
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[[{'label': 'sadness', 'score': 0.003758953418582678}, |
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{'label': 'joy', 'score': 0.9874302744865417}, |
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{'label': 'love', 'score': 0.00610917154699564}, |
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{'label': 'anger', 'score': 9.696640336187556e-05}, |
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{'label': 'fear', 'score': 0.0006420552381314337}, |
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{'label': 'surprise', 'score': 0.00196251692250371}]] |
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``` |
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## Training and evaluation data |
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[Emotion](https://huggingface.co/datasets/emotion) |
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Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. |
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## Training procedure |
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Refer to the [Colab](https://colab.research.google.com/github/laxmiharikumar/transformers/blob/main/TextClassification_Emotions_TF.ipynb) notebook |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: 'Adam', |
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- learning_rate': 5e-05, |
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- batch_size : 64 |
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- num_epochs - 5 |
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### Training results |
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| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |
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|:----------:|:--------------:|:---------------:|:-------------------:|:-----:| |
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| 0.9485 | 0.5543 | 0.8404 | 0.6870 | 0 | |
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| 0.4192 | 0.8347 | 0.3450 | 0.9040 | 1 | |
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| 0.2132 | 0.9178 | 0.2288 | 0.9240 | 2 | |
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| 0.1465 | 0.9364 | 0.1838 | 0.9295 | 3 | |
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| 0.1168 | 0.9446 | 0.1709 | 0.9350 | 4 | |
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### Evaluation Metrics |
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``` |
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{'accuracy': 0.935, |
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'precision': 0.937365614416424, |
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'recall': 0.935, |
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'f1_score': 0.9355424419858925} |
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``` |
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### Framework versions |
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- Transformers 4.26.1 |
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- TensorFlow 2.11.0 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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### References |
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1. https://www.youtube.com/watch?v=u--UVvH-LIQ |
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2. https://huggingface.co/docs/transformers |
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3. https://www.tensorflow.org/api_docs/python/tf |
