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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: minilm-finetuned-emotionclassification |
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results: [] |
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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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# minilm-finetuned-emotionclassification |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0554 |
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- F1 Score: 0.6732 |
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## Model description |
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The base model used is Microsoft MiniLM-L12-H384-uncased which is finetuned on [GoEmotions dataset](https://huggingface.co/datasets/go_emotions) available on huggingface. |
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With this model, you can classify emotions in English text data. The model predicts 10 basic emotions: |
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1) anger 🤬 |
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2) love ❤️ |
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3) fear 😨 |
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4) joy 😀 |
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5) excitement 😄 |
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6) sadness 😭 |
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7) surprise 😲 |
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8) gratitude 😊 |
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9) curiosity 🤔 |
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10 caring |
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## Intended uses & limitations |
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The model can be used to detect emotions from text/ documents which can be used for analysis contextual emotional analysis of the documents |
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## Training and evaluation data |
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The dataset used for Training and Evaluation is [GoEmotions dataset](https://huggingface.co/datasets/go_emotions) |
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and in this, we have used 10 emotion variables. |
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{0:'sadness',1:'joy',2:'love',3:'anger',4:'fear',5:'surprise',6:'excitement',7:'gratitude',8:'curiosity',9:'caring'} |
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## How to use the model |
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Here is how to use this model to extract the emotions from the given text in PyTorch: |
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```python |
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>>> from transformers import pipeline |
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>>> model_ckpt ="sid321axn/minilm-finetuned-emotionclassification" |
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>>> pipe = pipeline("text-classification",model=model_ckpt) |
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>>> pipe("I am really excited about second part of Brahmastra Movie") |
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[{'label': 'excitement', 'score': 0.7849715352058411}] |
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``` |
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## Training procedure |
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The training we have done by following this [video](https://www.youtube.com/watch?v=u--UVvH-LIQ) on Youtube by huggingface |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-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: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.1659 | 1.0 | 539 | 1.1419 | 0.6347 | |
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| 1.0719 | 2.0 | 1078 | 1.0789 | 0.6589 | |
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| 0.9893 | 3.0 | 1617 | 1.0537 | 0.6666 | |
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| 0.9296 | 4.0 | 2156 | 1.0366 | 0.6729 | |
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| 0.8763 | 5.0 | 2695 | 1.0359 | 0.6774 | |
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| 0.8385 | 6.0 | 3234 | 1.0484 | 0.6693 | |
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| 0.8085 | 7.0 | 3773 | 1.0478 | 0.6758 | |
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| 0.7842 | 8.0 | 4312 | 1.0488 | 0.6741 | |
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| 0.7608 | 9.0 | 4851 | 1.0538 | 0.6749 | |
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| 0.7438 | 10.0 | 5390 | 1.0554 | 0.6732 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.2 |
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