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