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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- AdamCodd/emotion-balanced
metrics:
- accuracy
- f1
- recall
- precision
base_model: prajjwal1/bert-tiny
model-index:
- name: AdamCodd/tinybert-emotion-balanced
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- type: accuracy
value: 0.9354
name: Accuracy
- type: loss
value: 0.1809
name: Loss
- type: f1
value: 0.9354946613311768
name: F1
---
# tinybert-emotion
This model is a fine-tuned version of [bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the [emotion balanced dataset](https://huggingface.co/datasets/AdamCodd/emotion-balanced).
It achieves the following results on the evaluation set:
- Loss: 0.1809
- Accuracy: 0.9354
## Model description
TinyBERT is 7.5 times smaller and 9.4 times faster on inference compared to its teacher BERT model (while DistilBERT is 40% smaller and 1.6 times faster than BERT).
The model has been trained on 89_754 examples split into train, validation and test. Each label was perfectly balanced in each split.
## Intended uses & limitations
This model is not as accurate as the [distilbert-emotion-balanced](https://huggingface.co/AdamCodd/distilbert-base-uncased-finetuned-emotion-balanced) one because the focus was on speed, which can lead to misinterpretation of complex sentences. Despite this, its performance is quite good and should be more than sufficient for most use cases.
Usage:
```python
from transformers import pipeline
# Create the pipeline
emotion_classifier = pipeline('text-classification', model='AdamCodd/tinybert-emotion-balanced')
# Now you can use the pipeline to classify emotions
result = emotion_classifier("We are delighted that you will be coming to visit us. It will be so nice to have you here.")
print(result)
#[{'label': 'joy', 'score': 0.9895486831665039}]
```
This model faces challenges in accurately categorizing negative sentences, as well as those containing elements of sarcasm or irony. These limitations are largely attributable to TinyBERT's constrained capabilities in semantic understanding. Although the model is generally proficient in emotion detection tasks, it may lack the nuance necessary for interpreting complex emotional nuances.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 1270
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 10
- weight_decay: 0.01
### Training results
precision recall f1-score support
sadness 0.9733 0.9245 0.9482 1496
joy 0.9651 0.8864 0.9240 1496
love 0.9127 0.9786 0.9445 1496
anger 0.9479 0.9365 0.9422 1496
fear 0.9213 0.9004 0.9108 1496
surprise 0.9016 0.9866 0.9422 1496
accuracy 0.9355 8976
macro avg 0.9370 0.9355 0.9353 8976
weighted avg 0.9370 0.9355 0.9353 8976
test_acc: 0.9354946613311768
test_loss: 0.1809326708316803
### Framework versions
- Transformers 4.33.0
- Pytorch lightning 2.0.8
- Tokenizers 0.13.3
If you want to support me, you can [here](https://ko-fi.com/adamcodd). |