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---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased_finetuned_on_emotions_data
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_finetuned_on_emotions_data
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1561
- Accuracy: 0.933
- F1: 0.9328
## Model description
his model is designed to analyze text and classify it into different emotional categories, such as joy, sadness, anger, etc. It has been trained on a dataset specifically labeled with emotions, allowing it to identify the emotional tone of the input text. The model works by processing the text and predicting which emotion best fits the given context
## Intended uses & limitations
More information needed
## limitations
- still this model is confused between fear and anger he model may confuse "fear" and "anger" because both emotions can be expressed in similar ways, especially in situations involving frustration, stress, or danger. Additionally, the language used to express these emotions might overlap, such as words like "nervous," "frustrated," or "threatened," which can be interpreted as either fear or anger depending on the context. This overlap in linguistic cues can make it challenging for the model to distinguish between the two emotions., joy & love
- similarely for love & Joy
## Training and evaluation data
I've used emotion data available on huggingface
Training data: emotion['train']
evaluation data: emotion['evaluation']
## confusion matrix:
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7824 | 1.0 | 250 | 0.2717 | 0.9145 | 0.9149 |
| 0.2093 | 2.0 | 500 | 0.1788 | 0.93 | 0.9306 |
| 0.1379 | 3.0 | 750 | 0.1594 | 0.9345 | 0.9349 |
| 0.1106 | 4.0 | 1000 | 0.1561 | 0.933 | 0.9328 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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