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avanishd
/
distilbert-base-uncased-finetuned-emotion

Text Classification
Transformers
Safetensors
English
distilbert
Generated from Trainer
Eval Results
Model card Files Community
distilbert-base-uncased-finetuned-emotion
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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-emotion
  results:
  - task:
      type: text-classification
    dataset:
      name: dar-ai/emotion
      type: dair-ai/emotion
    metrics:
    - name: f1
      type: f1
      value: 0.9237
  
datasets:
- dair-ai/emotion
language:
- en
pipeline_tag: text-classification
---

<!-- 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-emotion

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2197
- Accuracy: 0.9235
- F1: 0.9237

## Model description

This model can classify English text into one of six emotion categories: sadness, joy, love, anger, fear, and surprise.

## Intended uses & limitations

More information needed

## How to Use

```Python
from transformers import pipeline

model_name = "avanishd/distilbert-base-uncased-finetuned-emotion"

classifier = pipeline("text-classification", model=model_name)

text = "I am happy"

prediction = classifier(text)
```

## Training and evaluation data

More information needed

## 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 OptimizerNames.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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 250  | 0.3163          | 0.905    | 0.9042 |
| No log        | 2.0   | 500  | 0.2197          | 0.9235   | 0.9237 |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1