---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- emotions
- sentiment-analysis
model-index:
- name: Distilbert-base-uncased_dair-ai_emotion
  results: []
language:
- en
metrics:
- accuracy
- f1
pipeline_tag: text-classification
datasets:
- dair-ai/emotion
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Load model directly

    from transformers import AutoTokenizer, AutoModelForSequenceClassification

    tokenizer = AutoTokenizer.from_pretrained("Arjun4707/Distilbert-base-uncased_dair-ai_emotion")

    model = AutoModelForSequenceClassification.from_pretrained("Arjun4707/Distilbert-base-uncased_dair-ai_emotion", from_tf = True)


for more check out this notebook: https://github.com/BhammarArjun/NLP/blob/main/Model_validation_distilbert_emotions.ipynb

## Model description

Model takes text as input and outputs an predictions for one of the 6 emotions. 
          
      [label_0 :'anger', label_1 : 'fear', 
       label_2 : 'joy', label_3 : 'love', 
       label_4 : 'sadness', label_5 : 'surprise']

          
# Distilbert-base-uncased_dair-ai_emotion

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an dair-ai/emotion dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0896
- Train Accuracy: 0.9582
- Validation Loss: 0.1326
- Validation Accuracy: 0.9375
- Epoch: 4
            
## Intended uses & limitations

Use to identify an emotion of a user from above mentioned emotions. The statements starts with 'I' in data. Need further training


## Training and evaluation data

Training data size = 16000, validation data = 2000, and test data = 2000

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5820     | 0.8014         | 0.2002          | 0.9305              | 0     |
| 0.1598     | 0.9366         | 0.1431          | 0.9355              | 1     |
| 0.1101     | 0.9515         | 0.1390          | 0.9355              | 2     |
| 0.0896     | 0.9582         | 0.1326          | 0.9375              | 3     |