---
library_name: transformers
license: apache-2.0
datasets:
- dair-ai/emotion
language:
- en
metrics:
- accuracy
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
---



### Direct Use

```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch
  
  model_name = "SkyAsl/Bert-Emotion_classifier"
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  model = AutoModelForSequenceClassification.from_pretrained(model_name)
  
  text = "I am so happy to see you!"
  inputs = tokenizer(text, return_tensors="pt")
  outputs = model(**inputs)
  predicted_class = torch.argmax(outputs.logits, dim=1).item()
  
  id2label = {
      0: "sadness", 1: "joy", 2: "love",
      3: "anger", 4: "fear", 5: "surprise"
  }
  print("Predicted emotion:", id2label[predicted_class])
```

## Training Details

### Training Data

https://huggingface.co/datasets/dair-ai/emotion

#### Training Hyperparameters

lr = 2e-4
batch_size = 128
epochs = 5
weight_decay = 0.01


#### Metrics

training_loss: 0.106100
validation_loss: 0.143851
accuracy: 0.940000