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---
language: en
tags:
- emotion-classification
- bert
- lora
license: mit
---
# Emotion Classification Model
This model is a fine-tuned version of `bert-base-uncased` on the "dair-ai/emotion" dataset, using LoRA (Low-Rank Adaptation) for efficient fine-tuning.
label_list={"sadness", "joy", "love", "anger" ,"fear","surprise"}
## Model description
[Describe your model, its architecture, and the task it performs]
## Intended uses & limitations
[Describe what the model is intended for and any limitations]
## Training and evaluation data
The model was trained on the "dair-ai/emotion" dataset.
## Training procedure
[Describe your training procedure, hyperparameters, etc.]
## Eval results
[Include your evaluation results]
## How to use
Here's how you can use the model:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ahmetyaylalioglu/text-emotion-classifier")
tokenizer = AutoTokenizer.from_pretrained("ahmetyaylalioglu/text-emotion-classifier")
text = "I am feeling very happy today!"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)
print(model.config.id2label[predictions.item()]) |