Upload README.md

README.md

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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.
+
+## 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("YOUR_HUGGINGFACE_USERNAME/emotion_classifier")
+tokenizer = AutoTokenizer.from_pretrained("YOUR_HUGGINGFACE_USERNAME/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()])