| | --- |
| | 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()]) |
