Update README.md

README.md

@@ -1,62 +1,77 @@
 ---
-library_name: transformers
-license: apache-2.0
-base_model: bert-base-uncased
+language: en
+datasets:
+- ibm-research/argument_quality_ranking_30k
+metrics:
+- mean-squared-error
+- mean-absolute-error
+model-name: bert-base-uncased-finetuned-arg-quality
 tags:
-- generated_from_trainer
-model-index:
-- name: results
-  results: []
+- regression
+- argument-quality
+- bert
+- fine-tuned
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# BERT Base (uncased) fine-tuned on Argument Quality Ranking
 
-# results
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the **IBM Argument Quality Ranking** dataset. It predicts the **quality of arguments** as a score between 0 and 1. You can also convert the score to a **1–5 rating**.
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0404
-- Mse: 0.0404
-- Mae: 0.1499
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+---
 
-## Training procedure
+## Model Details
+- **Model type**: BERT (base, uncased)  
+- **Fine-tuned on**: IBM Argument Quality Ranking (~30k arguments)  
+- **Task**: Regression (argument quality score)  
+- **Output**: Integer between 1-5 
+- **Training framework**: [🤗 Transformers](https://github.com/huggingface/transformers)  
 
-### Training hyperparameters
+---
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 3
+## Training
+- Epochs: 3  
+- Batch size: 16  
+- Learning rate: 2e-5  
+- Optimizer: AdamW  
+- Evaluation metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE)
 
-### Training results
+---
 
-| Training Loss | Epoch | Step | Validation Loss | Mse    | Mae    |
-|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
-| 0.0078        | 1.0   | 1311 | 0.0420          | 0.0420 | 0.1557 |
-| 0.0154        | 2.0   | 2622 | 0.0368          | 0.0368 | 0.1420 |
-| 0.0098        | 3.0   | 3933 | 0.0378          | 0.0378 | 0.1441 |
+## Evaluation Results
+On the test set:
 
+| Metric | Value |
+|--------|-------|
+| MSE    | 0.0404 |
+| MAE    | 0.1499 |
 
-### Framework versions
+---
 
-- Transformers 4.56.1
-- Pytorch 2.8.0+cu126
-- Datasets 4.0.0
-- Tokenizers 0.22.0
+## How to Use
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
+
+model_name = "ByteMeHarder-404/bert-base-uncased-finetuned-arg-quality"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+def predict_quality(arguments):
+    inputs = tokenizer(arguments, truncation=True, padding=True, return_tensors="pt")
+    device = next(model.parameters()).device
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    model.eval()
+    with torch.no_grad():
+        outputs = model(**inputs)
+        preds = round(outputs.logits.squeeze().cpu().numpy()*4+1)
+    return preds
+
+# Example
+args = [
+    "School uniforms reduce individuality.",
+    "World Peace is great",
+    "Homework improves student learning outcomes."
+]
+
+print("Ratings:", predict_rating(args))  # Output: 1–5 ratings