Create README.md

README.md

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+---
+metrics:
+- mse
+- r_squared
+- mae
+---
+## DistilRoBERTa-query-wellformedness
+
+This model utilizes the [Distilroberta base](https://huggingface.co/distilroberta-base) architecture, which has been fine-tuned for a regression task on the [Google's query wellformedness](https://huggingface.co/datasets/google_wellformed_query) dataset encompassing 25,100 queries from the Paralex corpus. Each query received annotations from five raters, who provided a continuous rating indicating the degree to which the query is well-formed.
+
+## Model description
+
+The model evaluates the query for completeness and grammatical correctness, providing a score between 0 and 1, where 1 indicates correctness.
+
+## Usage
+
+```
+# Sentences
+sentences = [
+    "The cat and dog in the yard.",  # Incorrect - It should be "The cat and dog are in the yard."
+    "she don't like apples.",  # Incorrect - It should be "She doesn't like apples."
+    "Is rain sunny days sometimes?",  # Incorrect - It should be "Do sunny days sometimes have rain?"
+    "She enjoys reading books and playing chess.",  # Correct
+    "How many planets are there in our solar system?"  # Correct
+]
+
+# Tokenizing the sentences
+inputs = tokenizer(sentences, truncation=True, padding=True, return_tensors='pt')
+
+# Getting the model's predictions
+with torch.no_grad():  # Disabling gradient calculation as we are only doing inference
+    model.eval()  # Setting the model to evaluation mode
+    predicted_ratings = model(
+        input_ids=inputs['input_ids'], 
+        attention_mask=inputs['attention_mask']
+    )
+
+# The predicted_ratings is a tensor, so we'll convert it to a list of standard Python numbers
+predicted_ratings = predicted_ratings.squeeze().tolist()
+
+# Printing the predicted ratings
+for i, rating in enumerate(predicted_ratings):
+    print(f'Sentence: {sentences[i]}')
+    print(f'Predicted Rating: {rating}\n')
+```
+Output:
+```
+Sentence: The cat and dog in the yard.
+Predicted Rating: 0.3482873737812042
+
+Sentence: she don't like apples.
+Predicted Rating: 0.07787154614925385
+
+Sentence: Is rain sunny days sometimes?
+Predicted Rating: 0.19854165613651276
+
+Sentence: She enjoys reading books and playing chess.
+Predicted Rating: 0.9327691793441772
+
+Sentence: How many planets are there in our solar system?
+Predicted Rating: 0.9746372103691101
+```
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 450
+- num_epochs: 5
+
+### Training results
+
+Metrics: Mean Squared Error, R-Squared, Mean Absolute Error
+
+```
+'test_loss': 0.06214376166462898,
+'test_mse': 0.06214376166462898,
+'test_r2': 0.5705611109733582,
+'test_mae': 0.1838676631450653
+```
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch lightning 2.1.0
+- Tokenizers 0.14.1
+
+If you want to support me, you can [here](https://ko-fi.com/adamcodd).