Create README.md

README.md

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+---
+language:
+- en
+metrics:
+- accuracy
+library_name: transformers
+tags:
+- text-generation-inference
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model card lists fine-tuned byT5 model for the task of Semantic Parsing.
+
+## Model Details
+We worked on a pre-trained byt5-base model and fine-tuned it with the Parallel Meaning Bank dataset (DRS-Text pairs dataset).
+Furthermore, we enriched the gold_silver flavors of PMB (release 5.0.0) with different augmentation strategies.
+
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+To use the model, follow the code below for a quick response.
+
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+
+# Initialize the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('saadamin2k13/byT5_ft_semantic_parser', max_length=512)
+
+model = T5ForConditionalGeneration.from_pretrained('saadamin2k13/byT5_ft_semantic_parser')
+
+# Example sentence
+example = "This car is black."
+
+# Tokenize and prepare the input
+x = tokenizer(example, return_tensors='pt', padding=True, truncation=True, max_length=512)['input_ids']
+
+# Generate output
+output = model.generate(x)
+
+# Decode and print the output text
+pred_text = tokenizer.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+print(pred_text)