---
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.

```python

from transformers import ByT5Tokenizer, T5ForConditionalGeneration

# Initialize the tokenizer and model
tokenizer = ByT5Tokenizer.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)