| --- |
| 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) |
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|
