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--- |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model card lists fine-tuned byT5 model for the task of Semantic Parsing. |
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## Model Details |
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We worked on a pre-trained byt5-base model and fine-tuned it with the Parallel Meaning Bank dataset (DRS-Text pairs dataset). |
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Furthermore, we enriched the gold_silver flavors of PMB (release 5.0.0) with different augmentation strategies. |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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To use the model, follow the code below for a quick response. |
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```python |
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from transformers import ByT5Tokenizer, T5ForConditionalGeneration |
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# Initialize the tokenizer and model |
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tokenizer = ByT5Tokenizer.from_pretrained('saadamin2k13/byT5_ft_semantic_parser', max_length=512) |
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model = T5ForConditionalGeneration.from_pretrained('saadamin2k13/byT5_ft_semantic_parser') |
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# Example sentence |
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example = "This car is black." |
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# Tokenize and prepare the input |
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x = tokenizer(example, return_tensors='pt', padding=True, truncation=True, max_length=512)['input_ids'] |
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# Generate output |
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output = model.generate(x) |
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# Decode and print the output text |
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pred_text = tokenizer.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) |
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print(pred_text) |
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