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--- |
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license: apache-2.0 |
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datasets: |
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- jfleg |
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widget: |
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- text: "fix grammar: I am work with machine to write gooder english." |
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example_title: example |
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--- |
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This is my first model for grammar error correction. It uses the jfleg dataset and is built on `t5-base`. It is trained only for 3 epochs so the output isn't that great. |
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## Usage |
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You can use this model with the standard transformers library. This model should be small enough to run on the CPU. |
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``` |
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$ pip install transformers torch sentencepiece |
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``` |
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Once you have the dependencies setup, you should be able to run this model. |
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``` |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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model_name = 'vagmi/grammar-t5' |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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text = 'fix grammar: I am work with machine to write gooder english.' |
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inputs = tokenizer(text, return_tensors='pt') |
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outputs = model.generate(inputs['input_ids'], num_beams=2, max_length=512, early_stopping=True) |
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fixed = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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# I am working with machine to write better english. |
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``` |
