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--- |
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license: mit |
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base_model: roberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- matthews_correlation |
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model-index: |
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- name: c4-binary-english-grammar-checker |
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results: [] |
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--- |
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# Usage instructions: |
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The recommendation is to split the text into sentences and evaluate sentence by sentence, you can do that using spacy: |
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``` |
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import spacy |
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def clean_up_sentence(text: str) -> str: |
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text = text.replace("---", "") |
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text = text.replace("\n", " ") |
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text = text.strip() |
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if not text.endswith(('.', '!', '?', ":")): |
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# Since we are breaking a longer text into sentences ourselves, we should always end a sentence with a period. |
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text = text + "." |
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return text |
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sentence_splitter = spacy.load("en_core_web_sm") |
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spacy_document = sentence_splitter("This is a long text. It has two or more sentence. Spacy will break it down into sentences.") |
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results = [] |
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for sentence in spacy_document.sents: |
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clean_text = clean_up_sentence(str(sentence)) |
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classification = grammar_checker(clean_text)[0] |
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results.append({ |
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"label": classification['label'], |
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"score": classification['score'], |
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"sentence": clean_text |
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}) |
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pd.DataFrame.from_dict(results) |
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``` |
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# c4-binary-english-grammar-checker |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3546 |
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- Accuracy: 0.8577 |
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- Matthews Correlation: 0.7192 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Matthews Correlation | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:--------------------:| |
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| 0.363 | 1.0 | 200000 | 0.3634 | 0.8487 | 0.7025 | |
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| 0.3032 | 2.0 | 400000 | 0.3546 | 0.8577 | 0.7192 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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