Instructions to use Abirate/bert_fine_tuned_cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abirate/bert_fine_tuned_cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abirate/bert_fine_tuned_cola")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abirate/bert_fine_tuned_cola") model = AutoModelForSequenceClassification.from_pretrained("Abirate/bert_fine_tuned_cola") - Notebooks
- Google Colab
- Kaggle
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- Next sentence prediction (NSP)
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## Fine-tuned Model Description: BERT fine-tuned Cola
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The pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this
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By fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable
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- Next sentence prediction (NSP)
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## Fine-tuned Model Description: BERT fine-tuned Cola
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The pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this GLUE BENCHMARK.
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By fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable
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