Instructions to use JeswinMS4/bert-base-intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeswinMS4/bert-base-intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeswinMS4/bert-base-intent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeswinMS4/bert-base-intent") model = AutoModelForSequenceClassification.from_pretrained("JeswinMS4/bert-base-intent") - Notebooks
- Google Colab
- Kaggle
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# BERT Base Intent model
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This is a fine tuned model based on Bert-Base-Uncased model. This
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Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
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[this paper](https://arxiv.org/abs/1810.04805) and first released in
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[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
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# BERT Base Intent model
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This is a fine tuned model based on Bert-Base-Uncased model. This model is used to classify intent into 3 categories- Fintech, Out of Scope and Abusive.
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Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
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[this paper](https://arxiv.org/abs/1810.04805) and first released in
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[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
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