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README.md
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metrics:
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- accuracy
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---
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- stanfordnlp/imdb
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language:
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- en
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base_model:
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- distilbert/distilbert-base-uncased
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tags:
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- torch
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- code
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library_name: transformers
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pipeline_tag: text-classification
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---
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# Model Card for DistilBERT Fine-Tuned on IMDB Sentiment Analysis
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This model is a fine-tuned version of `DistilBERT` for sentiment analysis on the IMDB movie reviews dataset. It classifies movie reviews into two categories: positive and negative sentiments.
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This model has been fine-tuned on the IMDB dataset, which contains movie reviews labeled with sentiments: `positive` or `negative`. The model is based on the `DistilBERT` architecture, which is a lighter, more efficient variant of BERT, offering faster inference without significantly sacrificing accuracy.
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- **Developed by:**
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- **Shared by [optional]:**
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- **Model type:** Transformer-based model for text classification (sentiment analysis)
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- **Language(s) (NLP):** English
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- **Finetuned from model [optional]:** distilbert-base-uncased
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metrics:
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- accuracy
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# Model Card for DistilBERT Fine-Tuned on IMDB Sentiment Analysis
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This model is a fine-tuned version of `DistilBERT` for sentiment analysis on the IMDB movie reviews dataset. It classifies movie reviews into two categories: positive and negative sentiments.
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This model has been fine-tuned on the IMDB dataset, which contains movie reviews labeled with sentiments: `positive` or `negative`. The model is based on the `DistilBERT` architecture, which is a lighter, more efficient variant of BERT, offering faster inference without significantly sacrificing accuracy.
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- **Developed by:** Leonuraht/Scilineo
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- **Shared by [optional]:** Leonuraht/Scilineo
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- **Model type:** Transformer-based model for text classification (sentiment analysis)
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- **Language(s) (NLP):** English
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- **Finetuned from model [optional]:** distilbert-base-uncased
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