Instructions to use Mhammad2023/distilbert-base-uncased-fineTuned-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mhammad2023/distilbert-base-uncased-fineTuned-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mhammad2023/distilbert-base-uncased-fineTuned-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mhammad2023/distilbert-base-uncased-fineTuned-imdb") model = AutoModelForMaskedLM.from_pretrained("Mhammad2023/distilbert-base-uncased-fineTuned-imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Mhammad2023/distilbert-base-uncased-fineTuned-imdb
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [Large Movie Review Dataset (or IMDb for short)]https://huggingface.co/datasets/stanfordnlp/imdb.
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It achieves the following results on the evaluation set:
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- Train Loss: 2.6554
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- Validation Loss: 2.4705
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# Mhammad2023/distilbert-base-uncased-fineTuned-imdb
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [Large Movie Review Dataset (or IMDb for short)](https://huggingface.co/datasets/stanfordnlp/imdb).
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It achieves the following results on the evaluation set:
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- Train Loss: 2.6554
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- Validation Loss: 2.4705
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