Instructions to use damand2061/innermore-x-indobertweet-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use damand2061/innermore-x-indobertweet-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="damand2061/innermore-x-indobertweet-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("damand2061/innermore-x-indobertweet-base-uncased") model = AutoModelForTokenClassification.from_pretrained("damand2061/innermore-x-indobertweet-base-uncased") - Notebooks
- Google Colab
- Kaggle
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# damand2061/innermore-x-indobertweet-base-uncased
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This model is a fine-tuned version of [indolem/indobertweet-base-uncased](https://huggingface.co/indolem/indobertweet-base-uncased) on [Innermore-X dataset](https://huggingface.co/damand2061/innermore-x), an Indonesian NER Movie Reviews on X (Twitter).
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0062
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- Validation Loss: 0.1791
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# damand2061/innermore-x-indobertweet-base-uncased
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This model is a fine-tuned version of [indolem/indobertweet-base-uncased](https://huggingface.co/indolem/indobertweet-base-uncased) on [Innermore-X dataset](https://huggingface.co/datasets/damand2061/innermore-x), an Indonesian NER Movie Reviews on X (Twitter).
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0062
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- Validation Loss: 0.1791
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