Instructions to use covalenthq/cryptoNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use covalenthq/cryptoNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="covalenthq/cryptoNER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("covalenthq/cryptoNER") model = AutoModelForTokenClassification.from_pretrained("covalenthq/cryptoNER") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Model description
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This model is a fine-tuned version of xlm-roberta-base, specializing in Named Entity Recognition (NER) within the cryptocurrency domain. It is optimized to recognize and classify entities such as cryptocurrency
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## Intended uses
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Designed primarily for NER tasks in the cryptocurrency sector, this model excels in identifying and categorizing
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## Limitations
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## Model description
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This model is a fine-tuned version of xlm-roberta-base, specializing in Named Entity Recognition (NER) within the cryptocurrency domain. It is optimized to recognize and classify entities such as cryptocurrency TICKER SYMBOL, NAME, and blockscanner ADDRESS within text.
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## Intended uses
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Designed primarily for NER tasks in the cryptocurrency sector, this model excels in identifying and categorizing ticker symbol, token name, and blockscanner address in textual content.
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## Limitations
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