Instructions to use projecte-aina/multiner_ceil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use projecte-aina/multiner_ceil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="projecte-aina/multiner_ceil")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("projecte-aina/multiner_ceil") model = AutoModelForTokenClassification.from_pretrained("projecte-aina/multiner_ceil") - Notebooks
- Google Colab
- Kaggle
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README.md
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At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
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## Training
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We used the NERC dataset in Catalan called [Catalan Entity Identification and Linking](https://huggingface.co/datasets/
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## Evaluation
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At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
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## Training
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We used the NERC dataset in Catalan called [Catalan Entity Identification and Linking](https://huggingface.co/datasets/projecte-aina/ceil) for training and evaluation.
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## Evaluation
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