Instructions to use UMCU/PII_XMLR.eu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMCU/PII_XMLR.eu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="UMCU/PII_XMLR.eu", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("UMCU/PII_XMLR.eu", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("UMCU/PII_XMLR.eu", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -27,6 +27,8 @@ Finetuning was done using [MedNER](https://github.com/UPOD-datascience/MedNER.nl
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We limited the training to the following languages: English, Dutch, French, German, Spanish, Italian, Danish, Swedish and Portuguese,
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which are relatively close, in a linguistic sense, allowing for a languages to benefit from cross-over information.
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```bash
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#!/usr/bin/env bash
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set -euo pipefail
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We limited the training to the following languages: English, Dutch, French, German, Spanish, Italian, Danish, Swedish and Portuguese,
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which are relatively close, in a linguistic sense, allowing for a languages to benefit from cross-over information.
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The model was train in a multiclass-sense, using a cross-entropy loss. We replaced the standard 768-weight linear layer
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by 3x768 dense layers with 10\% dropout and ReLu activations.
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```bash
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#!/usr/bin/env bash
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set -euo pipefail
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