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Error:
"language[0]" with value "dutch" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.
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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
t5-base-dutch-demo 📰
Created by Yeb Havinga & Dat Nguyen during the Hugging Face community week
This model is based on t5-base-dutch and fine-tuned to create summaries of news articles.
For a demo of the model, head over to the Hugging Face Spaces for the Netherformer 📰 example application!
Dataset
t5-base-dutch-demo is fine-tuned on three mixed news sources:
- CNN DailyMail translated to Dutch with MarianMT.
- XSUM translated to Dutch with MarianMt.
- News article summaries distilled from the nu.nl website.
The total number of training examples in this dataset is 1366592.
Training
Training consisted of fine-tuning t5-base-dutch with the following parameters:
- Constant learning rate 0.0005
- Batch size 8
- 1 epoch (170842 steps)
Evaluation
The performance of the summarization model is measured with the Rouge metric from the Huggingface Datasets library.
"rouge{n}" (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
"rougeL": Longest common subsequence based scoring.
- Rouge1: 23.8
- Rouge2: 6.9
- RougeL: 19.7
These scores are expected to improve if the model is trained with evaluation configured for the CNN DM and XSUM datasets (translated to Dutch) individually.
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