Instructions to use mrm8488/bert2bert_shared-turkish-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/bert2bert_shared-turkish-summarization with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="mrm8488/bert2bert_shared-turkish-summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mrm8488/bert2bert_shared-turkish-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/bert2bert_shared-turkish-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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[dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) (BERT Checkpoint)
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## Dataset
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[MLSUM tu/tr](https://huggingface.co/datasets/viewer/?dataset=mlsum)
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## Results
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|Set|Metric| Value|
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| Test |Rouge2 - mid -precision | 32.41|
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| Test | Rouge2 - mid - recall | 28.65|
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| Test | Rouge2 - mid - fmeasure | 29.48|
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## Usage
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output = model.generate(input_ids, attention_mask=attention_mask)
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return tokenizer.decode(output[0], skip_special_tokens=True)
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text = "Your text here..."
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generate_summary(text)
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[dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) (BERT Checkpoint)
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## Dataset
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**MLSUM** is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, **Turkish**. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.
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[MLSUM tu/tr](https://huggingface.co/datasets/viewer/?dataset=mlsum)
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## Results
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|Set|Metric| Value|
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| Test |Rouge2 - mid -precision | **32.41**|
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| Test | Rouge2 - mid - recall | **28.65**|
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| Test | Rouge2 - mid - fmeasure | **29.48**|
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## Usage
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output = model.generate(input_ids, attention_mask=attention_mask)
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return tokenizer.decode(output[0], skip_special_tokens=True)
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text = "Your text here..."
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generate_summary(text)
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```
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> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) with the support of [Narrativa](https://www.narrativa.com/)
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> Made with <span style="color: #e25555;">♥</span> in Spain
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