Transformers
PyTorch
Safetensors
English
bart
text2text-generation
seq2seq
relation-extraction
triple-generation
entity-linking
entity-type-linking
relation-linking
Eval Results (legacy)
Instructions to use ibm-research/knowgl-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibm-research/knowgl-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ibm-research/knowgl-large") model = AutoModelForSeq2SeqLM.from_pretrained("ibm-research/knowgl-large") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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name: Babelscape/rebel-dataset
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type: REBEL
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metrics:
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- type: re+
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value: 70.74
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name: RE+
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---
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# KnowGL: Knowledge Generation and Linking from Text
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```
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If there are more than one triples generated, they are separated by `$` in the output. More details in [Rossiello et al. (AAAI 2023)](https://arxiv.org/pdf/2210.13952.pdf).
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The model achieves
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The generated labels (for the subject, relation, and object) and their types can be directly mapped to Wikidata IDs associated with them.
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name: Babelscape/rebel-dataset
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type: REBEL
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metrics:
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- type: re+ micro f1
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value: 70.74
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name: RE+ Micro F1
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---
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# KnowGL: Knowledge Generation and Linking from Text
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```
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If there are more than one triples generated, they are separated by `$` in the output. More details in [Rossiello et al. (AAAI 2023)](https://arxiv.org/pdf/2210.13952.pdf).
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The model achieves good results for relation extraction on the REBEL dataset. See results in [Mihindukulasooriya et al. (ISWC 2022)](https://arxiv.org/pdf/2207.05188.pdf).
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The generated labels (for the subject, relation, and object) and their types can be directly mapped to Wikidata IDs associated with them.
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