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README.md
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@@ -24,7 +24,7 @@ We introduce GNER, a **G**enerative **N**amed **E**ntity **R**ecognition framewo
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* 🔁 Reproduction Materials: [Reproduction Materials](https://drive.google.com/drive/folders/1m2FqDgItEbSoeUVo-i18AwMvBcNkZD46?usp=drive_link)
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<p align="center">
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<img src="https://github.com/yyDing1/GNER/
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</p>
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## PreTrained Models
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>>> response = response[preds.find("[/INST]") + len("[/INST]"):].strip()
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>>> print(response)
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"did(O) george(B-actor) clooney(I-actor) make(O) a(O) musical(B-genre) in(O) the(O) 1980s(B-year)"
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## Citation
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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* 🔁 Reproduction Materials: [Reproduction Materials](https://drive.google.com/drive/folders/1m2FqDgItEbSoeUVo-i18AwMvBcNkZD46?usp=drive_link)
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<p align="center">
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<img src="https://github.com/yyDing1/GNER/raw/main/assets/zero_shot_results.png">
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</p>
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## PreTrained Models
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>>> response = response[preds.find("[/INST]") + len("[/INST]"):].strip()
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>>> print(response)
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"did(O) george(B-actor) clooney(I-actor) make(O) a(O) musical(B-genre) in(O) the(O) 1980s(B-year)"
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```
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## Citation
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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