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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)
| Date | Stars |
|---|---|
| 2026-07-24 | 2240 |
| 2026-07-25 | 2240 |
| 2026-07-28 | 2240 |
| 2026-07-30 | 2240 |
| 2026-08-06 | 2240 |
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## Chinese NER using Bert BERT for Chinese NER. **update**：其他一些可以参考,包括Biaffine、GlobalPointer等:[examples](https://github.com/lonePatient/TorchBlocks/tree/master/examples) ### dataset list 1. cner: datasets/cner 2. CLUENER: https://github.com/CLUEbenchmark/CLUENER ### model list 1. BERT+Softmax 2. BERT+CRF 3. BERT+Span ### requirement 1. 1.1.0 =< PyTorch < 1.5.0 2. cuda=9.0 3. python3.6+ ### input format Input format (prefer BIOS tag scheme), with each character its label for one line. Sentences are splited with a null line. ```text 美 B-LOC 国 I-LOC 的 O 华 B-PER 莱 I-PER 士 I-PER 我 O 跟 O 他 O ``` ### run the code 1. Modify the configuration information in `run_ner_xxx.py` or `run_ner_xxx.sh` . 2. `sh scripts/run_ner_xxx.sh` **note**: file structure of the model ```text ├── prev_trained_model | └── bert_base | | └── pytorch_model.bin | | └── config.json | | └── vocab.txt | | └── ...... ``` ### CLUENER result The overall performance of BERT on **dev**: | | Accuracy (entity) | Recall (entity) | F1 score (entity) | | ------------ | ------------------ | ------------------ | ------------------ | | BERT+Softmax | 0.7897 | 0.8031 | 0.7963 | | BERT+CRF | 0.7977 | 0.8177 | 0.8076 | | BERT+Span | 0.8132 | 0.8092 | 0.8112 | | BERT+Span+adv | 0.8267 | 0.8073 | **0.8169** | | BERT-small(6 layers)+Span+kd | 0.8241 | 0.7839 | 0.8051 | | BERT+Span+focal_loss | 0.8121 | 0.8008 | 0.8064 | | BERT+Span+label_smoothing | 0.8235 | 0.7946 | 0.8088 | ### ALBERT for CLUENER The overall performance of ALBERT on **dev**: | model | version | Accuracy(entity) | Recall(entity) | F1(entity) | Train time/epoch | | ------ | ------------- | ---------------- | -------------- | ---------- | ---------------- | | albert | base_google | 0.8014 | 0.6908 | 0.7420 | 0.75x | | albert | large_google | 0.8024 | 0.7520 | 0.7763 | 2.1x | | albert | xlarge_google | 0.8286 | 0.7773 | 0.8021 | 6.7x | | bert | google | 0.8118 | 0.8031 | **0.8074** | ----- | | albert | base_bright | 0.8068 | 0.7529 | 0.7789 | 0.75x | | albert | large_bright | 0.8152 | 0.7480 | 0.7802 | 2.2x | | albert | xlarge_bright | 0.8222 | 0.7692 | 0.7948 | 7.3x | ### Cner result The overall performance of BERT on **dev(test)**: | | Accuracy (entity) | Recall (entity) | F1 score (entity) | | ------------ | ------------------ | ------------------ | ------------------ | | BERT+Softmax | 0.9586(0.9566) | 0.9644(0.9613) | 0.9615(0.9590) | | BERT+CRF | 0.9562(0.9539) | 0.9671(**0.9644**) | 0.9616(0.9591) | | BERT+Span | 0.9604(**0.9620**) | 0.9617(0.9632) | 0.9611(**0.9626**) | | BERT+Span+focal_loss | 0.9516(0.9569) | 0.9644(0.9681) | 0.9580(0.9625) | | BERT+Span+label_smoothing | 0.9566(0.9568) | 0.9624(0.9656) | 0.9595(0.9612) |
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Weitang Liu
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:595f375551d6200c, topic:nlp, desc:named entity recognition
matched fp:595f375551d6200c, topic:pytorch