We demonstrate all pre-built applications in HugNLP.
| Applications | Runing Tasks | Task Notes | PLM Models | Documents |
|---|---|---|---|---|
| Default Application | run_seq_cls.sh | Goal: Standard Fine-tuning or Prompt-tuning for sequence classification on user-defined dataset. Path: applications/default_applications |
BERT, RoBERTa, DeBERTa | click |
| run_seq_labeling.sh | Goal: Standard Fine-tuning for sequence labeling on user-defined dataset. Path: applications/default_applications |
BERT, RoBERTa, ALBERT | ||
| Pre-training | run_pretrain_mlm.sh | Goal: Pre-training via Masked Language Modeling (MLM). Path: applications/pretraining/ |
BERT, RoBERTa | click |
| run_pretrain_casual_lm.sh | Goal: Pre-training via Causal Language Modeling (CLM). Path: applications/pretraining |
BERT, RoBERTa | click | |
| GLUE Benchmark | run_glue.sh | Goal: Standard Fine-tuning or Prompt-tuning for GLUE classification tasks. Path: applications/benchmark/glue |
BERT, RoBERTa, DeBERTa | |
| run_causal_incontext_glue.sh | Goal: In-context learning for GLUE classification tasks. Path: applications/benchmark/glue |
GPT-2 | ||
| CLUE Benchmark | clue_finetune_dev.sh | Goal: Standard Fine-tuning and Prompt-tuning for CLUE classification task。 Path: applications/benchmark/clue |
BERT, RoBERTa, DeBERTa | |
| run_clue_cmrc.sh | Goal: Standard Fine-tuning for CLUE CMRC2018 task. Path: applications/benchmark/cluemrc |
BERT, RoBERTa, DeBERTa | ||
| run_clue_c3.sh | Goal: Standard Fine-tuning for CLUE C3 task. Path: applications/benchmark/cluemrc |
BERT, RoBERTa, DeBERTa | ||
| run_clue_chid.sh | Goal: Standard Fine-tuning for CLUE CHID task. Path: applications/benchmark/cluemrc |
BERT, RoBERTa, DeBERTa | ||
| Instruction-Prompting | run_causal_instruction.sh | Goal: Cross-task training via generative Instruction-tuning based on causal PLM. You can use it to train a small ChatGPT. Path: applications/instruction_prompting/instruction_tuning |
GPT2 | click |
| run_zh_extract_instruction.sh | Goal: Cross-task training via extractive Instruction-tuning based on Global Pointer model. Path: applications/instruction_prompting/chinese_instruction |
BERT, RoBERTa, DeBERTa | click | |
| run_causal_incontext_cls.sh | Goal: In-context learning for user-defined classification tasks. Path: applications/instruction_prompting/incontext_learning |
GPT-2 | click | |
| Information Extraction | run_extractive_unified_ie.sh | Goal: HugIE: training a unified chinese information extraction via extractive instruction-tuning. Path: applications/information_extraction/HugIE |
BERT, RoBERTa, DeBERTa | click |
| api_test.py | Goal: HugIE: API test. Path: applications/information_extraction/HugIE |
- | click | |
| run_fewnerd.sh | Goal: Prototypical learning for named entity recognition, including SpanProto, TokenProto Path: applications/information_extraction/fewshot_ner |
BERT | ||
| Code NLU | run_clone_cls.sh | Goal: Standard Fine-tuning for code clone classification task. Path: applications/code/code_clone |
CodeBERT, CodeT5, GraphCodeBERT, PLBART | click |
| run_defect_cls.sh | Goal: Standard Fine-tuning for code defect classification task. Path: applications/code/code_defect |
CodeBERT, CodeT5, GraphCodeBERT, PLBART | click |
We show the settings that matched with each pre-built application.
Notes:
- ✅: Have finished
- ⌛️: To do
- ⛔️: Not-available
| Applications | Runing Tasks | Adv-training | Parameter-efficient | Pattern-Verbalizer | Instruction-Prompting | Self-training | Calibration |
|---|---|---|---|---|---|---|---|
| Default Application | run_seq_cls.sh | ✅ | ✅ | ✅ | |||
| run_seq_labeling.sh | ✅ | ✅ | ✅ | ||||
| Pre-training | run_pretrain_mlm.sh | ✅ | |||||
| run_pretrain_casual_lm.sh | ✅ | ||||||
| GLUE Benchmark | run_glue.sh | ✅ | ✅ | ✅ | |||
| run_causal_incontext_glue.sh | ✅ | ✅ | ✅ | ✅ | ✅ | ||
| CLUE Benchmark | clue_finetune_dev.sh | ✅ | ✅ | ✅ | |||
| run_clue_cmrc.sh | ✅ | ||||||
| run_clue_c3.sh | ✅ | ||||||
| run_clue_chid.sh | ✅ | ||||||
| Instruction-Prompting | run_causal_instruction.sh | ✅ | ✅ | ||||
| run_zh_extract_instruction.sh | ✅ | ✅ | ✅ | ||||
| run_causal_incontext_cls.sh | ⛔️ | ⛔️ | ✅ | ✅ | ✅ | ||
| Information Extraction | run_extractive_unified_ie.sh | ✅ | |||||
| api_test.py | ⛔️ | ||||||
| run_fewnerd.sh | ✅ | ||||||
| Code NLU | run_clone_cls.sh | ✅ | ✅ | ||||
| run_defect_cls.sh | ✅ | ✅ |