https://github.com/hazyresearch/legalbench
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
Papers with Code 아카이브
Natural Language Inference
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 43
- 보존된 평가 기록
- 505
Natural language inference (NLI) is the task of determining whether a "hypothesis" is true (entailment), false (contradiction), or undetermined (neutral) given a "premise".
Example:
| Premise | Label | Hypothesis |
|---|---|---|
| A man inspects the uniform of a figure in some East Asian country. | contradiction | The man is sleeping. |
| An older and younger man smiling. | neutral | Two men are smiling and laughing at the cats playing on the floor. |
| A soccer game with multiple males playing. | entailment | Some men are playing a sport. |
Approaches used for NLI include earlier symbolic and statistical approaches to more recent deep learning approaches. Benchmark datasets used for NLI include SNLI, MultiNLI, SciTail, among others. You can get hands-on practice on the SNLI task by following this d2l.ai chapter.
Further readings:
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hazyresearch/safari
Hungry Hungry Hippos: Towards Language Modeling with State Space Models
https://github.com/hbaflast/bert-sentiment-analysis-pytorch
https://github.com/hbaflast/bert-sentiment-analysis-tensorflow
https://github.com/hegebharat/SelfDiscovery
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hegebharat/sentiment-Analysis-for-German-Datasets
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/heraclex12/vietpunc
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hieudepchai/BERT_IE
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/hihellohowareyou/RESREF_Chatbot_data_for_Korean
https://github.com/hilberthit/gpt-3
https://github.com/hiyouga/llama-efficient-tuning
https://github.com/hkuds/easyrec
https://github.com/hkunlp/symgen
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
https://github.com/hoangtrungchinh/Bert-SQuAD-v2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hoichunlaw/COVID-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hojjat-mokhtarabadi/promptsource
https://github.com/holgerbovbjerg/data2vec-kws
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/hooman650/supcl-seq
https://github.com/horizonheart/ELMO
https://github.com/hpprc/simple-simcse-ja
A large annotated corpus for learning natural language inference
https://github.com/https-seyhan/BugAI
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/huanghonggit/Mask-Language-Model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/huangxt39/BART_on_COVID_dialogue
https://github.com/huberemanuel/DeBERTa
https://github.com/huggingface/block_movement_pruning
https://github.com/huggingface/node-question-answering
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/huggingface/tflite-android-transformers
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/huggingface/transformers-bloom-inference
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_squeezebert.py
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
https://github.com/huggingface/transformers/tree/main/src/transformers/models/data2vec
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/huggingface/transformers/tree/master/src/transformers/models/byt5
ByT5: Towards a token-free future with pre-trained byte-to-byte models
https://github.com/huggingface/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/i2r-simmc/i2r-simmc-2020
https://github.com/iabd/QuantizedNMT
https://github.com/ialifinaritra/text_summarization
FlauBERT: Unsupervised Language Model Pre-training for French
https://github.com/ibm/graph_ensemble_learning
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.