| # nlp-flashcard-project | |
| ## Todo 2 | |
| - [ ] Contexts preprocessing | |
| - [ ] Formules enzo eruit filteren | |
| - [ ] Splitsen op zinnen...? | |
| - [ ] Meer language models proberen | |
| - [ ] Elasticsearch | |
| - [ ] CLI voor vragen beantwoorden | |
| ### Extra dingen | |
| - [ ] Huggingface spaces demo | |
| - [ ] Question generation voor finetuning | |
| - [ ] Language model finetunen | |
| ## Todo voor progress meeting | |
| - [ ] Data inlezen/Repo klaarmaken | |
| - [ ] Proof of concept met UnifiedQA | |
| - [ ] Standaard QA model met de dataset | |
| - [ ] Papers verzamelen/lezen | |
| - [ ] Eerder werk bekijken, inspiratie opdoen voor research richting | |
| ## Overview | |
| De meeste QA systemen bestaan uit twee onderdelen: | |
| - Een retriever. Die haalt adhv de vraag _k_ relevante stukken context op, bv. | |
| met `tf-idf`. | |
| - Een model dat het antwoord genereert. Wat je hier precies gebruikt hangt af | |
| van de manier van question answering: | |
| - Voor **extractive QA** gebruik je een reader; | |
| - Voor **generative QA** gebruik je een generator. | |
| Beide werken op basis van een language model. | |
| ## Handige info | |
| - Huggingface QA tutorial: <https://huggingface.co/docs/transformers/tasks/question_answering#finetune-with-tensorflow> | |
| - Overview van open-domain question answering technieken: <https://lilianweng.github.io/posts/2020-10-29-odqa/> | |
| ## Base model | |
| Tot nu toe alleen een retriever die adhv een vraag de top-k relevante documents | |
| ophaalt. Haalt voor veel vragen wel hoge similarity scores, maar de documents | |
| die die ophaalt zijn meestal niet erg relevant. | |
| ```bash | |
| poetry shell | |
| cd base_model | |
| poetry run python main.py | |
| ``` | |
| ### Voorbeeld | |
| "What is the perplexity of a language model?" | |
| > Result 1 (score: 74.10): | |
| > Figure 10 .17 A sample alignment between sentences in English and French, with | |
| > sentences extracted from Antoine de Saint-Exupery's Le Petit Prince and a | |
| > hypothetical translation. Sentence alignment takes sentences e 1 , ..., e n , | |
| > and f 1 , ..., f n and finds minimal > sets of sentences that are translations | |
| > of each other, including single sentence mappings like (e 1 ,f 1 ), (e 4 -f 3 | |
| > ), (e 5 -f 4 ), (e 6 -f 6 ) as well as 2-1 alignments (e 2 /e 3 ,f 2 ), (e 7 | |
| > /e 8 -f 7 ), and null alignments (f 5 ). | |
| > | |
| > Result 2 (score: 74.23): | |
| > Character or word overlap-based metrics like chrF (or BLEU, or etc.) are | |
| > mainly used to compare two systems, with the goal of answering questions like: | |
| > did the new algorithm we just invented improve our MT system? To know if the | |
| > difference between the chrF scores of two > MT systems is a significant | |
| > difference, we use the paired bootstrap test, or the similar randomization | |
| > test. | |
| > | |
| > Result 3 (score: 74.43): | |
| > The model thus predicts the class negative for the test sentence. | |
| > | |
| > Result 4 (score: 74.95): | |
| > Translating from languages with extensive pro-drop, like Chinese or Japanese, | |
| > to non-pro-drop languages like English can be difficult since the model must | |
| > somehow identify each zero and recover who or what is being talked about in | |
| > order to insert the proper pronoun. | |
| > | |
| > Result 5 (score: 76.22): | |
| > Similarly, a recent challenge set, the WinoMT dataset (Stanovsky et al., 2019) | |
| > shows that MT systems perform worse when they are asked to translate sentences | |
| > that describe people with non-stereotypical gender roles, like "The doctor | |
| > asked the nurse to help her in the > operation". | |
| ## Setting up elastic search. | |