Dalila Ku

Fine-tuning of bert-base-uncased for extractive Question Answering, delivered as part of assignment U2T01 (Adapting BERT for NLP tasks — Trends in Data Science, Unit 2, Universidad Politécnica de Yucatán). bert-base-uncased with a qaoutputs head that predicts, per token, the probability of being the start and the end of the answer span within the given context. (rajpurkar/squad), subsampled to 15,000 examples from the official train split (as required by the assignment), with a 90/10 split used for training/ validation. SQuAD's official validation set (10,570 questions, never seen during training) was reserved as the final test set. - Preprocessing uses a sliding window (stride) since…

Fine-tuning of bert-base-uncased for Part-of-Speech (POS) tagging, delivered as part of assignment U2T01 (Adapting BERT for NLP tasks — Trends in Data Science, Unit 2, Universidad Politécnica de Yucatán). bert-base-uncased with a token-level linear classification head over the last hidden state of every token, predicting one of 17 Universal POS (UPOS) tags (NOUN, VERB, ADJ, DET, PUNCT, etc.). (universal-dependencies/universaldependencies, config enewt). (universaldependencies/universaldependencies) has a typo; the correct organization name on the Hub uses a hyphen (universal-dependencies). - Same per-token label shape as NER, so the same subword-alignment convention is used: the label goes…

token-classification - bert - ner - lhoestq/conll2003 - precision - recall pipelinetag: token-classification basemodel: bert-base-uncased Fine-tuning of bert-base-uncased for Named Entity Recognition (NER), delivered as part of assignment U2T01 (Adapting BERT for NLP tasks — Trends in Data Science, Unit 2, Universidad Politécnica de Yucatán). bert-base-uncased with a token-level linear classification head over the last hidden state of every token, predicting one of 9 BIO-scheme entity tags (person, organization, location, miscellaneous, or none). (lhoestq/conll2003), using its own official train/validation/test splits. split a word into multiple subwords, the label is placed on the first…

Full fine-tuning of bert-base-uncased for 4-class news topic classification, delivered as part of assignment U2T01 (Adapting BERT for NLP tasks — Trends in Data Science, Unit 2, Universidad Politécnica de Yucatán). bert-base-uncased with a linear classification head on top of the [CLS] token's last hidden state, mapping to 4 topic classes: World, Sports, Business, Sci/Tech. - 10% of the official train split was held out as validation; the official test split was left untouched. - 4 balanced classes. Two adaptation methods were trained and compared; full fine-tuning is the delivered model, since it beat the feature-based baseline by a margin well above the run-to-run noise floor (±1–3 points…

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 64 - lrschedulertype: linear - numepochs: 2 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.23.1