Open-Weight Models

Model · Text generation

berna-r7-v3

MOHAMMED KAMIL

libraryname: transformers pipelinetag: text-generation - language-model - berna - dna-kernel-plexus - cells - research - non-commercial 1.2B parameter language model with self-growing cell architecture. 2. Saturation S = 0.8578 (just above threshold 0.85) Comparison: GPT-2 Small (124M) achieves HellaSwag 31.4%, ARC-Easy 44.0%, PIQA 64.1%. Berna R7 v3 (1.2B) matches this at ~15x less training data. code/ Training code config/ config.json cellsruntimecurrent/ Live cells + cognitive log cellsruntimefinal/ Final cell state checkpoints/step00010200/ Final model (with wrapper) checkpoints/step00001700/ Earlier checkpoint data/tokenized/ eng.npy, math.npy, code.npy data/tokenizer/ tokenizer.json…

Model · Text generation

berna-r8-v3

MOHAMMED KAMIL

Each cell that saturates (S >= 0.68, K6 >= 0.03): 3. Gradient hooks - zero gradients for frozen neurons Result: Bounded Forgetting - old knowledge protected while learning new.

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…

Model · Token classification

bert-base-cased-pos-tagging-ewt

Angel Rivaldo Canche Chuc

Modelo bert-base-cased ajustado finamente (Fine-tuned) para la tarea de Part-of-Speech (POS) Tagging utilizando las 17 etiquetas universales (UPOS) del dataset universal-dependencies/universaldependencies (configuración enewt). Etiquetado Morfosintáctico Automático: Clasificación de categorías gramaticales (Part-of-Speech Tagging) en oraciones en idioma inglés. Análisis Lingüístico: Extracción de características sintácticas para sistemas downstream de NLP, desambiguación léxica y procesamiento del lenguaje. Parameter Groups (Método 1 - Partial): classifier: Learning Rate 1e-3 bert.encoder.layer.10 y layer.11: Learning Rate 2e-5 Parameter Groups (Método 2 - Full Ganador): classifier…

Model · Question answering

bert-base-cased-squad-extractive-qa

Russel Ku

This repository provides a fine-tuned bert-base-cased model adapted for the downstream task of Extractive Question Answering on the full SQuAD v1.1 dataset. Given a (Question, Context) pair, the model predicts the exact start and end character/token spans within the context passage that answer the question. The model was evaluated on the complete SQuAD v1.1 validation set (valdataset, 10,570 examples): Our final Epoch 3 checkpoint achieves 88.21% F1 and 80.75% EM, matching the original Google BERT benchmark (-0.29 F1 points) with complete reproducibility on modern GPU hardware and AMP FP16. - Sequence Length (maxlength): 384 tokens - Document Stride (docstride): 128 tokens To better…

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…

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…

This model is a fine-tuned version of BAAI/bge-small-en-v1.5 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 10 - Transformers 4.50.0 - Pytorch 2.11.0+cu128 - Datasets 3.4.1 - Tokenizers 0.21.4