Transformer-based models implemented in tensorflow 2.x(using keras).
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Updated
Nov 23, 2021 - Python
Transformer-based models implemented in tensorflow 2.x(using keras).
[NeurIPS 2023 Main Track] This is the repository for the paper titled "Don’t Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner"
The source code used for paper "Empower Entity Set Expansion via Language Model Probing", published in ACL 2020.
Recent Advances in Vision-Language Pre-training!
Code to reproduce experiments from the paper "Continual Pre-Training Mitigates Forgetting in Language and Vision" https://arxiv.org/abs/2205.09357
Sample tutorials for training Natural Language Processing Models with Transformers
Comparing Selective Masking Methods for Depression Detection in Social Media
😷 The Fill-Mask Association Test (FMAT): Measuring Propositions in Natural Language.
[CHIL 2024] Interpretation of Intracardiac Electrograms Through Textual Representations
A transformer-based language model trained on politics-related Twitter data. This repo is the official resource of the paper "PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter", LREC 2022
Grammar test suite for masked language models
A Context Aware Approach for Generating Natural Language Attacks.
Transformers Intuition
Transformers Pre-Training with MLM objective — implemented encoder-only model and trained from scratch on Wikipedia dataset.
Predict the whole sequence and 3D structure of masked protein sequences with ESM by @evolutionaryscale
[EMNLP 2024] Official Implementation of DisGeM: Distractor Generation for Multiple Choice Question with Span Masking
Final assigment for "Gestione dell'Informazione" ("Search Engines") course @ UniMoRe
Score masked language models on grammar test suites
Measuring Biases in Masked Language Models for PyTorch Transformers. Support for multiple social biases and evaluation measures.
Training the first Cypriot Large Language Model on the Masked Language Modeling objective for predicting a given masked word token within a given context
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