Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo
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Updated
May 9, 2024 - Python
Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo
Tencent Pre-training framework in PyTorch & Pre-trained Model Zoo
Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing
🤖 A PyTorch library of curated Transformer models and their composable components
This repository contains the official release of the model "BanglaBERT" and associated downstream finetuning code and datasets introduced in the paper titled "BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla" accpeted in Findings of the Annual Conference of the North American Chap…
CINO: Pre-trained Language Models for Chinese Minority (少数民族语言预训练模型)
Unattended Lightweight Text Classifiers with State-of-the-Art LLM Embeddings
An implementation of drophead regularization for pytorch transformers
Deep-learning system proposed by HFL for SemEval-2022 Task 8: Multilingual News Similarity
Resources and tools for the Tutorial - "Hate speech detection, mitigation and beyond" presented at ICWSM 2021
Improving Low-Resource Neural Machine Translation of Related Languages by Transfer Learning
Improving Bilingual Lexicon Induction with Cross-Encoder Reranking (Findings of EMNLP 2022). Keywords: Bilingual Lexicon Induction, Word Translation, Cross-Lingual Word Embeddings.
Our source code for EACL2021 workshop: Offensive Language Identification in Dravidian Languages. We ranked 4th, 4th and 3rd in Tamil, Malayalam and Kannada language of this task finally!🥳
Language Model Decomposition: Quantifying the Dependency and Correlation of Language Models
Khmer New Classification with XLM-RoBERTa
Fine-tuning Question Answering models on German with the GermanQuAD dataset
Masked Language Modeling demo using XLM-RoBERTa + Gradio/FastAPI
Teknofest 2023 Doğal Dil İşleme Yarışması
[KDD 2024] Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning
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