Riccorl/transformers-embedder
A Word Level Transformer layer based on PyTorch and 🤗 Transformers.
This tool helps developers working with Natural Language Processing (NLP) to extract meaningful 'word embeddings' from transformer models. It takes text inputs, processes them using popular transformer models like BERT, and outputs numerical representations for each word, rather than sub-word fragments. This is useful for NLP engineers building systems for tasks like text classification, sentiment analysis, or machine translation.
No commits in the last 6 months. Available on PyPI.
Use this if you are an NLP engineer or researcher who needs to obtain word-level embeddings from transformer models for further downstream processing or analysis.
Not ideal if you are an end-user without programming experience, or if you need to train a transformer model from scratch.
Stars
34
Forks
5
Language
Python
License
—
Category
Last pushed
Jan 31, 2024
Commits (30d)
0
Dependencies
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/Riccorl/transformers-embedder"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
MinishLab/model2vec
Fast State-of-the-Art Static Embeddings
AnswerDotAI/ModernBERT
Bringing BERT into modernity via both architecture changes and scaling
tensorflow/hub
A library for transfer learning by reusing parts of TensorFlow models.
Embedding/Chinese-Word-Vectors
100+ Chinese Word Vectors 上百种预训练中文词向量
twang2218/vocab-coverage
语言模型中文认知能力分析