TrAVis VS siamese-nn-semantic-text-similarity

Compare TrAVis vs siamese-nn-semantic-text-similarity and see what are their differences.

siamese-nn-semantic-text-similarity

A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task, including architectures such as: Siamese LSTM Siamese BiLSTM with Attention Siamese Transformer Siamese BERT. (by shahrukhx01)
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TrAVis siamese-nn-semantic-text-similarity
1 2
53 45
- -
10.0 0.0
over 1 year ago about 2 years ago
Python Python
- Apache License 2.0
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TrAVis

Posts with mentions or reviews of TrAVis. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-21.

siamese-nn-semantic-text-similarity

Posts with mentions or reviews of siamese-nn-semantic-text-similarity. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing TrAVis and siamese-nn-semantic-text-similarity you can also consider the following projects:

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haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

nncf - Neural Network Compression Framework for enhanced OpenVINO™ inference

bart-base-jax - JAX implementation of the bart-base model

llmware - Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.

d3 - Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:

RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.