Top 9 Fasttext Open-Source Projects
-
-
pytorch-sentiment-analysis
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
-
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
fastlangid
fastlangid, the only language identification package that support cantonese (zh-yue), simplified (zh-hans) and traditional chinese (zh-hant)
-
Romanian-Word-Embeddings
Romanian Word Embeddings. Here you can find pre-trained corpora of word embeddings. Current methods: CBOW, Skip-Gram, Fast-Text (from Gensim library). The .vec and .model files are available for download (all in one archive).
This is a great guide.
Also - despite the fact that language model embedding [1] are currently the hot rage, good old embedding models are more than good enough for most tasks.
With just a bit of tuning, they're generally as good at many sentence embedding tasks [2], and with good libraries [3] you're getting something like 400k sentence/sec on laptop CPU versus ~4k-15k sentences/sec on a v100 for LM embeddings.
When you should use language model embeddings:
- Multilingual tasks. While some embedding models are multilingual aligned (eg. MUSE [4]), you still need to route the sentence to the correct embedding model file (you need something like langdetect). It's also cumbersome, with one 400mb file per language.
For LM embedding models, many are multilingual aligned right away.
- Tasks that are very context specific or require fine-tuning. For instance, if you're making a RAG system for medical documents, the embedding space is best when it creates larger deviations for the difference between seemingly-related medical words.
This means models with more embedding dimensions, and heavily favors LM models over classic embedding models.
1. sbert.net
2. https://collaborate.princeton.edu/en/publications/a-simple-b...
Fasttext related posts
Index
What are some of the best open-source Fasttext projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | gensim | 15,212 |
| 2 | pytorch-sentiment-analysis | 4,211 |
| 3 | magnitude | 1,610 |
| 4 | Fast_Sentence_Embeddings | 603 |
| 5 | sentence-classification | 235 |
| 6 | finalfusion-rust | 85 |
| 7 | fastchess | 83 |
| 8 | fastlangid | 35 |
| 9 | Romanian-Word-Embeddings | 12 |
