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Content: GTR, XRD identification, Flair #8

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Jan 25, 2022
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Content: GTR, XRD identification, Flair #8

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Content: GTR, XRD identification, Flair
monatis Jan 25, 2022
5eb6dec
Move Flair to applications
monatis Jan 25, 2022
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Fix conflict
monatis Jan 25, 2022
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23 changes: 22 additions & 1 deletion README.md
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Expand Up @@ -59,6 +59,20 @@ space as CLIP.</summary>
> It is an open-class object detector to detect any label encoded by CLIP without finetuning. See [demo](https://huggingface.co/spaces/akhaliq/Detic).
</details>

<details>
<summary><a href="https://tfhub.dev/google/collections/gtr/1">GTR</a> - Collection of Generalizable T5-based dense Retrievers (GTR) models.</summary>

> TensorFlow Hub offers a collection of pretrained models from the paper [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899).
> GTR models are first initialized from a pre-trained T5 checkpoint. They are then further pre-trained with a set of community question-answer pairs. Finally, they are fine-tuned on the MS Marco dataset.
> The two encoders are shared so the GTR model functions as a single text encoder. The input is variable-length English text and the output is a 768-dimensional vector.
</details>

<details>
<summary><a href="https://github.com/flairNLP/flair/blob/master/resources/docs/TUTORIAL_10_TRAINING_ZERO_SHOT_MODEL.md">TARS</a> - Task-aware representation of sentences, a novel method for several zero-shot tasks including NER</summary>

> The method and pretrained models found in Flair go beyond zero-shot sequence classification and offers zero-shot span tagging abilities for tasks such as named entity recognition and part of speech tagging.
</details>

<details>
<summary><a href="https://github.com/MaartenGr/BERTopic">BERTopic</a> - A novel topic modeling toolkit with BERT
embeddings.</summary>
Expand All @@ -73,6 +87,13 @@ high-dimensional data.</summary>
> It supports UMAP, T-SNE, PCA, or custom techniques to analyze embeddings of encoders.
</details>

<details>
<summary><a href="https://github.com/ma921/XRDidentifier">XRD Identifier</a> - Fingerprinting substances with metric learning</summary>

> Identification of substances based on spectral analysis plays a vital role in forensic science. Similarly, the material identification process is of paramount importance for malfunction reasoning in manufacturing sectors and materials research.
> This models enables to identify materials with deep metric learning applied to X-Ray Diffraction (XRD) spectrum. Read [this post](https://towardsdatascience.com/automatic-spectral-identification-using-deep-metric-learning-with-1d-regnet-and-adacos-8b7fb36f2d5f) for more background.
</details>


## Libraries 🧰

Expand Down Expand Up @@ -271,4 +292,4 @@ serving as a useful benchmark.</summary>
<summary><a href="https://www.drivendata.org/competitions/79/">MetaAI's 2021 Image Similarity Dataset and Challenge</a> - dataset has 1M Reference image set, 1M Training image set, 50K Dev query image set and 50K Test query image set</summary>

> The dataset is published along with ["The 2021 Image Similarity Dataset and Challenge"](http://arxiv.org/abs/2106.09672) paper.
</details>
</details>