mrm8488 commited on
Commit
817e91b
·
1 Parent(s): a8ee138

Create README.md

Browse files
Files changed (1) hide show
  1. README.md +36 -0
README.md ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: es
3
+ tags:
4
+ - Spanish
5
+ - Electra
6
+ - Legal
7
+ datasets:
8
+ - Spanish-legal-corpora
9
+ ---
10
+ ## LEGALECTRA ⚖️
11
+ **LEGALECTRA** (base) is an Electra like model (discriminator in this case) trained on [A collection of corpora of Spanish legal domain](https://zenodo.org/record/5495529#.YZItp3vMLJw).
12
+ As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB):
13
+ **ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
14
+ For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
15
+ ## Training details
16
+ TBA
17
+ ## Model details ⚙
18
+ |Param| # Value|
19
+ |-----|--------|
20
+ |Layers| 12 |
21
+ |Hidden | 256 |
22
+ |Params| 14M |
23
+ ## Evaluation metrics (for discriminator) 🧾
24
+ |Metric | # Score |
25
+ |-------|---------|
26
+ |Accuracy| 0.941|
27
+ |AUC | 0.794|
28
+ |Precision| |
29
+ ## Benchmarks 🔨
30
+ WIP 🚧
31
+ ## How to use the discriminator in `transformers`
32
+ TBA
33
+ ## Acknowledgments
34
+ TBA
35
+ > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
36
+ > Made with <span style="color: #e25555;">&hearts;</span> in Spain