Update README.md

README.md

@@ -7,7 +7,7 @@ tags:
 - tag2
 license: apache-2.0
 datasets:
-- Oscar
+- oscar
 metrics:
 - metric1
 - metric2
@@ -20,16 +20,20 @@ We release a `small` and `medium` version with the following configuration:
 
 | Model | Layers | Embedding/Hidden Size | Params | Vocab Size | Max Sequence Length | Cased |
 | --- | --- | --- | --- | ---  | --- | --- |
-| SELECTRA small | 12 | 256 | 22M | 50k | 512 | True |
+| **SELECTRA small** | **12** | **256** | **22M** | **50k** | **512** | **True** |
 | SELECTRA medium | 12 | 384 | 41M | 50k | 512 | True |
 
+Selectra small is about 5 times smaller than BETO but achieves comparable results (see Metrics section below).
+
 ## Usage
 
+
+
 ```python
 from transformers import ElectraForPreTraining, ElectraTokenizerFast
 
-discriminator = ElectraForPreTraining.from_pretrained("...")
-tokenizer = ElectraTokenizerFast.from_pretrained("...")
+discriminator = ElectraForPreTraining.from_pretrained("Recognai/selectra_small")
+tokenizer = ElectraTokenizerFast.from_pretrained("Recognai/selectra_small")
 
 sentence_with_fake_token = "Estamos desayunando pan rosa con tomate y aceite de oliva."
 
@@ -55,10 +59,20 @@ We fine-tune our models on 4 different down-stream tasks:
  - [CoNLL2002 - POS](https://huggingface.co/datasets/conll2002)
  - [CoNLL2002 - NER](https://huggingface.co/datasets/conll2002)
  
-We provide the mean and standard deviation of 5 fine-tuning runs. 
-| Model | 
-|
- 
+We provide the mean and standard deviation of 5 fine-tuning runs.
+
+The metrics 
+
+
+| Model | CoNLL2002 - POS (acc) | CoNLL2002 - NER (f1) | PAWS-X (acc) | XNLI (acc) | Params |
+| --- | --- | --- | --- | --- | --- |
+| SELECTRA small | 0.9653 +- 0.0007 | 0.863 +- 0.004 | 0.896 +- 0.002 | 0.784 +- 0.002 | 22M |
+| SELECTRA medium | 0.9677 +- 0.0004 | 0.870 +- 0.003 | 0.896 +- 0.002 | 0.804 +- 0.002 | 41M |
+| [mBERT](https://huggingface.co/bert-base-multilingual-cased) | 0.9689 | 0.8616 | 0.8895 | 0.7606 | 178M |
+| [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) | 0.9693 | 0.8596 | 0.8720 | 0.8012 | 110M |
+| [BSC-BNE](https://huggingface.co/BSC-TeMU/roberta-base-bne) | 0.9706 | 0.8764 | 0.8815 | 0.7771 | 125M |
+| [Bertin](https://huggingface.co/bertin-project/bertin-roberta-base-spanish/tree/v1-512) | 0.9697 | 0.8707 | 0.8965 | 0.7843 | 125M |
+
 
 ## Training  
 
@@ -66,4 +80,10 @@ We provide the mean and standard deviation of 5 fine-tuning runs.
 
 ## Motivation
 
-Despite the abundance of excelent Spanish language models (BETO, bertin, etc) we felt there was still a lack of distilled or compact models with comparable metrics to their bigger siblings.
+Despite the abundance of excelent Spanish language models (BETO, bertin, etc) we felt there was still a lack of distilled or compact models with comparable metrics to their bigger siblings.
+
+## Acknowledgment
+
+This research was supported by the use of the Google TPU Research Cloud (TRC).
+
+## Authors