language:
- es
thumbnail: url to a thumbnail used in social sharing
license: apache-2.0
datasets:
- oscar
SELECTRA: A Spanish ELECTRA
SELECTRA is a Spanish pre-trained language model based on ELECTRA.
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 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
From the original ELECTRA model card: "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." The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:
from transformers import ElectraForPreTraining, ElectraTokenizerFast
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."
inputs = tokenizer.encode(sentence_with_fake_token, return_tensors="pt")
logits = discriminator(inputs).logits.tolist()[0]
print("\t".join(tokenizer.tokenize(sentence_with_fake_token)))
print("\t".join(map(lambda x: str(x)[:4], logits[1:-1])))
"""Output:
Estamos desayun ##ando pan rosa con tomate y aceite de oliva .
-3.1 -3.6 -6.9 -3.0 0.19 -4.5 -3.3 -5.1 -5.7 -7.7 -4.4 -4.2
"""
However, you probably want to use this model to fine-tune it on a down-stream task.
- Links to our zero-shot-classifiers
Metrics
We fine-tune our models on 4 different down-stream tasks:
For each task, we conduct 5 trials and state the mean and standard deviation of the metrics in the table below. To compare our results to other Spanish language models, we provide the same metrics taken from Table 4 of the Bertin-project model card.
| 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 | 0.9689 | 0.8616 | 0.8895 | 0.7606 | 178M |
| BETO | 0.9693 | 0.8596 | 0.8720 | 0.8012 | 110M |
| BSC-BNE | 0.9706 | 0.8764 | 0.8815 | 0.7771 | 125M |
| Bertin | 0.9697 | 0.8707 | 0.8965 | 0.7843 | 125M |
Some details of our fine-tuning runs:
- epochs: 5
- batch-size: 32
- learning rate: 1e-4
- warmup proportion: 0.1
- linear learning rate decay
- layerwise learning rate decay
For all the details, check out our selectra repo.
Training
We pre-trained our SELECTRA models on the Spanish portion of the Oscar dataset, which is about 150GB in size. Each model version is trained for 300k steps, with a warm restart of the learning rate after the first 150k steps. Some details of the training:
- steps: 300k
- batch-size: 128
- learning rate: 5e-4
- warmup steps: 10k
- linear learning rate decay
- TPU cores: 8 (v2-8)
For all details, check out our selectra repo.
Note: Due to a misconfiguration in the pre-training scripts the embeddings of the vocabulary containing an accent were not optimized. If you fine-tune this model on a down-stream task, you might consider using a tokenizer that does not strip the accents:
tokenizer = ElectraTokenizerFast.from_pretrained("Recognai/selectra_small", strip_accents=False)
Motivation
Despite the abundance of excellent Spanish language models (BETO, BSC-BNE, Bertin, ELECTRICIDAD, etc.), we felt there was still a lack of distilled or compact Spanish language models and a lack of comparing those to their bigger siblings.
Acknowledgment
This research was supported by the Google TPU Research Cloud (TRC) program.