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--- |
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language: |
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- es |
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thumbnail: "url to a thumbnail used in social sharing" |
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tags: |
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- tag1 |
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- tag2 |
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license: apache-2.0 |
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datasets: |
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- oscar |
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metrics: |
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- metric1 |
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- metric2 |
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--- |
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# SELECTRA: A Spanish ELECTRA |
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SELECTRA is a Spanish pre-trained language model based on [ELECTRA](https://github.com/google-research/electra). |
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We release a `small` and `medium` version with the following configuration: |
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| Model | Layers | Embedding/Hidden Size | Params | Vocab Size | Max Sequence Length | Cased | |
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| --- | --- | --- | --- | --- | --- | --- | |
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| **SELECTRA small** | **12** | **256** | **22M** | **50k** | **512** | **True** | |
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| SELECTRA medium | 12 | 384 | 41M | 50k | 512 | True | |
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Selectra small is about 5 times smaller than BETO but achieves comparable results (see Metrics section below). |
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## Usage |
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```python |
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from transformers import ElectraForPreTraining, ElectraTokenizerFast |
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discriminator = ElectraForPreTraining.from_pretrained("Recognai/selectra_small") |
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tokenizer = ElectraTokenizerFast.from_pretrained("Recognai/selectra_small") |
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sentence_with_fake_token = "Estamos desayunando pan rosa con tomate y aceite de oliva." |
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inputs = tokenizer.encode(sentence_with_fake_token, return_tensors="pt") |
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logits = discriminator(inputs).logits.tolist()[0] |
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print("\t".join(tokenizer.tokenize(sentence_with_fake_token))) |
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print("\t".join(map(lambda x: str(x)[:4], logits[1:-1]))) |
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"""Output: |
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Estamos desayun ##ando pan rosa con tomate y aceite de oliva . |
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-3.1 -3.6 -6.9 -3.0 0.19 -4.5 -3.3 -5.1 -5.7 -7.7 -4.4 -4.2 |
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""" |
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``` |
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- Links to our zero-shot-classifiers |
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## Metrics |
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We fine-tune our models on 4 different down-stream tasks: |
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- [XNLI](https://huggingface.co/datasets/xnli) |
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- [PAWS-X](https://huggingface.co/datasets/paws-x) |
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- [CoNLL2002 - POS](https://huggingface.co/datasets/conll2002) |
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- [CoNLL2002 - NER](https://huggingface.co/datasets/conll2002) |
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We provide the mean and standard deviation of 5 fine-tuning runs. |
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The metrics |
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| Model | CoNLL2002 - POS (acc) | CoNLL2002 - NER (f1) | PAWS-X (acc) | XNLI (acc) | Params | |
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| --- | --- | --- | --- | --- | --- | |
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| SELECTRA small | 0.9653 +- 0.0007 | 0.863 +- 0.004 | 0.896 +- 0.002 | 0.784 +- 0.002 | 22M | |
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| SELECTRA medium | 0.9677 +- 0.0004 | 0.870 +- 0.003 | 0.896 +- 0.002 | 0.804 +- 0.002 | 41M | |
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| [mBERT](https://huggingface.co/bert-base-multilingual-cased) | 0.9689 | 0.8616 | 0.8895 | 0.7606 | 178M | |
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| [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) | 0.9693 | 0.8596 | 0.8720 | 0.8012 | 110M | |
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| [BSC-BNE](https://huggingface.co/BSC-TeMU/roberta-base-bne) | 0.9706 | 0.8764 | 0.8815 | 0.7771 | 125M | |
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| [Bertin](https://huggingface.co/bertin-project/bertin-roberta-base-spanish/tree/v1-512) | 0.9697 | 0.8707 | 0.8965 | 0.7843 | 125M | |
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## Training |
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- Link to our repo |
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## Motivation |
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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. |
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## Acknowledgment |
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This research was supported by the use of the Google TPU Research Cloud (TRC). |
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## Authors |
