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@@ -15,7 +15,7 @@ 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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@@ -43,9 +43,11 @@ Estamos desayun ##ando pan rosa con tomate y aceite de
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  """
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  ```
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- However, you probably want to use this model to fine-tune it on a down-stream task.
 
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- - Links to our zero-shot-classifiers
 
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  ## Metrics
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@@ -59,7 +61,7 @@ We fine-tune our models on 4 different down-stream tasks:
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  For each task, we conduct 5 trials and state the mean and standard deviation of the metrics in the table below.
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  To compare our results to other Spanish language models, we provide the same metrics taken from [Table 4](https://huggingface.co/bertin-project/bertin-roberta-base-spanish#results) of the Bertin-project model card.
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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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  | 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](https://huggingface.co/Recognai/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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  """
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  ```
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+ However, you probably want to use this model to fine-tune it on a downstream task.
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+ We provide models fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), which can be used together with the zero-shot classification pipeline:
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+ - [Zero-shot SELECTRA small](https://huggingface.co/Recognai/zeroshot_selectra_small)
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+ - [Zero-shot SELECTRA medium](https://huggingface.co/Recognai/zeroshot_selectra_medium)
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  ## Metrics
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  For each task, we conduct 5 trials and state the mean and standard deviation of the metrics in the table below.
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  To compare our results to other Spanish language models, we provide the same metrics taken from [Table 4](https://huggingface.co/bertin-project/bertin-roberta-base-spanish#results) of the Bertin-project model card.
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+ | Model | [CoNLL2002](https://huggingface.co/datasets/conll2002) - POS (acc) | [CoNLL2002](https://huggingface.co/datasets/conll2002) - NER (f1) | [PAWS-X](https://huggingface.co/datasets/paws-x) (acc) | [XNLI](https://huggingface.co/datasets/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 |