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Update README.md
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README.md
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@@ -87,6 +87,16 @@ It contains the following tasks and their related datasets:
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Here are the train/dev/test splits of the datasets:
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| Task | NER (F1) | POS (F1) | STS (Pearson) | TC (accuracy) | QA (ViquiQuAD) (F1/EM) | QA (XQuAD) (F1/EM) |
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| ------------|:-------------:| -----:|:------|:-------|:------|:----|
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| RoBERTa-base-ca-v2 | **89.80** | **99.10** | **80.00** | **83.40** | **88.00** | **71.50** |
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| XLM-RoBERTa | 87.66 | 98.89 | 75.40 | 71.68 | 85.50/70.47 | 67.10/46.42 |
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| WikiBERT-ca | 77.66 | 97.60 | 77.18 | 73.22 | 85.45/70.75 | 65.21/36.60 |
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### Results
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## Intended uses & limitations
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The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section)
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However, the is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification or Named Entity Recognition.
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Here are the train/dev/test splits of the datasets:
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| Task (Dataset) | Total | Train | Dev | Test |
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|:--|:--|:--|:--|:--|
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| NER (Ancora) |13,581 | 10,628 | 1,427 | 1,526 |
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| POS (Ancora)| 16,678 | 13,123 | 1,709 | 1,846 |
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| STS | 3,073 | 2,073 | 500 | 500 |
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| TC (TeCla) | 137,775 | 110,203 | 13,786 | 13,786|
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| QA (ViquiQuAD) | 14,239 | 11,255 | 1,492 | 1,429 |
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### Results
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| Task | NER (F1) | POS (F1) | STS (Pearson) | TC (accuracy) | QA (ViquiQuAD) (F1/EM) | QA (XQuAD) (F1/EM) |
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| ------------|:-------------:| -----:|:------|:-------|:------|:----|
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| RoBERTa-base-ca-v2 | **89.80** | **99.10** | **80.00** | **83.40** | **88.00** | **71.50** |
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| XLM-RoBERTa | 87.66 | 98.89 | 75.40 | 71.68 | 85.50/70.47 | 67.10/46.42 |
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| WikiBERT-ca | 77.66 | 97.60 | 77.18 | 73.22 | 85.45/70.75 | 65.21/36.60 |
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## Intended uses & limitations
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The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section)
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However, the is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification or Named Entity Recognition.
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