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<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
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Training procedure
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
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RoBERTa base model fine-tuned on pronoun fill masking This is RoBERTa base fine-tuned for fill masking of just pronouns. The model's purpose is to post process machine translated text where sentence level translation may not have enough context to correctly deduce the correct pronoun to use. This model was trained on 10B tokens of literature (private light novel and book dataset as well as books1 and 20\% of books3 from The Pile). This model achieves an 88\% top1 accuracy, evaluated with a sliding window of 512 tokens (84\% without a sliding window).
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bart-base-few-shot-k-256-finetuned-squad-seq2seq-seed-0 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the squad dataset.
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1no_dataset_mention
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
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Training procedure
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
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Dataset This model was trained on [COALAS](https://github.com/lirondos/coalas/), a corpus of Spanish newswire annotated with unassimilated lexical borrowings. The corpus contains 370,000 tokens and includes various written media written in European Spanish. The test set was designed to be as difficult as possible: it covers sources and dates not seen in the training set, includes a high number of OOV words (92% of the borrowings in the test set are OOV) and is very borrowing-dense (20 borrowings per 1,000 tokens). |Set | Tokens | ENG | OTHER | Unique | |:-------|-----:|-----:|---------:|---------:| |Training |231,126 |1,493 | 28 |380 | |Development |82,578 |306 |49 |316| |Test |58,997 |1,239 |46 |987| |**Total** |372,701 |3,038 |123 |1,683 |
0dataset_mention
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Training procedure
1no_dataset_mention
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Training procedure
1no_dataset_mention
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1no_dataset_mention
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
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bart-paraphrase-finetuned-xsum This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset.
0dataset_mention