Alina Kolesnikova commited on
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fcd48fc
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Conversational DistilRuBERT added

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.ipynb_checkpoints/README-checkpoint.md ADDED
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+ ---
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+ language:
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+ - ru
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+ ---
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+ # distilrubert-base-cased-conversational
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+ Conversational DistilRuBERT \(Russian, cased, 6‑layer, 768‑hidden, 12‑heads, 135.4M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)).
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+
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+ Our DistilRuBERT was highly inspired by \[3\], \[4\]. Namely, we used
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+ * KL loss (between teacher and student output logits)
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+ * MLM loss (between tokens labels and student output logits)
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+ * Cosine embedding loss between mean of two consecutive hidden states of the teacher and one hidden state of the student
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+
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+ \[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\)
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+ \[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.
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+ \[3\]: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. \(2019\). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
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+ \[4\]: <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>
README.md ADDED
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+ ---
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+ language:
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+ - ru
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+ ---
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+ # distilrubert-base-cased-conversational
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+ Conversational DistilRuBERT \(Russian, cased, 6‑layer, 768‑hidden, 12‑heads, 135.4M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)).
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+
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+ Our DistilRuBERT was highly inspired by \[3\], \[4\]. Namely, we used
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+ * KL loss (between teacher and student output logits)
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+ * MLM loss (between tokens labels and student output logits)
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+ * Cosine embedding loss between mean of two consecutive hidden states of the teacher and one hidden state of the student
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+
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+ \[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\)
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+ \[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.
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+ \[3\]: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. \(2019\). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
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+ \[4\]: <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>
config.json ADDED
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+ {
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+ "_name_or_path": "distilrubert-base-cased-conversational.pth",
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+ "activation": "gelu",
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+ "attention_dropout": 0.1,
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+ "dim": 768,
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+ "dropout": 0.1,
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+ "hidden_dim": 3072,
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+ "initializer_range": 0.02,
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+ "max_position_embeddings": 512,
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+ "model_type": "distilbert",
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+ "n_heads": 12,
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+ "n_layers": 6,
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+ "output_attentions": true,
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+ "output_hidden_states": true,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "qa_dropout": 0.1,
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+ "seq_classif_dropout": 0.2,
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+ "sinusoidal_pos_embds": false,
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+ "tie_weights_": true,
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+ "vocab_size": 119547
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+ }
pytorch_model.bin ADDED
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special_tokens_map.json ADDED
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+ {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
tokenizer_config.json ADDED
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+ {"do_lower_case": false}
vocab.txt ADDED
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