add encoder

README.md

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+---
+language: ar
+---
+
+# ar-seq2seq-gender (encoder)
+
+This is a seq2seq model (encoder half) to "flip" gender in **first-person** Arabic sentences.
+The model can augment your existing Arabic data, or generate counterfactuals
+to test a model's decisions (would changing the gender of the subject or speaker change output?).
+
+Intended Examples:
+- 'أنا سعيد' <=> 'انا سعيدة'
+- 'ركض إلى المتجر' <=> 'ركضت إلى المتجر'
+
+People's names, gender pronouns, gendered words (father, mother), and many other values are currently unchanged by this model. Future versions may be trained on more data.
+
+## Training
+
+I originally developed
+<a href="https://github.com/MonsoonNLP/el-la">a gender flip Python script</a>
+for Spanish sentences, using
+<a href="https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased">BETO</a>,
+and spaCy. More about this project: https://medium.com/ai-in-plain-english/gender-bias-in-spanish-bert-1f4d76780617
+
+The Arabic model encoder and decoder started with weights and vocabulary from
+<a href="https://github.com/UBC-NLP/marbert">MARBERT from UBC-NLP</a>,
+and was trained on the
+<a href="https://camel.abudhabi.nyu.edu/arabic-parallel-gender-corpus/">Arabic Parallel Gender Corpus</a>
+from NYU Abu Dhabi. The text is first-person sentences from OpenSubtitles, with parallel
+gender-reinflected sentences generated by Arabic speakers.
+
+Training notebook: https://colab.research.google.com/drive/1TuDfnV2gQ-WsDtHkF52jbn699bk6vJZV
+
+## Non-binary gender
+
+This model is useful to generate male and female text samples, but falls
+short of capturing gender diversity in the world and in the Arabic
+language. This subject is discussed in the bias statement of the
+<a href="https://www.aclweb.org/anthology/2020.gebnlp-1.12/">Gender Reinflection paper</a>.

config.json

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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.2.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 100000
+}

pytorch_model.bin

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+oid sha256:9c6871fa024e86bf720b7064542a727dd81a456b8f876c4aa0988b8aa2672f7e
+size 651449554

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": true, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "tokenizer_file": null}