commit from amine

README.md

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+---
+language: multilingual
+
+datasets: wikipedia
+
+license: apache-2.0
+---
+
+# bert-base-en-pt-cased
+
+We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
+
+Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy.
+
+
+For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-pt-cased")
+model = AutoModel.from_pretrained("Geotrend/bert-base-en-pt-cased")
+
+```
+
+To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
+
+### How to cite
+
+```bibtex
+@inproceedings{smallermbert,
+  title={Load What You Need: Smaller Versions of Mutlilingual BERT},
+  author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
+  booktitle={SustaiNLP / EMNLP},
+  year={2020}
+}
+```
+
+## Contact 
+
+Please contact amine@geotrend.fr for any question, feedback or request.

config.json

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+{
+  "_name_or_path": "new-models/bert-base-en-pt-cased",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "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",
+  "type_vocab_size": 2,
+  "vocab_size": 34325
+}

pytorch_model.bin

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tf_model.h5

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tokenizer_config.json

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+{"do_lower_case": false, "model_max_length": 512}