--- language: "ca" tags: - masked-lm - catalan - exbert license: mit --- # Calbert: a Catalan Language Model ## Introduction CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture. It is now available on Hugging Face in its `tiny-uncased` version (the one you're looking at) and `base-uncased` as well, and was pretrained on the [OSCAR dataset](https://traces1.inria.fr/oscar/). For further information or requests, please go to the [GitHub repository](https://github.com/codegram/calbert) ## Pre-trained models | Model | Arch. | Training data | | ----------------------------------- | -------------- | ---------------------- | | `codegram` / `calbert-tiny-uncased` | Tiny (uncased) | OSCAR (4.3 GB of text) | | `codegram` / `calbert-base-uncased` | Base (uncased) | OSCAR (4.3 GB of text) | ## How to use Calbert with HuggingFace #### Load Calbert and its tokenizer: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("codegram/calbert-tiny-uncased") model = AutoModel.from_pretrained("codegram/calbert-tiny-uncased") model.eval() # disable dropout (or leave in train mode to finetune ``` #### Filling masks using pipeline ```python from transformers import pipeline calbert_fill_mask = pipeline("fill-mask", model="codegram/calbert-tiny-uncased", tokenizer="codegram/calbert-tiny-uncased") results = calbert_fill_mask("M'agrada [MASK] això") # results # [{'sequence': "[CLS] m'agrada molt aixo[SEP]", 'score': 0.4403671622276306, 'token': 61}, # {'sequence': "[CLS] m'agrada més aixo[SEP]", 'score': 0.050061386078596115, 'token': 43}, # {'sequence': "[CLS] m'agrada veure aixo[SEP]", 'score': 0.026286985725164413, 'token': 157}, # {'sequence': "[CLS] m'agrada bastant aixo[SEP]", 'score': 0.022483550012111664, 'token': 2143}, # {'sequence': "[CLS] m'agrada moltíssim aixo[SEP]", 'score': 0.014491282403469086, 'token': 4867}] ``` #### Extract contextual embedding features from Calbert output ```python import torch # Tokenize in sub-words with SentencePiece tokenized_sentence = tokenizer.tokenize("M'és una mica igual") # ['▁m', "'", 'es', '▁una', '▁mica', '▁igual'] # 1-hot encode and add special starting and end tokens encoded_sentence = tokenizer.encode(tokenized_sentence) # [2, 109, 7, 71, 36, 371, 1103, 3] # NB: Can be done in one step : tokenize.encode("M'és una mica igual") # Feed tokens to Calbert as a torch tensor (batch dim 1) encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0) embeddings, _ = model(encoded_sentence) embeddings.size() # torch.Size([1, 8, 312]) embeddings.detach() # tensor([[[-0.2726, -0.9855, 0.9643, ..., 0.3511, 0.3499, -0.1984], # [-0.2824, -1.1693, -0.2365, ..., -3.1866, -0.9386, -1.3718], # [-2.3645, -2.2477, -1.6985, ..., -1.4606, -2.7294, 0.2495], # ..., # [ 0.8800, -0.0244, -3.0446, ..., 0.5148, -3.0903, 1.1879], # [ 1.1300, 0.2425, 0.2162, ..., -0.5722, -2.2004, 0.4045], # [ 0.4549, -0.2378, -0.2290, ..., -2.1247, -2.2769, -0.0820]]]) ``` ## Authors CALBERT was trained and evaluated by [Txus Bach](https://twitter.com/txustice), as part of [Codegram](https://www.codegram.com)'s applied research.